Monte Carlo maximum likelihood estimation for discretely observed diffusion processes
Beskos, Alexandros; Papaspiliopoulos, Omiros; Roberts, Gareth
2009-01-01
This paper introduces a Monte Carlo method for maximum likelihood inference in the context of discretely observed diffusion processes. The method gives unbiased and a.s.\\@ continuous estimators of the likelihood function for a family of diffusion models and its performance in numerical examples is computationally efficient. It uses a recently developed technique for the exact simulation of diffusions, and involves no discretization error. We show that, under regularity conditions, the Monte C...
Olivares, G.; Teferle, F. N.
2013-12-01
Geodetic time series provide information which helps to constrain theoretical models of geophysical processes. It is well established that such time series, for example from GPS, superconducting gravity or mean sea level (MSL), contain time-correlated noise which is usually assumed to be a combination of a long-term stochastic process (characterized by a power-law spectrum) and random noise. Therefore, when fitting a model to geodetic time series it is essential to also estimate the stochastic parameters beside the deterministic ones. Often the stochastic parameters include the power amplitudes of both time-correlated and random noise, as well as, the spectral index of the power-law process. To date, the most widely used method for obtaining these parameter estimates is based on maximum likelihood estimation (MLE). We present an integration method, the Bayesian Monte Carlo Markov Chain (MCMC) method, which, by using Markov chains, provides a sample of the posteriori distribution of all parameters and, thereby, using Monte Carlo integration, all parameters and their uncertainties are estimated simultaneously. This algorithm automatically optimizes the Markov chain step size and estimates the convergence state by spectral analysis of the chain. We assess the MCMC method through comparison with MLE, using the recently released GPS position time series from JPL and apply it also to the MSL time series from the Revised Local Reference data base of the PSMSL. Although the parameter estimates for both methods are fairly equivalent, they suggest that the MCMC method has some advantages over MLE, for example, without further computations it provides the spectral index uncertainty, is computationally stable and detects multimodality.
Likelihood Analysis for Mega Pixel Maps
Kogut, Alan J.
1999-01-01
The derivation of cosmological parameters from astrophysical data sets routinely involves operations counts which scale as O(N(exp 3) where N is the number of data points. Currently planned missions, including MAP and Planck, will generate sky maps with N(sub d) = 10(exp 6) or more pixels. Simple "brute force" analysis, applied to such mega-pixel data, would require years of computing even on the fastest computers. We describe an algorithm which allows estimation of the likelihood function in the direct pixel basis. The algorithm uses a conjugate gradient approach to evaluate X2 and a geometric approximation to evaluate the determinant. Monte Carlo simulations provide a correction to the determinant, yielding an unbiased estimate of the likelihood surface in an arbitrary region surrounding the likelihood peak. The algorithm requires O(N(sub d)(exp 3/2) operations and O(Nd) storage for each likelihood evaluation, and allows for significant parallel computation.
Thorn, Graeme J; King, John R
2016-01-01
The Gram-positive bacterium Clostridium acetobutylicum is an anaerobic endospore-forming species which produces acetone, butanol and ethanol via the acetone-butanol (AB) fermentation process, leading to biofuels including butanol. In previous work we looked to estimate the parameters in an ordinary differential equation model of the glucose metabolism network using data from pH-controlled continuous culture experiments. Here we combine two approaches, namely the approximate Bayesian computation via an existing sequential Monte Carlo (ABC-SMC) method (to compute credible intervals for the parameters), and the profile likelihood estimation (PLE) (to improve the calculation of confidence intervals for the same parameters), the parameters in both cases being derived from experimental data from forward shift experiments. We also apply the ABC-SMC method to investigate which of the models introduced previously (one non-sporulation and four sporulation models) have the greatest strength of evidence. We find that the joint approximate posterior distribution of the parameters determines the same parameters as previously, including all of the basal and increased enzyme production rates and enzyme reaction activity parameters, as well as the Michaelis-Menten kinetic parameters for glucose ingestion, while other parameters are not as well-determined, particularly those connected with the internal metabolites acetyl-CoA, acetoacetyl-CoA and butyryl-CoA. We also find that the approximate posterior is strongly non-Gaussian, indicating that our previous assumption of elliptical contours of the distribution is not valid, which has the effect of reducing the numbers of pairs of parameters that are (linearly) correlated with each other. Calculations of confidence intervals using the PLE method back this up. Finally, we find that all five of our models are equally likely, given the data available at present. PMID:26561777
Maximum Likelihood Analysis in the PEN Experiment
Lehman, Martin
2013-10-01
The experimental determination of the π+ -->e+ ν (γ) decay branching ratio currently provides the most accurate test of lepton universality. The PEN experiment at PSI, Switzerland, aims to improve the present world average experimental precision of 3 . 3 ×10-3 to 5 ×10-4 using a stopped beam approach. During runs in 2008-10, PEN has acquired over 2 ×107 πe 2 events. The experiment includes active beam detectors (degrader, mini TPC, target), central MWPC tracking with plastic scintillator hodoscopes, and a spherical pure CsI electromagnetic shower calorimeter. The final branching ratio will be calculated using a maximum likelihood analysis. This analysis assigns each event a probability for 5 processes (π+ -->e+ ν , π+ -->μ+ ν , decay-in-flight, pile-up, and hadronic events) using Monte Carlo verified probability distribution functions of our observables (energies, times, etc). A progress report on the PEN maximum likelihood analysis will be presented. Work supported by NSF grant PHY-0970013.
Empirical likelihood method in survival analysis
Zhou, Mai
2015-01-01
Add the Empirical Likelihood to Your Nonparametric ToolboxEmpirical Likelihood Method in Survival Analysis explains how to use the empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out examples with the associated R code. The datasets and code are available for download on his website and CRAN.The book focuses on all the standard survival analysis topics treated with empirical likelihood, including hazard functions, cumulative distribution functions, analysis of the Cox model, and computation of empiric
Likelihood Analysis of Seasonal Cointegration
DEFF Research Database (Denmark)
Johansen, Søren; Schaumburg, Ernst
1999-01-01
The error correction model for seasonal cointegration is analyzed. Conditions are found under which the process is integrated of order 1 and cointegrated at seasonal frequency, and a representation theorem is given. The likelihood function is analyzed and the numerical calculation of the maximum...... likelihood estimators is discussed. The asymptotic distribution of the likelihood ratio test for cointegrating rank is given. It is shown that the estimated cointegrating vectors are asymptotically mixed Gaussian. The results resemble the results for cointegration at zero frequency when expressed in terms...
Likelihood analysis of large-scale flows
Jaffe, A; Jaffe, Andrew; Kaiser, Nick
1994-01-01
We apply a likelihood analysis to the data of Lauer & Postman 1994. With P(k) parametrized by (\\sigma_8, \\Gamma), the likelihood function peaks at \\sigma_8\\simeq0.9, \\Gamma\\simeq0.05, indicating at face value very strong large-scale power, though at a level incompatible with COBE. There is, however, a ridge of likelihood such that more conventional power spectra do not seem strongly disfavored. The likelihood calculated using as data only the components of the bulk flow solution peaks at higher \\sigma_8, as suggested by other analyses, but is rather broad. The likelihood incorporating both bulk flow and shear gives a different picture. The components of the shear are all low, and this pulls the peak to lower amplitudes as a compromise. The velocity data alone are therefore {\\em consistent} with models with very strong large scale power which generates a large bulk flow, but the small shear (which also probes fairly large scales) requires that the power would have to be at {\\em very} large scales, which is...
cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation
Ishida, E E O; Penna-Lima, M; Cisewski, J; de Souza, R S; Trindade, A M M; Cameron, E
2015-01-01
Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogues. Here we present cosmoabc, a Python ABC sampler featuring a Population Monte Carlo (PMC) variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code is very flexible and can be easily coupled to an external simulator, while allowing to incorporate arbitrary distance and prior functions. As an example of practical application, we coupled cosmoabc with the numcosmo library and demonstrate how it can be used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function. cosmoabc is published under the GPLv3 license on PyPI and GitHub and documentation is availabl...
Likelihood Analysis of Supersymmetric SU(5) GUTs
Bagnaschi, E; Sakurai, K; Borsato, M; Buchmueller, O; Cavanaugh, R; Chobanova, V; Citron, M; De Roeck, A; Dolan, M J; Ellis, J R; Flächer, H; Heinemeyer, S; Isidori, G; Lucio, M; Santos, D Martínez; Olive, K A; Richards, A; de Vries, K J; Weiglein, G
2016-01-01
We perform a likelihood analysis of the constraints from accelerator experiments and astrophysical observations on supersymmetric (SUSY) models with SU(5) boundary conditions on soft SUSY-breaking parameters at the GUT scale. The parameter space of the models studied has 7 parameters: a universal gaugino mass $m_{1/2}$, distinct masses for the scalar partners of matter fermions in five- and ten-dimensional representations of SU(5), $m_5$ and $m_{10}$, and for the $\\mathbf{5}$ and $\\mathbf{\\bar 5}$ Higgs representations $m_{H_u}$ and $m_{H_d}$, a universal trilinear soft SUSY-breaking parameter $A_0$, and the ratio of Higgs vevs $\\tan \\beta$. In addition to previous constraints from direct sparticle searches, low-energy and flavour observables, we incorporate constraints based on preliminary results from 13 TeV LHC searches for jets + MET events and long-lived particles, as well as the latest PandaX-II and LUX searches for direct Dark Matter detection. In addition to previously-identified mechanisms for bringi...
Likelihood analysis of the I(2) model
DEFF Research Database (Denmark)
Johansen, Søren
1997-01-01
The I(2) model is defined as a submodel of the general vector autoregressive model, by two reduced rank conditions. The model describes stochastic processes with stationary second difference. A parametrization is suggested which makes likelihood inference feasible. Consistency of the maximum...
Evaluating Network Models: A Likelihood Analysis
Wang, Wen-Qiang; Zhou, Tao
2011-01-01
Many models are put forward to mimic the evolution of real networked systems. A well-accepted way to judge the validity is to compare the modeling results with real networks subject to several structural features. Even for a specific real network, we cannot fairly evaluate the goodness of different models since there are too many structural features while there is no criterion to select and assign weights on them. Motivated by the studies on link prediction algorithms, we propose a unified method to evaluate the network models via the comparison of the likelihoods of the currently observed network driven by different models, with an assumption that the higher the likelihood is, the better the model is. We test our method on the real Internet at the Autonomous System (AS) level, and the results suggest that the Generalized Linear Preferential (GLP) model outperforms the Tel Aviv Network Generator (Tang), while both two models are better than the Barab\\'asi-Albert (BA) and Erd\\"os-R\\'enyi (ER) models. Our metho...
Likelihood-Based Confidence Intervals in Exploratory Factor Analysis
Oort, Frans J.
2011-01-01
In exploratory or unrestricted factor analysis, all factor loadings are free to be estimated. In oblique solutions, the correlations between common factors are free to be estimated as well. The purpose of this article is to show how likelihood-based confidence intervals can be obtained for rotated factor loadings and factor correlations, by…
International Nuclear Information System (INIS)
In order to study properties of the Maximum Likelihood Estimator (MLE) algorithm for image reconstruction in Positron Emission Tomographyy (PET), the algorithm is applied to data obtained by the ECAT-III tomograph from a brain phantom. The procedure for subtracting accidental coincidences from the data stream generated by this physical phantom is such that he resultant data are not Poisson distributed. This makes the present investigation different from other investigations based on computer-simulated phantoms. It is shown that the MLE algorithm is robust enough to yield comparatively good images, especially when the phantom is in the periphery of the field of view, even though the underlying assumption of the algorithm is violated. Two transition matrices are utilized. The first uses geometric considerations only. The second is derived by a Monte Carlo simulation which takes into account Compton scattering in the detectors, positron range, etc. in the detectors. It is demonstrated that the images obtained from the Monte Carlo matrix are superior in some specific ways. A stopping rule derived earlier and allowing the user to stop the iterative process before the images begin to deteriorate is tested. Since the rule is based on the Poisson assumption, it does not work well with the presently available data, although it is successful wit computer-simulated Poisson data
CUSUM control charts based on likelihood ratio for preliminary analysis
Institute of Scientific and Technical Information of China (English)
Yi DAI; Zhao-jun WANG; Chang-liang ZOU
2007-01-01
To detect and estimate a shift in either the mean and the deviation or both for the preliminary analysis, the statistical process control (SPC) tool, the control chart based on the likelihood ratio test (LRT), is the most popular method.Sullivan and woodall pointed out the test statistic lrt (n1, n2) is approximately distributed as x2 (2) as the sample size n, n1 and n2 are very large, and the value of n1 = 2, 3,..., n- 2 and that of n2 = n- n1.So it is inevitable that n1 or n2 is not large. In this paper the limit distribution of lrt(n1, n2) for fixed n1 or n2 is figured out, and the exactly analytic formulae for evaluating the expectation and the variance of the limit distribution are also obtained.In addition, the properties of the standardized likelihood ratio statistic slr(n1,n) are discussed in this paper. Although slr(n1, n) contains the most important information, slr(i, n)(i ≠ n1) also contains lots of information. The cumulative sum (CUSUM) control chart can obtain more information in this condition. So we propose two CUSUM control charts based on the likelihood ratio statistics for the preliminary analysis on the individual observations. One focuses on detecting the shifts in location in the historical data and the other is more general in detecting a shift in either the location and the scale or both.Moreover, the simulated results show that the proposed two control charts are, respectively, superior to their competitors not only in the detection of the sustained shifts but also in the detection of some other out-of-control situations considered in this paper.
CUSUM control charts based on likelihood ratio for preliminary analysis
Institute of Scientific and Technical Information of China (English)
2007-01-01
To detect and estimate a shift in either the mean and the deviation or both for the preliminary analysis, the statistical process control (SPC) tool, the control chart based on the likelihood ratio test (LRT), is the most popular method. Sullivan and woodall pointed out the test statistic lrt(n1, n2) is approximately distributed as x2(2) as the sample size n,n1 and n2 are very large, and the value of n1 = 2,3,..., n - 2 and that of n2 = n - n1. So it is inevitable that n1 or n2 is not large. In this paper the limit distribution of lrt(n1, n2) for fixed n1 or n2 is figured out, and the exactly analytic formulae for evaluating the expectation and the variance of the limit distribution are also obtained. In addition, the properties of the standardized likelihood ratio statistic slr(n1, n) are discussed in this paper. Although slr(n1, n) contains the most important information, slr(i, n)(i≠n1) also contains lots of information. The cumulative sum (CUSUM) control chart can obtain more information in this condition. So we propose two CUSUM control charts based on the likelihood ratio statistics for the preliminary analysis on the individual observations. One focuses on detecting the shifts in location in the historical data and the other is more general in detecting a shift in either the location and the scale or both. Moreover, the simulated results show that the proposed two control charts are, respectively, superior to their competitors not only in the detection of the sustained shifts but also in the detection of some other out-of-control situations considered in this paper.
Maximum Likelihood Analysis of Low Energy CDMS II Germanium Data
Agnese, R; Balakishiyeva, D; Thakur, R Basu; Bauer, D A; Billard, J; Borgland, A; Bowles, M A; Brandt, D; Brink, P L; Bunker, R; Cabrera, B; Caldwell, D O; Cerdeno, D G; Chagani, H; Chen, Y; Cooley, J; Cornell, B; Crewdson, C H; Cushman, P; Daal, M; Di Stefano, P C F; Doughty, T; Esteban, L; Fallows, S; Figueroa-Feliciano, E; Fritts, M; Godfrey, G L; Golwala, S R; Graham, M; Hall, J; Harris, H R; Hertel, S A; Hofer, T; Holmgren, D; Hsu, L; Huber, M E; Jastram, A; Kamaev, O; Kara, B; Kelsey, M H; Kennedy, A; Kiveni, M; Koch, K; Leder, A; Loer, B; Asamar, E Lopez; Mahapatra, R; Mandic, V; Martinez, C; McCarthy, K A; Mirabolfathi, N; Moffatt, R A; Moore, D C; Nelson, R H; Oser, S M; Page, K; Page, W A; Partridge, R; Pepin, M; Phipps, A; Prasad, K; Pyle, M; Qiu, H; Rau, W; Redl, P; Reisetter, A; Ricci, Y; Rogers, H E; Saab, T; Sadoulet, B; Sander, J; Schneck, K; Schnee, R W; Scorza, S; Serfass, B; Shank, B; Speller, D; Upadhyayula, S; Villano, A N; Welliver, B; Wright, D H; Yellin, S; Yen, J J; Young, B A; Zhang, J
2014-01-01
We report on the results of a search for a Weakly Interacting Massive Particle (WIMP) signal in low-energy data of the Cryogenic Dark Matter Search (CDMS~II) experiment using a maximum likelihood analysis. A background model is constructed using GEANT4 to simulate the surface-event background from $^{210}$Pb decay-chain events, while using independent calibration data to model the gamma background. Fitting this background model to the data results in no statistically significant WIMP component. In addition, we perform fits using an analytic ad hoc background model proposed by Collar and Fields, who claimed to find a large excess of signal-like events in our data. We confirm the strong preference for a signal hypothesis in their analysis under these assumptions, but excesses are observed in both single- and multiple-scatter events, which implies the signal is not caused by WIMPs, but rather reflects the inadequacy of their background model.
Towards integration of compositional risk analysis using Monte Carlo simulation and security testing
Viehmann, Johannes
2014-01-01
This short paper describes ongoing efforts to combine concepts of security risk analysis with security testing into a single process. Using risk analysis artefact composition and Monte Carlo simulation to calculate likelihood values, the method described here is intended to become applicable for complex large scale systems with dynamically changing probability values.
Matilainen, Kaarina; Mäntysaari, Esa A; Lidauer, Martin H; Strandén, Ismo; Thompson, Robin
2013-01-01
Estimation of variance components by Monte Carlo (MC) expectation maximization (EM) restricted maximum likelihood (REML) is computationally efficient for large data sets and complex linear mixed effects models. However, efficiency may be lost due to the need for a large number of iterations of the EM algorithm. To decrease the computing time we explored the use of faster converging Newton-type algorithms within MC REML implementations. The implemented algorithms were: MC Newton-Raphson (NR), where the information matrix was generated via sampling; MC average information(AI), where the information was computed as an average of observed and expected information; and MC Broyden's method, where the zero of the gradient was searched using a quasi-Newton-type algorithm. Performance of these algorithms was evaluated using simulated data. The final estimates were in good agreement with corresponding analytical ones. MC NR REML and MC AI REML enhanced convergence compared to MC EM REML and gave standard errors for the estimates as a by-product. MC NR REML required a larger number of MC samples, while each MC AI REML iteration demanded extra solving of mixed model equations by the number of parameters to be estimated. MC Broyden's method required the largest number of MC samples with our small data and did not give standard errors for the parameters directly. We studied the performance of three different convergence criteria for the MC AI REML algorithm. Our results indicate the importance of defining a suitable convergence criterion and critical value in order to obtain an efficient Newton-type method utilizing a MC algorithm. Overall, use of a MC algorithm with Newton-type methods proved feasible and the results encourage testing of these methods with different kinds of large-scale problem settings.
Directory of Open Access Journals (Sweden)
Kaarina Matilainen
Full Text Available Estimation of variance components by Monte Carlo (MC expectation maximization (EM restricted maximum likelihood (REML is computationally efficient for large data sets and complex linear mixed effects models. However, efficiency may be lost due to the need for a large number of iterations of the EM algorithm. To decrease the computing time we explored the use of faster converging Newton-type algorithms within MC REML implementations. The implemented algorithms were: MC Newton-Raphson (NR, where the information matrix was generated via sampling; MC average information(AI, where the information was computed as an average of observed and expected information; and MC Broyden's method, where the zero of the gradient was searched using a quasi-Newton-type algorithm. Performance of these algorithms was evaluated using simulated data. The final estimates were in good agreement with corresponding analytical ones. MC NR REML and MC AI REML enhanced convergence compared to MC EM REML and gave standard errors for the estimates as a by-product. MC NR REML required a larger number of MC samples, while each MC AI REML iteration demanded extra solving of mixed model equations by the number of parameters to be estimated. MC Broyden's method required the largest number of MC samples with our small data and did not give standard errors for the parameters directly. We studied the performance of three different convergence criteria for the MC AI REML algorithm. Our results indicate the importance of defining a suitable convergence criterion and critical value in order to obtain an efficient Newton-type method utilizing a MC algorithm. Overall, use of a MC algorithm with Newton-type methods proved feasible and the results encourage testing of these methods with different kinds of large-scale problem settings.
An I(2) Cointegration Model with Piecewise Linear Trends: Likelihood Analysis and Application
DEFF Research Database (Denmark)
Kurita, Takamitsu; Nielsen, Heino Bohn; Rahbæk, Anders
This paper presents likelihood analysis of the I(2) cointegrated vector autoregression with piecewise linear deterministic terms. Limiting behavior of the maximum likelihood estimators are derived, which is used to further derive the limiting distribution of the likelihood ratio statistic...... asymptotic inference is discussed in detail for one of the cointegration parameters. To illustrate, an empirical analysis of US consumption, income and wealth, 1965 - 2008, is performed, emphasizing the importance of a change in nominal price trends after 1980....
''No-background'' maximum likelihood analysis in HBT interferometry
International Nuclear Information System (INIS)
We present a new 'no-background' procedure, based on the maximum likelihood method, for fitting the space-time size parameters of the particle production region in ultra-relativistic heavy ion collisions. This procedure uses an approximation to avoid the necessity of constructing a mixed-event background before fitting the data. (orig.)
San Carlos Apache Tribe - Energy Organizational Analysis
Energy Technology Data Exchange (ETDEWEB)
Rapp, James; Albert, Steve
2012-04-01
The San Carlos Apache Tribe (SCAT) was awarded $164,000 in late-2011 by the U.S. Department of Energy (U.S. DOE) Tribal Energy Program's "First Steps Toward Developing Renewable Energy and Energy Efficiency on Tribal Lands" Grant Program. This grant funded: The analysis and selection of preferred form(s) of tribal energy organization (this Energy Organization Analysis, hereinafter referred to as "EOA"). Start-up staffing and other costs associated with the Phase 1 SCAT energy organization. An intern program. Staff training. Tribal outreach and workshops regarding the new organization and SCAT energy programs and projects, including two annual tribal energy summits (2011 and 2012). This report documents the analysis and selection of preferred form(s) of a tribal energy organization.
Analysis of Minute Features in Speckled Imagery with Maximum Likelihood Estimation
Directory of Open Access Journals (Sweden)
Alejandro C. Frery
2004-12-01
Full Text Available This paper deals with numerical problems arising when performing maximum likelihood parameter estimation in speckled imagery using small samples. The noise that appears in images obtained with coherent illumination, as is the case of sonar, laser, ultrasound-B, and synthetic aperture radar, is called speckle, and it can neither be assumed Gaussian nor additive. The properties of speckle noise are well described by the multiplicative model, a statistical framework from which stem several important distributions. Amongst these distributions, one is regarded as the universal model for speckled data, namely, the Ã°ÂÂ’Â¢0 law. This paper deals with amplitude data, so the Ã°ÂÂ’Â¢A0 distribution will be used. The literature reports that techniques for obtaining estimates (maximum likelihood, based on moments and on order statistics of the parameters of the Ã°ÂÂ’Â¢A0 distribution require samples of hundreds, even thousands, of observations in order to obtain sensible values. This is verified for maximum likelihood estimation, and a proposal based on alternate optimization is made to alleviate this situation. The proposal is assessed with real and simulated data, showing that the convergence problems are no longer present. A Monte Carlo experiment is devised to estimate the quality of maximum likelihood estimators in small samples, and real data is successfully analyzed with the proposed alternated procedure. Stylized empirical influence functions are computed and used to choose a strategy for computing maximum likelihood estimates that is resistant to outliers.
Generalized linear models with random effects unified analysis via H-likelihood
Lee, Youngjo; Pawitan, Yudi
2006-01-01
Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors.Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives. Complementing theory with examples, many of...
Challenges and prospects for whole-coremonte Carlo analysis
International Nuclear Information System (INIS)
The advantages for using Monte Carlo methods to analyze full-core reactor configurations include essentially exact representation of geometry and physical phenomena that are important for reactor analysis. But this substantial advantage comes at a substantial cost because of the computational burden, both in terms of memory demand and computational time. This paper focuses on the challenges facing full-core Monte Carlo for keff calculations and the prospects for Monte Carlo becoming a routine tool for reactor analysis.
Royle, J. Andrew; Sutherland, Christopher S.; Fuller, Angela K.; Sun, Catherine C.
2015-01-01
We develop a likelihood analysis framework for fitting spatial capture-recapture (SCR) models to data collected on class structured or stratified populations. Our interest is motivated by the necessity of accommodating the problem of missing observations of individual class membership. This is particularly problematic in SCR data arising from DNA analysis of scat, hair or other material, which frequently yields individual identity but fails to identify the sex. Moreover, this can represent a large fraction of the data and, given the typically small sample sizes of many capture-recapture studies based on DNA information, utilization of the data with missing sex information is necessary. We develop the class structured likelihood for the case of missing covariate values, and then we address the scaling of the likelihood so that models with and without class structured parameters can be formally compared regardless of missing values. We apply our class structured model to black bear data collected in New York in which sex could be determined for only 62 of 169 uniquely identified individuals. The models containing sex-specificity of both the intercept of the SCR encounter probability model and the distance coefficient, and including a behavioral response are strongly favored by log-likelihood. Estimated population sex ratio is strongly influenced by sex structure in model parameters illustrating the importance of rigorous modeling of sex differences in capture-recapture models.
Can, Seda; van de Schoot, Rens; Hox, Joop
2015-01-01
Because variables may be correlated in the social and behavioral sciences, multicollinearity might be problematic. This study investigates the effect of collinearity manipulated in within and between levels of a two-level confirmatory factor analysis by Monte Carlo simulation. Furthermore, the influence of the size of the intraclass correlation…
Can, Seda; van de Schoot, Rens; Hox, Joop
2014-01-01
Because variables may be correlated in the social and behavioral sciences, multicollinearity might be problematic. This study investigates the effect of collinearity manipulated in within and between levels of a two-level confirmatory factor analysis by Monte Carlo simulation. Furthermore, the influ
Bayesian phylogeny analysis via stochastic approximation Monte Carlo
Cheon, Sooyoung
2009-11-01
Monte Carlo methods have received much attention in the recent literature of phylogeny analysis. However, the conventional Markov chain Monte Carlo algorithms, such as the Metropolis-Hastings algorithm, tend to get trapped in a local mode in simulating from the posterior distribution of phylogenetic trees, rendering the inference ineffective. In this paper, we apply an advanced Monte Carlo algorithm, the stochastic approximation Monte Carlo algorithm, to Bayesian phylogeny analysis. Our method is compared with two popular Bayesian phylogeny software, BAMBE and MrBayes, on simulated and real datasets. The numerical results indicate that our method outperforms BAMBE and MrBayes. Among the three methods, SAMC produces the consensus trees which have the highest similarity to the true trees, and the model parameter estimates which have the smallest mean square errors, but costs the least CPU time. © 2009 Elsevier Inc. All rights reserved.
Bayesian model comparison with intractable likelihoods
Everitt, Richard G; Rowing, Ellen; Evdemon-Hogan, Melina
2015-01-01
Markov random field models are used widely in computer science, statistical physics and spatial statistics and network analysis. However, Bayesian analysis of these models using standard Monte Carlo methods is not possible due to their intractable likelihood functions. Several methods have been developed that permit exact, or close to exact, simulation from the posterior distribution. However, estimating the evidence and Bayes' factors (BFs) for these models remains challenging in general. This paper describes new random weight importance sampling and sequential Monte Carlo methods for estimating BFs that use simulation to circumvent the evaluation of the intractable likelihood, and compares them to existing methods. In some cases we observe an advantage in the use of biased weight estimates; an initial investigation into the theoretical and empirical properties of this class of methods is presented.
Maximum likelihood-based analysis of photon arrival trajectories in single-molecule FRET
International Nuclear Information System (INIS)
Highlights: ► We study model selection and parameter recovery from single-molecule FRET experiments. ► We examine the maximum likelihood-based analysis of two-color photon trajectories. ► The number of observed photons determines the performance of the method. ► For long trajectories, one can extract mean dwell times that are comparable to inter-photon times. -- Abstract: When two fluorophores (donor and acceptor) are attached to an immobilized biomolecule, anti-correlated fluctuations of the donor and acceptor fluorescence caused by Förster resonance energy transfer (FRET) report on the conformational kinetics of the molecule. Here we assess the maximum likelihood-based analysis of donor and acceptor photon arrival trajectories as a method for extracting the conformational kinetics. Using computer generated data we quantify the accuracy and precision of parameter estimates and the efficiency of the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) in selecting the true kinetic model. We find that the number of observed photons is the key parameter determining parameter estimation and model selection. For long trajectories, one can extract mean dwell times that are comparable to inter-photon times.
The R Package metaLik for Likelihood Inference in Meta-Analysis
Directory of Open Access Journals (Sweden)
Annamaria Guolo
2012-08-01
Full Text Available Meta-analysis is a statistical method for combining information from different studies about the same issue of interest. Meta-analysis is widely diffuse in medical investigation and more recently it received a growing interest also in social disciplines. Typical applications involve a small number of studies, thus making ordinary inferential methods based on first-order asymptotics unreliable. More accurate results can be obtained by exploiting the theory of higher-order asymptotics. This paper describes the metaLik package which provides an R implementation of higher-order likelihood methods in meta-analysis. The extension to meta-regression is included. Two real data examples are used to illustrate the capabilities of the package.
Likelihood ratio meta-analysis: New motivation and approach for an old method.
Dormuth, Colin R; Filion, Kristian B; Platt, Robert W
2016-03-01
A 95% confidence interval (CI) in an updated meta-analysis may not have the expected 95% coverage. If a meta-analysis is simply updated with additional data, then the resulting 95% CI will be wrong because it will not have accounted for the fact that the earlier meta-analysis failed or succeeded to exclude the null. This situation can be avoided by using the likelihood ratio (LR) as a measure of evidence that does not depend on type-1 error. We show how an LR-based approach, first advanced by Goodman, can be used in a meta-analysis to pool data from separate studies to quantitatively assess where the total evidence points. The method works by estimating the log-likelihood ratio (LogLR) function from each study. Those functions are then summed to obtain a combined function, which is then used to retrieve the total effect estimate, and a corresponding 'intrinsic' confidence interval. Using as illustrations the CAPRIE trial of clopidogrel versus aspirin in the prevention of ischemic events, and our own meta-analysis of higher potency statins and the risk of acute kidney injury, we show that the LR-based method yields the same point estimate as the traditional analysis, but with an intrinsic confidence interval that is appropriately wider than the traditional 95% CI. The LR-based method can be used to conduct both fixed effect and random effects meta-analyses, it can be applied to old and new meta-analyses alike, and results can be presented in a format that is familiar to a meta-analytic audience.
CERN. Geneva
2015-01-01
Most physics results at the LHC end in a likelihood ratio test. This includes discovery and exclusion for searches as well as mass, cross-section, and coupling measurements. The use of Machine Learning (multivariate) algorithms in HEP is mainly restricted to searches, which can be reduced to classification between two fixed distributions: signal vs. background. I will show how we can extend the use of ML classifiers to distributions parameterized by physical quantities like masses and couplings as well as nuisance parameters associated to systematic uncertainties. This allows for one to approximate the likelihood ratio while still using a high dimensional feature vector for the data. Both the MEM and ABC approaches mentioned above aim to provide inference on model parameters (like cross-sections, masses, couplings, etc.). ABC is fundamentally tied Bayesian inference and focuses on the “likelihood free” setting where only a simulator is available and one cannot directly compute the likelihood for the dat...
Owen, Art B
2001-01-01
Empirical likelihood provides inferences whose validity does not depend on specifying a parametric model for the data. Because it uses a likelihood, the method has certain inherent advantages over resampling methods: it uses the data to determine the shape of the confidence regions, and it makes it easy to combined data from multiple sources. It also facilitates incorporating side information, and it simplifies accounting for censored, truncated, or biased sampling.One of the first books published on the subject, Empirical Likelihood offers an in-depth treatment of this method for constructing confidence regions and testing hypotheses. The author applies empirical likelihood to a range of problems, from those as simple as setting a confidence region for a univariate mean under IID sampling, to problems defined through smooth functions of means, regression models, generalized linear models, estimating equations, or kernel smooths, and to sampling with non-identically distributed data. Abundant figures offer vi...
Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model
International Nuclear Information System (INIS)
We have developed a model for FROC curve fitting that relates the observer's FROC performance not to the ROC performance that would be obtained if the observer's responses were scored on a per image basis, but rather to a hypothesized ROC performance that the observer would obtain in the task of classifying a set of 'candidate detections' as positive or negative. We adopt the assumptions of the Bunch FROC model, namely that the observer's detections are all mutually independent, as well as assumptions qualitatively similar to, but different in nature from, those made by Chakraborty in his AFROC scoring methodology. Under the assumptions of our model, we show that the observer's FROC performance is a linearly scaled version of the candidate analysis ROC curve, where the scaling factors are just given by the FROC operating point coordinates for detecting initial candidates. Further, we show that the likelihood function of the model parameters given observational data takes on a simple form, and we develop a maximum likelihood method for fitting a FROC curve to this data. FROC and AFROC curves are produced for computer vision observer datasets and compared with the results of the AFROC scoring method. Although developed primarily with computer vision schemes in mind, we hope that the methodology presented here will prove worthy of further study in other applications as well
A maximum likelihood analysis of the CoGeNT public dataset
Kelso, Chris
2016-06-01
The CoGeNT detector, located in the Soudan Underground Laboratory in Northern Minnesota, consists of a 475 grams (fiducial mass of 330 grams) target mass of p-type point contact germanium detector that measures the ionization charge created by nuclear recoils. This detector has searched for recoils created by dark matter since December of 2009. We analyze the public dataset from the CoGeNT experiment to search for evidence of dark matter interactions with the detector. We perform an unbinned maximum likelihood fit to the data and compare the significance of different WIMP hypotheses relative to each other and the null hypothesis of no WIMP interactions. This work presents the current status of the analysis.
W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis.
Trifinopoulos, Jana; Nguyen, Lam-Tung; von Haeseler, Arndt; Minh, Bui Quang
2016-07-01
This article presents W-IQ-TREE, an intuitive and user-friendly web interface and server for IQ-TREE, an efficient phylogenetic software for maximum likelihood analysis. W-IQ-TREE supports multiple sequence types (DNA, protein, codon, binary and morphology) in common alignment formats and a wide range of evolutionary models including mixture and partition models. W-IQ-TREE performs fast model selection, partition scheme finding, efficient tree reconstruction, ultrafast bootstrapping, branch tests, and tree topology tests. All computations are conducted on a dedicated computer cluster and the users receive the results via URL or email. W-IQ-TREE is available at http://iqtree.cibiv.univie.ac.at It is free and open to all users and there is no login requirement. PMID:27084950
Likelihood based observability analysis and confidence intervals for predictions of dynamic models
Kreutz, Clemens; Timmer, Jens
2011-01-01
Mechanistic dynamic models of biochemical networks such as Ordinary Differential Equations (ODEs) contain unknown parameters like the reaction rate constants and the initial concentrations of the compounds. The large number of parameters as well as their nonlinear impact on the model responses hamper the determination of confidence regions for parameter estimates. At the same time, classical approaches translating the uncertainty of the parameters into confidence intervals for model predictions are hardly feasible. In this article it is shown that a so-called prediction profile likelihood yields reliable confidence intervals for model predictions, despite arbitrarily complex and high-dimensional shapes of the confidence regions for the estimated parameters. Prediction confidence intervals of the dynamic states allow a data-based observability analysis. The approach renders the issue of sampling a high-dimensional parameter space into evaluating one-dimensional prediction spaces. The method is also applicable ...
Rapidly evaluating the compact binary likelihood function via interpolation
Smith, Rory; Mandel, Ilya; Vecchio, Alberto
2013-01-01
Bayesian parameter estimation on gravitational waves from compact binary coalescences (CBCs) typically requires millions of template waveform computations at different values of the parameters describing the binary. Sampling techniques such as Markov chain Monte Carlo and nested sampling evaluate likelihoods, and hence compute template waveforms, serially; thus, the total computational time of the analysis scales linearly with that of template generation. Here we address the issue of rapidly computing the likelihood function of CBC sources with non-spinning components. We show how to efficiently compute the continuous likelihood function on the three-dimensional subspace of parameters on which it has a non-trivial dependence (the chirp mass, symmetric mass ratio and coalescence time) via interpolation. Subsequently, sampling this interpolated likelihood function is a significantly cheaper computational process than directly evaluating the likelihood; we report improvements in computational time of two to thre...
Monte carlo depletion analysis of SMART core by MCNAP code
Energy Technology Data Exchange (ETDEWEB)
Jung, Jong Sung; Sim, Hyung Jin; Kim, Chang Hyo [Seoul National Univ., Seoul (Korea, Republic of); Lee, Jung Chan; Ji, Sung Kyun [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)
2001-05-01
Depletion an analysis of SMART, a small-sized advanced integral PWR under development by KAERI, is conducted using the Monte Carlo (MC) depletion analysis program, MCNAP. The results are compared with those of the CASMO-3/ MASTER nuclear analysis. The difference between MASTER and MCNAP on k{sub eff} prediction is observed about 600pcm at BOC, and becomes smaller as the core burnup increases. The maximum difference bet ween two predict ions on fuel assembly (FA) normalized power distribution is about 6.6% radially , and 14.5% axially but the differences are observed to lie within standard deviation of MC estimations.
Gray matter atrophy in narcolepsy: An activation likelihood estimation meta-analysis.
Weng, Hsu-Huei; Chen, Chih-Feng; Tsai, Yuan-Hsiung; Wu, Chih-Ying; Lee, Meng; Lin, Yu-Ching; Yang, Cheng-Ta; Tsai, Ying-Huang; Yang, Chun-Yuh
2015-12-01
The authors reviewed the literature on the use of voxel-based morphometry (VBM) in narcolepsy magnetic resonance imaging (MRI) studies via the use of a meta-analysis of neuroimaging to identify concordant and specific structural deficits in patients with narcolepsy as compared with healthy subjects. We used PubMed to retrieve articles published between January 2000 and March 2014. The authors included all VBM research on narcolepsy and compared the findings of the studies by using gray matter volume (GMV) or gray matter concentration (GMC) to index differences in gray matter. Stereotactic data were extracted from 8 VBM studies of 149 narcoleptic patients and 162 control subjects. We applied activation likelihood estimation (ALE) technique and found significant regional gray matter reduction in the bilateral hypothalamus, thalamus, globus pallidus, extending to nucleus accumbens (NAcc) and anterior cingulate cortex (ACC), left mid orbital and rectal gyri (BAs 10 and 11), right inferior frontal gyrus (BA 47), and the right superior temporal gyrus (BA 41) in patients with narcolepsy. The significant gray matter deficits in narcoleptic patients occurred in the bilateral hypothalamus and frontotemporal regions, which may be related to the emotional processing abnormalities and orexin/hypocretin pathway common among populations of patients with narcolepsy.
Maximum likelihood-based analysis of single-molecule photon arrival trajectories
Hajdziona, Marta; Molski, Andrzej
2011-02-01
In this work we explore the statistical properties of the maximum likelihood-based analysis of one-color photon arrival trajectories. This approach does not involve binning and, therefore, all of the information contained in an observed photon strajectory is used. We study the accuracy and precision of parameter estimates and the efficiency of the Akaike information criterion and the Bayesian information criterion (BIC) in selecting the true kinetic model. We focus on the low excitation regime where photon trajectories can be modeled as realizations of Markov modulated Poisson processes. The number of observed photons is the key parameter in determining model selection and parameter estimation. For example, the BIC can select the true three-state model from competing two-, three-, and four-state kinetic models even for relatively short trajectories made up of 2 × 103 photons. When the intensity levels are well-separated and 104 photons are observed, the two-state model parameters can be estimated with about 10% precision and those for a three-state model with about 20% precision.
Assessing evidentiary value in fire debris analysis by chemometric and likelihood ratio approaches.
Sigman, Michael E; Williams, Mary R
2016-07-01
Results are presented from support vector machine (SVM), linear and quadratic discriminant analysis (LDA and QDA) and k-nearest neighbors (kNN) methods of binary classification of fire debris samples as positive or negative for ignitable liquid residue. Training samples were prepared by computationally mixing data from ignitable liquid and substrate pyrolysis databases. Validation was performed on an unseen set of computationally mixed (in silico) data and on fire debris from large-scale research burns. The probabilities of class membership were calculated using an uninformative (equal) prior and a likelihood ratio was calculated from the resulting class membership probabilities. The SVM method demonstrated a high discrimination, low error rate and good calibration for the in silico validation data; however, the performance decreased significantly for the fire debris validation data, as indicated by a significant increase in the error rate and decrease in the calibration. The QDA and kNN methods showed similar performance trends. The LDA method gave poorer discrimination, higher error rates and slightly poorer calibration for the in silico validation data; however the performance did not deteriorate for the fire debris validation data. PMID:27081767
Likelihood Analysis of Cosmic Shear on Simulated and VIRMOS-DESCART Data
Van Waerbeke, L; Pellò, R; Pen, U L; McCracken, H J; Jain, B
2002-01-01
We present a maximum likelihood analysis of cosmological parameters from measurements of the aperture mass up to 35 arcmin, using simulated and real cosmic shear data. A four-dimensional parameter space is explored which examines the mean density \\Omega_M, the mass power spectrum normalization \\sigma_8, the shape parameter \\Gamma and the redshift of the sources z_s. Constraints on \\Omega_M and \\sigma_8 (resp. \\Gamma and z_s) are then given by marginalizing over \\Gamma and z_s (resp. \\Omega_M and \\sigma_8). For a flat LCDM cosmologies, using a photometric redshift prior for the sources and \\Gamma \\in [0.1,0.4], we find \\sigma_8=(0.57\\pm0.04) \\Omega_M^{(0.24\\mp 0.18) \\Omega_M-0.49} at the 68% confidence level (the error budget includes statistical noise, full cosmic variance and residual systematic). The estimate of \\Gamma, marginalized over \\Omega_M \\in [0.1,0.4], \\sigma_8 \\in [0.7,1.3] and z_s constrained by photometric redshifts, gives \\Gamma=0.25\\pm 0.13 at 68% confidence. Adopting h=0.7, a flat universe, \\...
Zhou, Rurui; Li, Yu; Lu, Di; Liu, Haixing; Zhou, Huicheng
2016-09-01
This paper investigates the use of an epsilon-dominance non-dominated sorted genetic algorithm II (ɛ-NSGAII) as a sampling approach with an aim to improving sampling efficiency for multiple metrics uncertainty analysis using Generalized Likelihood Uncertainty Estimation (GLUE). The effectiveness of ɛ-NSGAII based sampling is demonstrated compared with Latin hypercube sampling (LHS) through analyzing sampling efficiency, multiple metrics performance, parameter uncertainty and flood forecasting uncertainty with a case study of flood forecasting uncertainty evaluation based on Xinanjiang model (XAJ) for Qing River reservoir, China. Results obtained demonstrate the following advantages of the ɛ-NSGAII based sampling approach in comparison to LHS: (1) The former performs more effective and efficient than LHS, for example the simulation time required to generate 1000 behavioral parameter sets is shorter by 9 times; (2) The Pareto tradeoffs between metrics are demonstrated clearly with the solutions from ɛ-NSGAII based sampling, also their Pareto optimal values are better than those of LHS, which means better forecasting accuracy of ɛ-NSGAII parameter sets; (3) The parameter posterior distributions from ɛ-NSGAII based sampling are concentrated in the appropriate ranges rather than uniform, which accords with their physical significance, also parameter uncertainties are reduced significantly; (4) The forecasted floods are close to the observations as evaluated by three measures: the normalized total flow outside the uncertainty intervals (FOUI), average relative band-width (RB) and average deviation amplitude (D). The flood forecasting uncertainty is also reduced a lot with ɛ-NSGAII based sampling. This study provides a new sampling approach to improve multiple metrics uncertainty analysis under the framework of GLUE, and could be used to reveal the underlying mechanisms of parameter sets under multiple conflicting metrics in the uncertainty analysis process.
,
2015-01-01
We present a sophisticated likelihood reconstruction algorithm for shower-image analysis of imaging Cherenkov telescopes. The reconstruction algorithm is based on the comparison of the camera pixel amplitudes with the predictions from a Monte Carlo based model. Shower parameters are determined by a maximisation of a likelihood function. Maximisation of the likelihood as a function of shower fit parameters is performed using a numerical non-linear optimisation technique. A related reconstruction technique has already been developed by the CAT and the H.E.S.S. experiments, and provides a more precise direction and energy reconstruction of the photon induced shower compared to the second moment of the camera image analysis. Examples are shown of the performance of the analysis on simulated gamma-ray data from the VERITAS array.
Asymptotic analysis of spatial discretizations in implicit Monte Carlo
Energy Technology Data Exchange (ETDEWEB)
Densmore, Jeffery D [Los Alamos National Laboratory
2009-01-01
We perform an asymptotic analysis of spatial discretizations in Implicit Monte Carlo (IMC). We consider two asymptotic scalings: one that represents a time step that resolves the mean-free time, and one that corresponds to a fixed, optically large time step. We show that only the latter scaling results in a valid spatial discretization of the proper diffusion equation, and thus we conclude that IMC only yields accurate solutions when using optically large spatial cells if time steps are also optically large. We demonstrate the validity of our analysis with a set of numerical examples.
Asymptotic analysis of spatial discretizations in implicit Monte Carlo
Energy Technology Data Exchange (ETDEWEB)
Densmore, Jeffery D [Los Alamos National Laboratory
2008-01-01
We perform an asymptotic analysis of spatial discretizations in Implicit Monte Carlo (IMC). We consider two asymptotic scalings: one that represents a time step that resolves the mean-free time, and one that corresponds to a fixed, optically large time step. We show that only the latter scaling results in a valid spatial discretization of the proper diffusion equation, and thus we conclude that IMC only yields accurate solutions when using optically large spatial cells if time steps are also optically large, We demonstrate the validity of our analysis with a set of numerical examples.
An EM Algorithm for Maximum Likelihood Estimation of Process Factor Analysis Models
Lee, Taehun
2010-01-01
In this dissertation, an Expectation-Maximization (EM) algorithm is developed and implemented to obtain maximum likelihood estimates of the parameters and the associated standard error estimates characterizing temporal flows for the latent variable time series following stationary vector ARMA processes, as well as the parameters defining the…
The impact of Monte Carlo simulation: a scientometric analysis of scholarly literature
Pia, Maria Grazia; Bell, Zane W; Dressendorfer, Paul V
2010-01-01
A scientometric analysis of Monte Carlo simulation and Monte Carlo codes has been performed over a set of representative scholarly journals related to radiation physics. The results of this study are reported and discussed. They document and quantitatively appraise the role of Monte Carlo methods and codes in scientific research and engineering applications.
Iterative Monte Carlo analysis of spin-dependent parton distributions
Sato, Nobuo; Kuhn, S E; Ethier, J J; Accardi, A
2016-01-01
We present a comprehensive new global QCD analysis of polarized inclusive deep-inelastic scattering, including the latest high-precision data on longitudinal and transverse polarization asymmetries from Jefferson Lab and elsewhere. The analysis is performed using a new iterative Monte Carlo fitting technique which generates stable fits to polarized parton distribution functions (PDFs) with statistically rigorous uncertainties. Inclusion of the Jefferson Lab data leads to a reduction in the PDF errors for the valence and sea quarks, as well as in the gluon polarization uncertainty at $x \\gtrsim 0.1$. The study also provides the first determination of the flavor-separated twist-3 PDFs and the $d_2$ moment of the nucleon within a global PDF analysis.
Application of Monte Carlo method to nuclear core characteristic analysis
Energy Technology Data Exchange (ETDEWEB)
Kim, J. K.; Han, C. Y.; Shin, C. H. [Hangyang Univ., Seoul (Korea, Republic of)
2000-05-01
The nuclear core characteristic analysis for Korean Next Generation Reactor(KNGR) was performed by using Monte Carlo method. MCNP4B code was employed to model the initial core of KNGR on a three dimensional representation. Material compositions for each type and burnup of fuel assemblies were obtained by using CASMO-3 runs. A new cross section library for different in-vessel core temperatures was generated by NJOY 97 code. The criticality benchmark of the modeled KNGR core was carried out though KCODE calculation and the relative powers of each fuel rod were obtained. The nuclear characteristics including the effective multiplication factor, relative power distributions, pin peaking factor, and axial offset(AO) were obtained from the results in KCODE calculation. The comparison between the results from MCNP calculation and the reference data from KEPCO Nuclear Fuel Company(KNFC) validates the MCNP modeling for KNGR core and the leads to the applications of Monte Carlo method to the nuclear core characteristic analysis.
Recursive Pathways to Marginal Likelihood Estimation with Prior-Sensitivity Analysis
Cameron, Ewan
2013-01-01
We investigate the utility to contemporary Bayesian studies of recursive, Gauss-Seidel-type pathways to marginal likelihood estimation characterized by reverse logistic regression and the density of states. Through a pair of illustrative, numerical examples (including mixture modeling of the well-known 'galaxy dataset') we highlight both the remarkable diversity of bridging schemes amenable to recursive normalization and the notable efficiency of the resulting pseudo-mixture densities for gauging prior-sensitivity in the model selection context. Our key theoretical contributions show the connection between the nested sampling identity and the density of states. Further, we introduce a novel heuristic ('thermodynamic integration via importance sampling') for qualifying the role of the bridging sequence in marginal likelihood estimation. An efficient pseudo-mixture density scheme for harnessing the information content of otherwise discarded draws in ellipse-based nested sampling is also introduced.
Risk analysis of gravity dam instability using credibility theory Monte Carlo simulation model.
Xin, Cao; Chongshi, Gu
2016-01-01
Risk analysis of gravity dam stability involves complicated uncertainty in many design parameters and measured data. Stability failure risk ratio described jointly by probability and possibility has deficiency in characterization of influence of fuzzy factors and representation of the likelihood of risk occurrence in practical engineering. In this article, credibility theory is applied into stability failure risk analysis of gravity dam. Stability of gravity dam is viewed as a hybrid event considering both fuzziness and randomness of failure criterion, design parameters and measured data. Credibility distribution function is conducted as a novel way to represent uncertainty of influence factors of gravity dam stability. And combining with Monte Carlo simulation, corresponding calculation method and procedure are proposed. Based on a dam section, a detailed application of the modeling approach on risk calculation of both dam foundation and double sliding surfaces is provided. The results show that, the present method is feasible to be applied on analysis of stability failure risk for gravity dams. The risk assessment obtained can reflect influence of both sorts of uncertainty, and is suitable as an index value. PMID:27386264
Monte Carlo analysis of Musashi TRIGA mark II reactor core
Energy Technology Data Exchange (ETDEWEB)
Matsumoto, Tetsuo [Atomic Energy Research Laboratory, Musashi Institute of Technology, Kawasaki, Kanagawa (Japan)
1999-08-01
The analysis of the TRIGA-II core at the Musashi Institute of Technology Research Reactor (Musashi reactor, 100 kW) was performed by the three-dimensional continuous-energy Monte Carlo code (MCNP4A). Effective multiplication factors (k{sub eff}) for the several fuel-loading patterns including the initial core criticality experiment, the fuel element and control rod reactivity worth as well as the neutron flux measurements were used in the validation process of the physical model and neutron cross section data from the ENDF/B-V evaluation. The calculated k{sub eff} overestimated the experimental data by about 1.0%{delta}k/k for both the initial core and the several fuel-loading arrangements. The calculated reactivity worths of control rod and fuel element agree well the measured ones within the uncertainties. The comparison of neutron flux distribution was consistent with the experimental ones which were measured by activation methods at the sample irradiation tubes. All in all, the agreement between the MCNP predictions and the experimentally determined values is good, which indicated that the Monte Carlo model is enough to simulate the Musashi TRIGA-II reactor core. (author)
Stratified source-sampling techniques for Monte Carlo eigenvalue analysis.
Energy Technology Data Exchange (ETDEWEB)
Mohamed, A.
1998-07-10
In 1995, at a conference on criticality safety, a special session was devoted to the Monte Carlo ''Eigenvalue of the World'' problem. Argonne presented a paper, at that session, in which the anomalies originally observed in that problem were reproduced in a much simplified model-problem configuration, and removed by a version of stratified source-sampling. In this paper, stratified source-sampling techniques are generalized and applied to three different Eigenvalue of the World configurations which take into account real-world statistical noise sources not included in the model problem, but which differ in the amount of neutronic coupling among the constituents of each configuration. It is concluded that, in Monte Carlo eigenvalue analysis of loosely-coupled arrays, the use of stratified source-sampling reduces the probability of encountering an anomalous result over that if conventional source-sampling methods are used. However, this gain in reliability is substantially less than that observed in the model-problem results.
Review of neutron noise analysis theory by Monte Carlo simulation
International Nuclear Information System (INIS)
Some debates on the theory of neutron noise analysis for reactor kinetic parameter measurement were found before 1970 but a report firmly clearing these debates has not been found, and a question was raised when neutron noise experiments for the TRIGA and HANARO reactors in Korea were performed. In order to clarify this question, the neutron noise experiment is simulated by the Monte Carlo method. This simulation confirms that the widely used equation is approximately valid and that the confusion was caused from the explanation on the derivation of the equation. Rossi-α technique is one of the representative methods of noise analyses for the reactor kinetic parameter measurement, but different opinions were raised for the chain reaction related term in the equation. The equation originally derived at the Los Alamos National Laboratory (LANL) has been widely accepted. However, the others were supported by strict mathematics and experiments as well, and the reason of discrepancy has not been clarified. Since it is the problem of basic concept before the effect of neutron energy or geometry is included, the Monte Carlo simulation for the simplest reactor model could clarify it. For this purpose, the experiment measuring the neutron noise is simulated, and it results that the original equation is approximately valid. However, it is judged that the explanation on the equation by the authors derived it for the first time is not so correct, but Orndoff who made the first experiment by the Ross-α technique explained it rather correctly
Sensitivity analysis for oblique incidence reflectometry using Monte Carlo simulations
DEFF Research Database (Denmark)
Kamran, Faisal; Andersen, Peter E.
2015-01-01
Oblique incidence reflectometry has developed into an effective, noncontact, and noninvasive measurement technology for the quantification of both the reduced scattering and absorption coefficients of a sample. The optical properties are deduced by analyzing only the shape of the reflectance...... profiles. This article presents a sensitivity analysis of the technique in turbid media. Monte Carlo simulations are used to investigate the technique and its potential to distinguish the small changes between different levels of scattering. We present various regions of the dynamic range of optical...... properties in which system demands vary to be able to detect subtle changes in the structure of the medium, translated as measured optical properties. Effects of variation in anisotropy are discussed and results presented. Finally, experimental data of milk products with different fat content are considered...
Implementation and analysis of an adaptive multilevel Monte Carlo algorithm
Hoel, Hakon
2014-01-01
We present an adaptive multilevel Monte Carlo (MLMC) method for weak approximations of solutions to Itô stochastic dierential equations (SDE). The work [11] proposed and analyzed an MLMC method based on a hierarchy of uniform time discretizations and control variates to reduce the computational effort required by a single level Euler-Maruyama Monte Carlo method from O(TOL-3) to O(TOL-2 log(TOL-1)2) for a mean square error of O(TOL2). Later, the work [17] presented an MLMC method using a hierarchy of adaptively re ned, non-uniform time discretizations, and, as such, it may be considered a generalization of the uniform time discretizationMLMC method. This work improves the adaptiveMLMC algorithms presented in [17] and it also provides mathematical analysis of the improved algorithms. In particular, we show that under some assumptions our adaptive MLMC algorithms are asymptotically accurate and essentially have the correct complexity but with improved control of the complexity constant factor in the asymptotic analysis. Numerical tests include one case with singular drift and one with stopped diusion, where the complexity of a uniform single level method is O(TOL-4). For both these cases the results con rm the theory, exhibiting savings in the computational cost for achieving the accuracy O(TOL) from O(TOL-3) for the adaptive single level algorithm to essentially O(TOL-2 log(TOL-1)2) for the adaptive MLMC algorithm. © 2014 by Walter de Gruyter Berlin/Boston 2014.
Directory of Open Access Journals (Sweden)
Wang Huai-Chun
2009-09-01
Full Text Available Abstract Background The covarion hypothesis of molecular evolution holds that selective pressures on a given amino acid or nucleotide site are dependent on the identity of other sites in the molecule that change throughout time, resulting in changes of evolutionary rates of sites along the branches of a phylogenetic tree. At the sequence level, covarion-like evolution at a site manifests as conservation of nucleotide or amino acid states among some homologs where the states are not conserved in other homologs (or groups of homologs. Covarion-like evolution has been shown to relate to changes in functions at sites in different clades, and, if ignored, can adversely affect the accuracy of phylogenetic inference. Results PROCOV (protein covarion analysis is a software tool that implements a number of previously proposed covarion models of protein evolution for phylogenetic inference in a maximum likelihood framework. Several algorithmic and implementation improvements in this tool over previous versions make computationally expensive tree searches with covarion models more efficient and analyses of large phylogenomic data sets tractable. PROCOV can be used to identify covarion sites by comparing the site likelihoods under the covarion process to the corresponding site likelihoods under a rates-across-sites (RAS process. Those sites with the greatest log-likelihood difference between a 'covarion' and an RAS process were found to be of functional or structural significance in a dataset of bacterial and eukaryotic elongation factors. Conclusion Covarion models implemented in PROCOV may be especially useful for phylogenetic estimation when ancient divergences between sequences have occurred and rates of evolution at sites are likely to have changed over the tree. It can also be used to study lineage-specific functional shifts in protein families that result in changes in the patterns of site variability among subtrees.
On Monte Carlo Simulation and Analysis of Electricity Markets
International Nuclear Information System (INIS)
This dissertation is about how Monte Carlo simulation can be used to analyse electricity markets. There are a wide range of applications for simulation; for example, players in the electricity market can use simulation to decide whether or not an investment can be expected to be profitable, and authorities can by means of simulation find out which consequences a certain market design can be expected to have on electricity prices, environmental impact, etc. In the first part of the dissertation, the focus is which electricity market models are suitable for Monte Carlo simulation. The starting point is a definition of an ideal electricity market. Such an electricity market is partly practical from a mathematical point of view (it is simple to formulate and does not require too complex calculations) and partly it is a representation of the best possible resource utilisation. The definition of the ideal electricity market is followed by analysis how the reality differs from the ideal model, what consequences the differences have on the rules of the electricity market and the strategies of the players, as well as how non-ideal properties can be included in a mathematical model. Particularly, questions about environmental impact, forecast uncertainty and grid costs are studied. The second part of the dissertation treats the Monte Carlo technique itself. To reduce the number of samples necessary to obtain accurate results, variance reduction techniques can be used. Here, six different variance reduction techniques are studied and possible applications are pointed out. The conclusions of these studies are turned into a method for efficient simulation of basic electricity markets. The method is applied to some test systems and the results show that the chosen variance reduction techniques can produce equal or better results using 99% fewer samples compared to when the same system is simulated without any variance reduction technique. More complex electricity market models
Using Maximum Likelihood analysis in HBT interferometry: bin-free treatment of correlated errors
International Nuclear Information System (INIS)
We present a new procedure, based on the Maximum Likelihood Method, for fitting the space-time size parameters of the particle production region in ultra-relativistic heavy ion collisions. This procedure offers two significant advantages: 1) it does not require sorting of the correlation data into arbitrary bins in the multidimensional momentum space and 2) it applies all available information on the experimental resolution error matrix separately to each correlated particle multiplet analyzed. These features permit extraction of maximum information from the data. The technique may be particularly important in ultra-relativistic heavy ion collisions, because in this energy domain large source radii and long source lifetimes are expected, and high-multiplicity HBT interferometry with a single collision event is a possibility. ((orig.))
Monte Carlo analysis of radiative transport in oceanographic lidar measurements
Energy Technology Data Exchange (ETDEWEB)
Cupini, E.; Ferro, G. [ENEA, Divisione Fisica Applicata, Centro Ricerche Ezio Clementel, Bologna (Italy); Ferrari, N. [Bologna Univ., Bologna (Italy). Dipt. Ingegneria Energetica, Nucleare e del Controllo Ambientale
2001-07-01
The analysis of oceanographic lidar systems measurements is often carried out with semi-empirical methods, since there is only a rough understanding of the effects of many environmental variables. The development of techniques for interpreting the accuracy of lidar measurements is needed to evaluate the effects of various environmental situations, as well as of different experimental geometric configurations and boundary conditions. A Monte Carlo simulation model represents a tool that is particularly well suited for answering these important questions. The PREMAR-2F Monte Carlo code has been developed taking into account the main molecular and non-molecular components of the marine environment. The laser radiation interaction processes of diffusion, re-emission, refraction and absorption are treated. In particular are considered: the Rayleigh elastic scattering, produced by atoms and molecules with small dimensions with respect to the laser emission wavelength (i.e. water molecules), the Mie elastic scattering, arising from atoms or molecules with dimensions comparable to the laser wavelength (hydrosols), the Raman inelastic scattering, typical of water, the absorption of water, inorganic (sediments) and organic (phytoplankton and CDOM) hydrosols, the fluorescence re-emission of chlorophyll and yellow substances. PREMAR-2F is an extension of a code for the simulation of the radiative transport in atmospheric environments (PREMAR-2). The approach followed in PREMAR-2 was to combine conventional Monte Carlo techniques with analytical estimates of the probability of the receiver to have a contribution from photons coming back after an interaction in the field of view of the lidar fluorosensor collecting apparatus. This offers an effective mean for modelling a lidar system with realistic geometric constraints. The retrieved semianalytic Monte Carlo radiative transfer model has been developed in the frame of the Italian Research Program for Antarctica (PNRA) and it is
Bueno, R. A.
1977-01-01
Results of the generalized likelihood ratio (GLR) technique for the detection of failures in aircraft application are presented, and its relationship to the properties of the Kalman-Bucy filter is examined. Under the assumption that the system is perfectly modeled, the detectability and distinguishability of four failure types are investigated by means of analysis and simulations. Detection of failures is found satisfactory, but problems in identifying correctly the mode of a failure may arise. These issues are closely examined as well as the sensitivity of GLR to modeling errors. The advantages and disadvantages of this technique are discussed, and various modifications are suggested to reduce its limitations in performance and computational complexity.
Extended Maximum Likelihood Halo-independent Analysis of Dark Matter Direct Detection Data
Gelmini, Graciela B; Gondolo, Paolo; Huh, Ji-Haeng
2015-01-01
We extend and correct a recently proposed maximum-likelihood halo-independent method to analyze unbinned direct dark matter detection data. Instead of the recoil energy as independent variable we use the minimum speed a dark matter particle must have to impart a given recoil energy to a nucleus. This has the advantage of allowing us to apply the method to any type of target composition and interaction, e.g. with general momentum and velocity dependence, and with elastic or inelastic scattering. We prove the method and provide a rigorous statistical interpretation of the results. As first applications, we find that for dark matter particles with elastic spin-independent interactions and neutron to proton coupling ratio $f_n/f_p=-0.7$, the WIMP interpretation of the signal observed by CDMS-II-Si is compatible with the constraints imposed by all other experiments with null results. We also find a similar compatibility for exothermic inelastic spin-independent interactions with $f_n/f_p=-0.8$.
Statistical analysis of maximum likelihood estimator images of human brain FDG PET studies
International Nuclear Information System (INIS)
The work presented in this paper evaluates the statistical characteristics of regional bias and expected error in reconstructions of real PET data of human brain fluorodeoxiglucose (FDG) studies carried out by the maximum likelihood estimator (MLE) method with a robust stopping rule, and compares them with the results of filtered backprojection (FBP) reconstructions and with the method of sieves. The task that the authors have investigated is that of quantifying radioisotope uptake in regions-of-interest (ROI's). They first describe a robust methodology for the use of the MLE method with clinical data which contains only one adjustable parameter: the kernel size for a Gaussian filtering operation that determines final resolution and expected regional error. Simulation results are used to establish the fundamental characteristics of the reconstructions obtained by out methodology, corresponding to the case in which the transition matrix is perfectly known. Then, data from 72 independent human brain FDG scans from four patients are used to show that the results obtained from real data are consistent with the simulation, although the quality of the data and of the transition matrix have an effect on the final outcome
Directory of Open Access Journals (Sweden)
Coenraad J Hattingh
2013-01-01
Full Text Available Background:Social anxiety disorder (SAD is characterised by abnormal fear and anxiety in social situations. Functional magnetic resonance imaging (fMRI is a brain imaging technique that can be used to illustrate neural activation to emotionally salient stimuli. However, no attempt has yet been made to statistically collate fMRI studies of brain activation, using the activation likelihood-estimate technique, in response to emotion recognition tasks in individuals with social anxiety disorder. Methods:A systematic search of fMRI studies of neural responses to socially emotive cues in SAD and GSP was undertaken. Activation likelihood-estimate (ALE meta-analysis, a voxel based meta-analytic technique, was used to estimate the most significant activations during emotional recognition. Results: 7 studies were eligible for inclusion in the meta-analysis, constituting a total of 91 subjects with SAD or GSP, and 93 healthy controls. The most significant areas of activation during emotional recognition versus neutral stimuli in individuals with social anxiety disorder compared to controls were: bilateral amygdala, left medial temporal lobe encompassing the entorhinal cortex, left medial aspect of the inferior temporal lobe encompassing perirhinal cortex and parahippocampus, right anterior cingulate, right globus pallidus, and distal tip of right postcentral gyrus.Conclusion:The results are consistent with neuroanatomic models of the role of the amygdala in fear conditioning, and the importance of the limbic circuitry in mediating anxiety symptoms.
Royle, J. Andrew; Chandler, Richard B.; Yackulic, Charles; Nichols, James D.
2012-01-01
1. Understanding the factors affecting species occurrence is a pre-eminent focus of applied ecological research. However, direct information about species occurrence is lacking for many species. Instead, researchers sometimes have to rely on so-called presence-only data (i.e. when no direct information about absences is available), which often results from opportunistic, unstructured sampling. MAXENT is a widely used software program designed to model and map species distribution using presence-only data. 2. We provide a critical review of MAXENT as applied to species distribution modelling and discuss how it can lead to inferential errors. A chief concern is that MAXENT produces a number of poorly defined indices that are not directly related to the actual parameter of interest – the probability of occurrence (ψ). This focus on an index was motivated by the belief that it is not possible to estimate ψ from presence-only data; however, we demonstrate that ψ is identifiable using conventional likelihood methods under the assumptions of random sampling and constant probability of species detection. 3. The model is implemented in a convenient r package which we use to apply the model to simulated data and data from the North American Breeding Bird Survey. We demonstrate that MAXENT produces extreme under-predictions when compared to estimates produced by logistic regression which uses the full (presence/absence) data set. We note that MAXENT predictions are extremely sensitive to specification of the background prevalence, which is not objectively estimated using the MAXENT method. 4. As with MAXENT, formal model-based inference requires a random sample of presence locations. Many presence-only data sets, such as those based on museum records and herbarium collections, may not satisfy this assumption. However, when sampling is random, we believe that inference should be based on formal methods that facilitate inference about interpretable ecological quantities
Lu, Dan; Ye, Ming; Curtis, Gary P.
2015-10-01
While Bayesian model averaging (BMA) has been widely used in groundwater modeling, it is infrequently applied to groundwater reactive transport modeling because of multiple sources of uncertainty in the coupled hydrogeochemical processes and because of the long execution time of each model run. To resolve these problems, this study analyzed different levels of uncertainty in a hierarchical way, and used the maximum likelihood version of BMA, i.e., MLBMA, to improve the computational efficiency. This study demonstrates the applicability of MLBMA to groundwater reactive transport modeling in a synthetic case in which twenty-seven reactive transport models were designed to predict the reactive transport of hexavalent uranium (U(VI)) based on observations at a former uranium mill site near Naturita, CO. These reactive transport models contain three uncertain model components, i.e., parameterization of hydraulic conductivity, configuration of model boundary, and surface complexation reactions that simulate U(VI) adsorption. These uncertain model components were aggregated into the alternative models by integrating a hierarchical structure into MLBMA. The modeling results of the individual models and MLBMA were analyzed to investigate their predictive performance. The predictive logscore results show that MLBMA generally outperforms the best model, suggesting that using MLBMA is a sound strategy to achieve more robust model predictions relative to a single model. MLBMA works best when the alternative models are structurally distinct and have diverse model predictions. When correlation in model structure exists, two strategies were used to improve predictive performance by retaining structurally distinct models or assigning smaller prior model probabilities to correlated models. Since the synthetic models were designed using data from the Naturita site, the results of this study are expected to provide guidance for real-world modeling. Limitations of applying MLBMA to the
The Monte Carlo Simulation Method for System Reliability and Risk Analysis
Zio, Enrico
2013-01-01
Monte Carlo simulation is one of the best tools for performing realistic analysis of complex systems as it allows most of the limiting assumptions on system behavior to be relaxed. The Monte Carlo Simulation Method for System Reliability and Risk Analysis comprehensively illustrates the Monte Carlo simulation method and its application to reliability and system engineering. Readers are given a sound understanding of the fundamentals of Monte Carlo sampling and simulation and its application for realistic system modeling. Whilst many of the topics rely on a high-level understanding of calculus, probability and statistics, simple academic examples will be provided in support to the explanation of the theoretical foundations to facilitate comprehension of the subject matter. Case studies will be introduced to provide the practical value of the most advanced techniques. This detailed approach makes The Monte Carlo Simulation Method for System Reliability and Risk Analysis a key reference for senior undergra...
Monte Carlo Alpha Iteration Algorithm for a Subcritical System Analysis
Directory of Open Access Journals (Sweden)
Hyung Jin Shim
2015-01-01
Full Text Available The α-k iteration method which searches the fundamental mode alpha-eigenvalue via iterative updates of the fission source distribution has been successfully used for the Monte Carlo (MC alpha-static calculations of supercritical systems. However, the α-k iteration method for the deep subcritical system analysis suffers from a gigantic number of neutron generations or a huge neutron weight, which leads to an abnormal termination of the MC calculations. In order to stably estimate the prompt neutron decay constant (α of prompt subcritical systems regardless of subcriticality, we propose a new MC alpha-static calculation method named as the α iteration algorithm. The new method is derived by directly applying the power method for the α-mode eigenvalue equation and its calculation stability is achieved by controlling the number of time source neutrons which are generated in proportion to α divided by neutron speed in MC neutron transport simulations. The effectiveness of the α iteration algorithm is demonstrated for two-group homogeneous problems with varying the subcriticality by comparisons with analytic solutions. The applicability of the proposed method is evaluated for an experimental benchmark of the thorium-loaded accelerator-driven system.
Monte-Carlo Application for Nondestructive Nuclear Waste Analysis
Carasco, C.; Engels, R.; Frank, M.; Furletov, S.; Furletova, J.; Genreith, C.; Havenith, A.; Kemmerling, G.; Kettler, J.; Krings, T.; Ma, J.-L.; Mauerhofer, E.; Neike, D.; Payan, E.; Perot, B.; Rossbach, M.; Schitthelm, O.; Schumann, M.; Vasquez, R.
2014-06-01
Radioactive waste has to undergo a process of quality checking in order to check its conformance with national regulations prior to its transport, intermediate storage and final disposal. Within the quality checking of radioactive waste packages non-destructive assays are required to characterize their radio-toxic and chemo-toxic contents. The Institute of Energy and Climate Research - Nuclear Waste Management and Reactor Safety of the Forschungszentrum Jülich develops in the framework of cooperation nondestructive analytical techniques for the routine characterization of radioactive waste packages at industrial-scale. During the phase of research and development Monte Carlo techniques are used to simulate the transport of particle, especially photons, electrons and neutrons, through matter and to obtain the response of detection systems. The radiological characterization of low and intermediate level radioactive waste drums is performed by segmented γ-scanning (SGS). To precisely and accurately reconstruct the isotope specific activity content in waste drums by SGS measurement, an innovative method called SGSreco was developed. The Geant4 code was used to simulate the response of the collimated detection system for waste drums with different activity and matrix configurations. These simulations allow a far more detailed optimization, validation and benchmark of SGSreco, since the construction of test drums covering a broad range of activity and matrix properties is time consuming and cost intensive. The MEDINA (Multi Element Detection based on Instrumental Neutron Activation) test facility was developed to identify and quantify non-radioactive elements and substances in radioactive waste drums. MEDINA is based on prompt and delayed gamma neutron activation analysis (P&DGNAA) using a 14 MeV neutron generator. MCNP simulations were carried out to study the response of the MEDINA facility in terms of gamma spectra, time dependence of the neutron energy spectrum
Composite likelihood estimation of demographic parameters
Directory of Open Access Journals (Sweden)
Garrigan Daniel
2009-11-01
Full Text Available Abstract Background Most existing likelihood-based methods for fitting historical demographic models to DNA sequence polymorphism data to do not scale feasibly up to the level of whole-genome data sets. Computational economies can be achieved by incorporating two forms of pseudo-likelihood: composite and approximate likelihood methods. Composite likelihood enables scaling up to large data sets because it takes the product of marginal likelihoods as an estimator of the likelihood of the complete data set. This approach is especially useful when a large number of genomic regions constitutes the data set. Additionally, approximate likelihood methods can reduce the dimensionality of the data by summarizing the information in the original data by either a sufficient statistic, or a set of statistics. Both composite and approximate likelihood methods hold promise for analyzing large data sets or for use in situations where the underlying demographic model is complex and has many parameters. This paper considers a simple demographic model of allopatric divergence between two populations, in which one of the population is hypothesized to have experienced a founder event, or population bottleneck. A large resequencing data set from human populations is summarized by the joint frequency spectrum, which is a matrix of the genomic frequency spectrum of derived base frequencies in two populations. A Bayesian Metropolis-coupled Markov chain Monte Carlo (MCMCMC method for parameter estimation is developed that uses both composite and likelihood methods and is applied to the three different pairwise combinations of the human population resequence data. The accuracy of the method is also tested on data sets sampled from a simulated population model with known parameters. Results The Bayesian MCMCMC method also estimates the ratio of effective population size for the X chromosome versus that of the autosomes. The method is shown to estimate, with reasonable
An Advanced Neutronic Analysis Toolkit with Inline Monte Carlo capability for BHTR Analysis
Energy Technology Data Exchange (ETDEWEB)
William R. Martin; John C. Lee
2009-12-30
Monte Carlo capability has been combined with a production LWR lattice physics code to allow analysis of high temperature gas reactor configurations, accounting for the double heterogeneity due to the TRISO fuel. The Monte Carlo code MCNP5 has been used in conjunction with CPM3, which was the testbench lattice physics code for this project. MCNP5 is used to perform two calculations for the geometry of interest, one with homogenized fuel compacts and the other with heterogeneous fuel compacts, where the TRISO fuel kernels are resolved by MCNP5.
Yang, Ji; Gu, Hongya; Yang, Ziheng
2004-01-01
Chalcone synthase (CHS) is a key enzyme in the biosynthesis of flavonoides, which are important for the pigmentation of flowers and act as attractants to pollinators. Genes encoding CHS constitute a multigene family in which the copy number varies among plant species and functional divergence appears to have occurred repeatedly. In morning glories (Ipomoea), five functional CHS genes (A-E) have been described. Phylogenetic analysis of the Ipomoea CHS gene family revealed that CHS A, B, and C experienced accelerated rates of amino acid substitution relative to CHS D and E. To examine whether the CHS genes of the morning glories underwent adaptive evolution, maximum-likelihood models of codon substitution were used to analyze the functional sequences in the Ipomoea CHS gene family. These models used the nonsynonymous/synonymous rate ratio (omega = d(N)/ d(S)) as an indicator of selective pressure and allowed the ratio to vary among lineages or sites. Likelihood ratio test suggested significant variation in selection pressure among amino acid sites, with a small proportion of them detected to be under positive selection along the branches ancestral to CHS A, B, and C. Positive Darwinian selection appears to have promoted the divergence of subfamily ABC and subfamily DE and is at least partially responsible for a rate increase following gene duplication. PMID:14743314
Kudrolli, Haris A.
2001-04-01
A three dimensional (3D) reconstruction procedure for Positron Emission Tomography (PET) based on inverse Monte Carlo analysis is presented. PET is a medical imaging modality which employs a positron emitting radio-tracer to give functional images of an organ's metabolic activity. This makes PET an invaluable tool in the detection of cancer and for in-vivo biochemical measurements. There are a number of analytical and iterative algorithms for image reconstruction of PET data. Analytical algorithms are computationally fast, but the assumptions intrinsic in the line integral model limit their accuracy. Iterative algorithms can apply accurate models for reconstruction and give improvements in image quality, but at an increased computational cost. These algorithms require the explicit calculation of the system response matrix, which may not be easy to calculate. This matrix gives the probability that a photon emitted from a certain source element will be detected in a particular detector line of response. The ``Three Dimensional Stochastic Sampling'' (SS3D) procedure implements iterative algorithms in a manner that does not require the explicit calculation of the system response matrix. It uses Monte Carlo techniques to simulate the process of photon emission from a source distribution and interaction with the detector. This technique has the advantage of being able to model complex detector systems and also take into account the physics of gamma ray interaction within the source and detector systems, which leads to an accurate image estimate. A series of simulation studies was conducted to validate the method using the Maximum Likelihood - Expectation Maximization (ML-EM) algorithm. The accuracy of the reconstructed images was improved by using an algorithm that required a priori knowledge of the source distribution. Means to reduce the computational time for reconstruction were explored by using parallel processors and algorithms that had faster convergence rates
Further experience in Bayesian analysis using Monte Carlo Integration
Dijk, Herman; Kloek, Teun
1980-01-01
textabstractAn earlier paper [Kloek and Van Dijk (1978)] is extended in three ways. First, Monte Carlo integration is performed in a nine-dimensional parameter space of Klein's model I [Klein (1950)]. Second, Monte Carlo is used as a tool for the elicitation of a uniform prior on a finite region by making use of several types of prior information. Third, special attention is given to procedures for the construction of importance functions which make use of nonlinear optimization methods. *1 T...
Pestotnik, R.; Križan, P.; Korpar, S.; Iijima, T.
2008-09-01
The use of a sequence of aerogel radiators with different refractive indices in a proximity focusing Cherenkov ring imaging detector has been shown to improve the resolution of the Cherenkov angle. In order to obtain further information on the capabilities of such a detector, a maximum-likelihood analysis has been performed on simulated data, with the simulation being appropriate for the upgraded Belle detector. The results show that by using a sequence of two aerogel layers with different refractive indices, the K/π separation efficiency is improved in the kinematic region above 3 GeV/ c. In the low momentum region, the focusing configuration (with n1 and n2 chosen such that the Cherenkov rings from different aerogel layers at 4 GeV/ c overlap) shows a better performance than the defocusing one (where the two Cherenkov rings are well separated).
DEFF Research Database (Denmark)
De Carvalho, Elisabeth; Omar, Samir; Slock, Dirk
2013-01-01
We analyze two algorithms that have been introduced previously for Deterministic Maximum Likelihood (DML) blind estimation of multiple FIR channels. The first one is a modification of the Iterative Quadratic ML (IQML) algorithm. IQML gives biased estimates of the channel and performs poorly at low...... SNR due to noise induced bias. The IQML cost function can be “denoised” by eliminating the noise contribution: the resulting algorithm, Denoised IQML (DIQML), gives consistent estimates and outperforms IQML. Furthermore, DIQML is asymptotically globally convergent and hence insensitive......, but requires a consistent initialization. We furthermore compare DIQML and PQML to the strategy of alternating minimization w.r.t. symbols and channel for solving DML (AQML). An asymptotic performance analysis, a complexity evaluation and simulation results are also presented. The proposed DIQML and PQML...
Fast inference in generalized linear models via expected log-likelihoods.
Ramirez, Alexandro D; Paninski, Liam
2014-04-01
Generalized linear models play an essential role in a wide variety of statistical applications. This paper discusses an approximation of the likelihood in these models that can greatly facilitate computation. The basic idea is to replace a sum that appears in the exact log-likelihood by an expectation over the model covariates; the resulting "expected log-likelihood" can in many cases be computed significantly faster than the exact log-likelihood. In many neuroscience experiments the distribution over model covariates is controlled by the experimenter and the expected log-likelihood approximation becomes particularly useful; for example, estimators based on maximizing this expected log-likelihood (or a penalized version thereof) can often be obtained with orders of magnitude computational savings compared to the exact maximum likelihood estimators. A risk analysis establishes that these maximum EL estimators often come with little cost in accuracy (and in some cases even improved accuracy) compared to standard maximum likelihood estimates. Finally, we find that these methods can significantly decrease the computation time of marginal likelihood calculations for model selection and of Markov chain Monte Carlo methods for sampling from the posterior parameter distribution. We illustrate our results by applying these methods to a computationally-challenging dataset of neural spike trains obtained via large-scale multi-electrode recordings in the primate retina.
Wen, Yalu; Lu, Qing
2016-09-01
Although compelling evidence suggests that the genetic etiology of complex diseases could be heterogeneous in subphenotype groups, little attention has been paid to phenotypic heterogeneity in genetic association analysis of complex diseases. Simply ignoring phenotypic heterogeneity in association analysis could result in attenuated estimates of genetic effects and low power of association tests if subphenotypes with similar clinical manifestations have heterogeneous underlying genetic etiologies. To facilitate the family-based association analysis allowing for phenotypic heterogeneity, we propose a clustered multiclass likelihood-ratio ensemble (CMLRE) method. The proposed method provides an alternative way to model the complex relationship between disease outcomes and genetic variants. It allows for heterogeneous genetic causes of disease subphenotypes and can be applied to various pedigree structures. Through simulations, we found CMLRE outperformed the commonly adopted strategies in a variety of underlying disease scenarios. We further applied CMLRE to a family-based dataset from the International Consortium to Identify Genes and Interactions Controlling Oral Clefts (ICOC) to investigate the genetic variants and interactions predisposing to subphenotypes of oral clefts. The analysis suggested that two subphenotypes, nonsyndromic cleft lip without palate (CL) and cleft lip with palate (CLP), shared similar genetic etiologies, while cleft palate only (CP) had its own genetic mechanism. The analysis further revealed that rs10863790 (IRF6), rs7017252 (8q24), and rs7078160 (VAX1) were jointly associated with CL/CLP, while rs7969932 (TBK1), rs227731 (17q22), and rs2141765 (TBK1) jointly contributed to CP.
Samson, Fabienne; Zeffiro, Thomas A.; Toussaint, Alain; Belin, Pascal
2011-01-01
Investigations of the functional organization of human auditory cortex typically examine responses to different sound categories. An alternative approach is to characterize sounds with respect to their amount of variation in the time and frequency domains (i.e., spectral and temporal complexity). Although the vast majority of published studies examine contrasts between discrete sound categories, an alternative complexity-based taxonomy can be evaluated through meta-analysis. In a quantitative...
Simulations of Baryon Acoustic Oscillations III: Likelihood analysis of the matter power spectrum
Takahashi, Ryuichi; Takada, Masahiro; Matsubara, Takahiko; Sugiyama, Naoshi; Kayo, Issha; Nishimichi, Takahiro; Saito, Shun; Taruya, Atsushi
2009-01-01
We study the sample variance of the matter power spectrum for the standard Lambda Cold Dark Matter universe. We use a total of 5000 cosmological N-body cosmological simulations to study in detail the distribution of the best-fit cosmological parameters and the baryon acoustic peak positions. The obtained distribution is compared with the results from the Fisher matrix analysis with and without including non-Gaussian errors. For the Fisher matrix analysis, we compute the derivatives of the matter power spectrum with respect to cosmological parameters using directly full nonlinear simulations. We show that the non-Gaussian errors increase the unmarginalized errors by up to a factor 5 for k_{max}=0.4h/Mpc if there is only one free parameter provided other parameters are well determined by external information. On the other hand, for multi-parameter fitting, the impact of the non-Gaussian errors is significantly mitigated due to severe parameter degeneracies in the power spectrum. The distribution of the acoustic...
Event-related fMRI studies of false memory: An Activation Likelihood Estimation meta-analysis.
Kurkela, Kyle A; Dennis, Nancy A
2016-01-29
Over the last two decades, a wealth of research in the domain of episodic memory has focused on understanding the neural correlates mediating false memories, or memories for events that never happened. While several recent qualitative reviews have attempted to synthesize this literature, methodological differences amongst the empirical studies and a focus on only a sub-set of the findings has limited broader conclusions regarding the neural mechanisms underlying false memories. The current study performed a voxel-wise quantitative meta-analysis using activation likelihood estimation to investigate commonalities within the functional magnetic resonance imaging (fMRI) literature studying false memory. The results were broken down by memory phase (encoding, retrieval), as well as sub-analyses looking at differences in baseline (hit, correct rejection), memoranda (verbal, semantic), and experimental paradigm (e.g., semantic relatedness and perceptual relatedness) within retrieval. Concordance maps identified significant overlap across studies for each analysis. Several regions were identified in the general false retrieval analysis as well as multiple sub-analyses, indicating their ubiquitous, yet critical role in false retrieval (medial superior frontal gyrus, left precentral gyrus, left inferior parietal cortex). Additionally, several regions showed baseline- and paradigm-specific effects (hit/perceptual relatedness: inferior and middle occipital gyrus; CRs: bilateral inferior parietal cortex, precuneus, left caudate). With respect to encoding, analyses showed common activity in the left middle temporal gyrus and anterior cingulate cortex. No analysis identified a common cluster of activation in the medial temporal lobe. PMID:26683385
Efficient strategies for genome scanning using maximum-likelihood affected-sib-pair analysis
Energy Technology Data Exchange (ETDEWEB)
Holmans, P.; Craddock, N. [Univ. of Wales College of Medicine, Cardiff (United Kingdom)
1997-03-01
Detection of linkage with a systematic genome scan in nuclear families including an affected sibling pair is an important initial step on the path to cloning susceptibility genes for complex genetic disorders, and it is desirable to optimize the efficiency of such studies. The aim is to maximize power while simultaneously minimizing the total number of genotypings and probability of type I error. One approach to increase efficiency, which has been investigated by other workers, is grid tightening: a sample is initially typed using a coarse grid of markers, and promising results are followed up by use of a finer grid. Another approach, not previously considered in detail in the context of an affected-sib-pair genome scan for linkage, is sample splitting: a portion of the sample is typed in the screening stage, and promising results are followed up in the whole sample. In the current study, we have used computer simulation to investigate the relative efficiency of two-stage strategies involving combinations of both grid tightening and sample splitting and found that the optimal strategy incorporates both approaches. In general, typing half the sample of affected pairs with a coarse grid of markers in the screening stage is an efficient strategy under a variety of conditions. If Hardy-Weinberg equilibrium holds, it is most efficient not to type parents in the screening stage. If Hardy-Weinberg equilibrium does not hold (e.g., because of stratification) failure to type parents in the first stage increases the amount of genotyping required, although the overall probability of type I error is not greatly increased, provided the parents are used in the final analysis. 23 refs., 4 figs., 5 tabs.
Analytical band Monte Carlo analysis of electron transport in silicene
Yeoh, K. H.; Ong, D. S.; Ooi, C. H. Raymond; Yong, T. K.; Lim, S. K.
2016-06-01
An analytical band Monte Carlo (AMC) with linear energy band dispersion has been developed to study the electron transport in suspended silicene and silicene on aluminium oxide (Al2O3) substrate. We have calibrated our model against the full band Monte Carlo (FMC) results by matching the velocity-field curve. Using this model, we discover that the collective effects of charge impurity scattering and surface optical phonon scattering can degrade the electron mobility down to about 400 cm2 V‑1 s‑1 and thereafter it is less sensitive to the changes of charge impurity in the substrate and surface optical phonon. We also found that further reduction of mobility to ∼100 cm2 V‑1 s‑1 as experimentally demonstrated by Tao et al (2015 Nat. Nanotechnol. 10 227) can only be explained by the renormalization of Fermi velocity due to interaction with Al2O3 substrate.
Analysis of neutron-reflectometry data by Monte Carlo technique
Singh, S
2002-01-01
Neutron-reflectometry data is collected in momentum space. The real-space information is extracted by fitting a model for the structure of a thin-film sample. We have attempted a Monte Carlo technique to extract the structure of the thin film. In this technique we change the structural parameters of the thin film by simulated annealing based on the Metropolis algorithm. (orig.)
Maintaining symmetry of simulated likelihood functions
DEFF Research Database (Denmark)
Andersen, Laura Mørch
This paper suggests solutions to two different types of simulation errors related to Quasi-Monte Carlo integration. Likelihood functions which depend on standard deviations of mixed parameters are symmetric in nature. This paper shows that antithetic draws preserve this symmetry and thereby...
Finite-Time Analysis of Stratified Sampling for Monte Carlo
Carpentier, Alexandra; Munos, Rémi
2011-01-01
International audience We consider the problem of stratified sampling for Monte-Carlo integration. We model this problem in a multi-armed bandit setting, where the arms represent the strata, and the goal is to estimate a weighted average of the mean values of the arms. We propose a strategy that samples the arms according to an upper bound on their standard deviations and compare its estimation quality to an ideal allocation that would know the standard deviations of the strata. We provide...
Gauge Potts model with generalized action: A Monte Carlo analysis
Energy Technology Data Exchange (ETDEWEB)
Fanchiotti, H.; Canal, C.A.G.; Sciutto, S.J.
1985-08-15
Results of a Monte Carlo calculation on the q-state gauge Potts model in d dimensions with a generalized action involving planar 1 x 1, plaquette, and 2 x 1, fenetre, loop interactions are reported. For d = 3 and q = 2, first- and second-order phase transitions are detected. The phase diagram for q = 3 presents only first-order phase transitions. For d = 2, a comparison with analytical results is made. Here also, the behavior of the numerical simulation in the vicinity of a second-order transition is analyzed.
Accelerated Monte Carlo Simulation for Safety Analysis of the Advanced Airspace Concept
Thipphavong, David
2010-01-01
Safe separation of aircraft is a primary objective of any air traffic control system. An accelerated Monte Carlo approach was developed to assess the level of safety provided by a proposed next-generation air traffic control system. It combines features of fault tree and standard Monte Carlo methods. It runs more than one order of magnitude faster than the standard Monte Carlo method while providing risk estimates that only differ by about 10%. It also preserves component-level model fidelity that is difficult to maintain using the standard fault tree method. This balance of speed and fidelity allows sensitivity analysis to be completed in days instead of weeks or months with the standard Monte Carlo method. Results indicate that risk estimates are sensitive to transponder, pilot visual avoidance, and conflict detection failure probabilities.
Directory of Open Access Journals (Sweden)
Pascal eMolenberghs
2012-04-01
Full Text Available The critical lesion site responsible for the syndrome of unilateral spatial neglect has been debated for more than a decade. Here we performed an activation likelihood estimation (ALE to provide for the first time an objective quantitative index of the consistency of lesion sites across anatomical group studies of spatial neglect. The analysis revealed several distinct regions in which damage has consistently been associated with spatial neglect symptoms. Lesioned clusters were located in several cortical and subcortical regions of the right hemisphere, including the middle and superior temporal gyrus, inferior parietal lobule, intraparietal sulcus, precuneus, middle occipital gyrus, caudate nucleus and posterior insula, as well as in the white matter pathway corresponding to the posterior part of the superior longitudinal fasciculus. Further analyses suggested that separate lesion sites are associated with impairments in different behavioural tests, such as line bisection and target cancellation. Similarly, specific subcomponents of the heterogeneous neglect syndrome, such as extinction and allocentric and personal neglect, are associated with distinct lesion sites. Future progress in delineating the neuropathological correlates of spatial neglect will depend upon the development of more refined measures of perceptual and cognitive functions than those currently available in the clinical setting.
International Nuclear Information System (INIS)
An unbalanced nested sampling design was used to investigate the spatial scale of soil and herbicide interactions at the field scale. A hierarchical analysis of variance based on residual maximum likelihood (REML) was used to analyse the data and provide a first estimate of the variogram. Soil samples were taken at 108 locations at a range of separating distances in a 9 ha field to explore small and medium scale spatial variation. Soil organic matter content, pH, particle size distribution, microbial biomass and the degradation and sorption of the herbicide, isoproturon, were determined for each soil sample. A large proportion of the spatial variation in isoproturon degradation and sorption occurred at sampling intervals less than 60 m, however, the sampling design did not resolve the variation present at scales greater than this. A sampling interval of 20-25 m should ensure that the main spatial structures are identified for isoproturon degradation rate and sorption without too great a loss of information in this field. - Estimating the spatial scale of herbicide and soil interactions by nested sampling.
pyNSMC: A Python Module for Null-Space Monte Carlo Uncertainty Analysis
White, J.; Brakefield, L. K.
2015-12-01
The null-space monte carlo technique is a non-linear uncertainty analyses technique that is well-suited to high-dimensional inverse problems. While the technique is powerful, the existing workflow for completing null-space monte carlo is cumbersome, requiring the use of multiple commandline utilities, several sets of intermediate files and even a text editor. pyNSMC is an open-source python module that automates the workflow of null-space monte carlo uncertainty analyses. The module is fully compatible with the PEST and PEST++ software suites and leverages existing functionality of pyEMU, a python framework for linear-based uncertainty analyses. pyNSMC greatly simplifies the existing workflow for null-space monte carlo by taking advantage of object oriented design facilities in python. The core of pyNSMC is the ensemble class, which draws and stores realized random vectors and also provides functionality for exporting and visualizing results. By relieving users of the tedium associated with file handling and command line utility execution, pyNSMC instead focuses the user on the important steps and assumptions of null-space monte carlo analysis. Furthermore, pyNSMC facilitates learning through flow charts and results visualization, which are available at many points in the algorithm. The ease-of-use of the pyNSMC workflow is compared to the existing workflow for null-space monte carlo for a synthetic groundwater model with hundreds of estimable parameters.
Maucec, M.; Rigollet, C.
2004-01-01
The performance of a detection system based on the pulsed fast/thermal neutron analysis technique was assessed using Monte Carlo simulations. The aim was to develop and implement simulation methods, to support and advance the data analysis techniques of the characteristic gamma-ray spectra, potentia
Lautenschlager, Gary J.
The parallel analysis method for determining the number of components to retain in a principal components analysis has received a recent resurgence of support and interest. However, researchers and practitioners desiring to use this criterion have been hampered by the required Monte Carlo analyses needed to develop the criteria. Two recent…
Rising Above Chaotic Likelihoods
Du, Hailiang
2014-01-01
Berliner (Likelihood and Bayesian prediction for chaotic systems, J. Am. Stat. Assoc. 1991) identified a number of difficulties in using the likelihood function within the Bayesian paradigm for state estimation and parameter estimation of chaotic systems. Even when the equations of the system are given, he demonstrated "chaotic likelihood functions" of initial conditions and parameter values in the 1-D Logistic Map. Chaotic likelihood functions, while ultimately smooth, have such complicated small scale structure as to cast doubt on the possibility of identifying high likelihood estimates in practice. In this paper, the challenge of chaotic likelihoods is overcome by embedding the observations in a higher dimensional sequence-space, which is shown to allow good state estimation with finite computational power. An Importance Sampling approach is introduced, where Pseudo-orbit Data Assimilation is employed in the sequence-space in order first to identify relevant pseudo-orbits and then relevant trajectories. Es...
Directory of Open Access Journals (Sweden)
Thanh-Son Tran
2012-05-01
Full Text Available Abstract Background The serovars Enteritidis and Typhimurium of the Gram-negative bacterium Salmonella enterica are significant causes of human food poisoning. Fowl carrying these bacteria often show no clinical disease, with detection only established post-mortem. Increased resistance to the carrier state in commercial poultry could be a way to improve food safety by reducing the spread of these bacteria in poultry flocks. Previous studies identified QTLs for both resistance to carrier state and resistance to Salmonella colonization in the same White Leghorn inbred lines. Until now, none of the QTLs identified was common to the two types of resistance. All these analyses were performed using the F2 inbred or backcross option of the QTLExpress software based on linear regression. In the present study, QTL analysis was achieved using Maximum Likelihood with QTLMap software, in order to test the effect of the QTL analysis method on QTL detection. We analyzed the same phenotypic and genotypic data as those used in previous studies, which were collected on 378 animals genotyped with 480 genome-wide SNP markers. To enrich these data, we added eleven SNP markers located within QTLs controlling resistance to colonization and we looked for potential candidate genes co-localizing with QTLs. Results In our case the QTL analysis method had an important impact on QTL detection. We were able to identify new genomic regions controlling resistance to carrier-state, in particular by testing the existence of two segregating QTLs. But some of the previously identified QTLs were not confirmed. Interestingly, two QTLs were detected on chromosomes 2 and 3, close to the locations of the major QTLs controlling resistance to colonization and to candidate genes involved in the immune response identified in other, independent studies. Conclusions Due to the lack of stability of the QTLs detected, we suggest that interesting regions for further studies are those that were
General purpose dynamic Monte Carlo with continuous energy for transient analysis
Energy Technology Data Exchange (ETDEWEB)
Sjenitzer, B. L.; Hoogenboom, J. E. [Delft Univ. of Technology, Dept. of Radiation, Radionuclide and Reactors, Mekelweg 15, 2629JB Delft (Netherlands)
2012-07-01
For safety assessments transient analysis is an important tool. It can predict maximum temperatures during regular reactor operation or during an accident scenario. Despite the fact that this kind of analysis is very important, the state of the art still uses rather crude methods, like diffusion theory and point-kinetics. For reference calculations it is preferable to use the Monte Carlo method. In this paper the dynamic Monte Carlo method is implemented in the general purpose Monte Carlo code Tripoli4. Also, the method is extended for use with continuous energy. The first results of Dynamic Tripoli demonstrate that this kind of calculation is indeed accurate and the results are achieved in a reasonable amount of time. With the method implemented in Tripoli it is now possible to do an exact transient calculation in arbitrary geometry. (authors)
Advanced Mesh-Enabled Monte carlo capability for Multi-Physics Reactor Analysis
Energy Technology Data Exchange (ETDEWEB)
Wilson, Paul; Evans, Thomas; Tautges, Tim
2012-12-24
This project will accumulate high-precision fluxes throughout reactor geometry on a non- orthogonal grid of cells to support multi-physics coupling, in order to more accurately calculate parameters such as reactivity coefficients and to generate multi-group cross sections. This work will be based upon recent developments to incorporate advanced geometry and mesh capability in a modular Monte Carlo toolkit with computational science technology that is in use in related reactor simulation software development. Coupling this capability with production-scale Monte Carlo radiation transport codes can provide advanced and extensible test-beds for these developments. Continuous energy Monte Carlo methods are generally considered to be the most accurate computational tool for simulating radiation transport in complex geometries, particularly neutron transport in reactors. Nevertheless, there are several limitations for their use in reactor analysis. Most significantly, there is a trade-off between the fidelity of results in phase space, statistical accuracy, and the amount of computer time required for simulation. Consequently, to achieve an acceptable level of statistical convergence in high-fidelity results required for modern coupled multi-physics analysis, the required computer time makes Monte Carlo methods prohibitive for design iterations and detailed whole-core analysis. More subtly, the statistical uncertainty is typically not uniform throughout the domain, and the simulation quality is limited by the regions with the largest statistical uncertainty. In addition, the formulation of neutron scattering laws in continuous energy Monte Carlo methods makes it difficult to calculate adjoint neutron fluxes required to properly determine important reactivity parameters. Finally, most Monte Carlo codes available for reactor analysis have relied on orthogonal hexahedral grids for tallies that do not conform to the geometric boundaries and are thus generally not well
Reliability analysis of tunnel surrounding rock stability by Monte-Carlo method
Institute of Scientific and Technical Information of China (English)
XI Jia-mi; YANG Geng-she
2008-01-01
Discussed advantages of improved Monte-Carlo method and feasibility aboutproposed approach applying in reliability analysis for tunnel surrounding rock stability. Onthe basis of deterministic parsing for tunnel surrounding rock, reliability computing methodof surrounding rock stability was derived from improved Monte-Carlo method. The com-puting method considered random of related parameters, and therefore satisfies relativityamong parameters. The proposed method can reasonably determine reliability of sur-rounding rock stability. Calculation results show that this method is a scientific method indiscriminating and checking surrounding rock stability.
The seasonal KPSS test when neglecting seasonal dummies: a Monte Carlo analysis
El Montasser, Ghassen; Boufateh, Talel; Issaoui, Fakhri
2013-01-01
This paper shows through a Monte Carlo analysis the effect of neglecting seasonal deterministics on the seasonal KPSS test. We found that the test is most of the time heavily oversized and not convergent in this case. In addition, Bartlett-type non-parametric correction of error variances did not signally change the test's rejection frequencies.
Ainscow, E K; Brand, M D
1998-09-21
The errors associated with experimental application of metabolic control analysis are difficult to assess. In this paper, we give examples where Monte-Carlo simulations of published experimental data are used in error analysis. Data was simulated according to the mean and error obtained from experimental measurements and the simulated data was used to calculate control coefficients. Repeating the simulation 500 times allowed an estimate to be made of the error implicit in the calculated control coefficients. In the first example, state 4 respiration of isolated mitochondria, Monte-Carlo simulations based on the system elasticities were performed. The simulations gave error estimates similar to the values reported within the original paper and those derived from a sensitivity analysis of the elasticities. This demonstrated the validity of the method. In the second example, state 3 respiration of isolated mitochondria, Monte-Carlo simulations were based on measurements of intermediates and fluxes. A key feature of this simulation was that the distribution of the simulated control coefficients did not follow a normal distribution, despite simulation of the original data being based on normal distributions. Consequently, the error calculated using simulation was greater and more realistic than the error calculated directly by averaging the original results. The Monte-Carlo simulations are also demonstrated to be useful in experimental design. The individual data points that should be repeated in order to reduce the error in the control coefficients can be highlighted.
Comparative Criticality Analysis of Two Monte Carlo Codes on Centrifugal Atomizer: MCNPS and SCALE
Energy Technology Data Exchange (ETDEWEB)
Kang, H-S; Jang, M-S; Kim, S-R [NESS, Daejeon (Korea, Republic of); Park, J-M; Kim, K-N [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2015-10-15
There are two well-known Monte Carlo codes for criticality analysis, MCNP5 and SCALE. MCNP5 is a general-purpose Monte Carlo N-Particle code that can be used for neutron, photon, electron or coupled neutron / photon / electron transport, including the capability to calculate eigenvalues for critical system as a main analysis code. SCALE provides a comprehensive, verified and validated, user-friendly tool set for criticality safety, reactor physics, radiation shielding, radioactive source term characterization, and sensitivity and uncertainty analysis. SCALE was conceived and funded by US NRC to perform standardized computer analysis for licensing evaluation and is used widely in the world. We performed a validation test of MCNP5 and a comparative analysis of Monte Carlo codes, MCNP5 and SCALE, in terms of the critical analysis of centrifugal atomizer. In the criticality analysis using MCNP5 code, we obtained the statistically reliable results by using a large number of source histories per cycle and performing of uncertainty analysis.
Risk Analysis of Tilapia Recirculating Aquaculture Systems: A Monte Carlo Simulation Approach
Kodra, Bledar
2007-01-01
Risk Analysis of Tilapia Recirculating Aquaculture Systems: A Monte Carlo Simulation Approach Bledar Kodra (ABSTRACT) The purpose of this study is to modify an existing static analytical model developed for a Re-circulating Aquaculture Systems through incorporation of risk considerations to evaluate the economic viability of the system. In addition the objective of this analysis is to provide a well documented risk based analytical system so that individuals (investors/lenders) c...
McGee, Steven
2002-01-01
Likelihood ratios are one of the best measures of diagnostic accuracy, although they are seldom used, because interpreting them requires a calculator to convert back and forth between “probability” and “odds” of disease. This article describes a simpler method of interpreting likelihood ratios, one that avoids calculators, nomograms, and conversions to “odds” of disease. Several examples illustrate how the clinician can use this method to refine diagnostic decisions at the bedside.
Perturbation analysis for Monte Carlo continuous cross section models
International Nuclear Information System (INIS)
Sensitivity analysis, including both its forward and adjoint applications, collectively referred to hereinafter as Perturbation Analysis (PA), is an essential tool to complete Uncertainty Quantification (UQ) and Data Assimilation (DA). PA-assisted UQ and DA have traditionally been carried out for reactor analysis problems using deterministic as opposed to stochastic models for radiation transport. This is because PA requires many model executions to quantify how variations in input data, primarily cross sections, affect variations in model's responses, e.g. detectors readings, flux distribution, multiplication factor, etc. Although stochastic models are often sought for their higher accuracy, their repeated execution is at best computationally expensive and in reality intractable for typical reactor analysis problems involving many input data and output responses. Deterministic methods however achieve computational efficiency needed to carry out the PA analysis by reducing problem dimensionality via various spatial and energy homogenization assumptions. This however introduces modeling error components into the PA results which propagate to the following UQ and DA analyses. The introduced errors are problem specific and therefore are expected to limit the applicability of UQ and DA analyses to reactor systems that satisfy the introduced assumptions. This manuscript introduces a new method to complete PA employing a continuous cross section stochastic model and performed in a computationally efficient manner. If successful, the modeling error components introduced by deterministic methods could be eliminated, thereby allowing for wider applicability of DA and UQ results. Two MCNP models demonstrate the application of the new method - a Critical Pu Sphere (Jezebel), a Pu Fast Metal Array (Russian BR-1). The PA is completed for reaction rate densities, reaction rate ratios, and the multiplication factor. (author)
Empirical likelihood estimation of discretely sampled processes of OU type
Institute of Scientific and Technical Information of China (English)
SUN ShuGuang; ZHANG XinSheng
2009-01-01
This paper presents an empirical likelihood estimation procedure for parameters of the discretely sampled process of Ornstein-Uhlenbeck type. The proposed procedure is based on the condi-tional characteristic function, and the maximum empirical likelihood estimator is proved to be consistent and asymptotically normal. Moreover, this estimator is shown to be asymptotically efficient under some tensity parameter can be exactly recovered, and we study the maximum empirical likelihood estimator with the plug-in estimated intensity parameter. Testing procedures based on the empirical likelihood ratio statistic are developed for parameters and for estimating equations, respectively. Finally, Monte Carlo simulations are conducted to demonstrate the performance of proposed estimators.
Chiappo, A; Conrad, J; Strigari, L E; Anderson, B; Sanchez-Conde, M A
2016-01-01
Line-of-sight integrals of the squared density, commonly called the J-factor, are essential for inferring dark matter annihilation signals. The J-factors of dark matter-dominated dwarf spheroidal satellite galaxies (dSphs) have typically been derived using Bayesian techniques, which for small data samples implies that a choice of priors constitutes a non-negligible systematic uncertainty. Here we report the development of a new fully frequentist approach to construct the profile likelihood of the J-factor. Using stellar kinematic data from several classical and ultra-faint dSphs, we derive the maximum likelihood value for the J-factor and its confidence intervals. We validate this method, in particular its bias and coverage, using simulated data from the Gaia Challenge. We find that the method possesses good statistical properties. The J-factors and their uncertainties are generally in good agreement with the Bayesian-derived values, with the largest deviations restricted to the systems with the smallest kine...
International Nuclear Information System (INIS)
A domain decomposed Monte Carlo communication kernel is used to carry out performance tests to establish the feasibility of using Monte Carlo techniques for practical Light Water Reactor (LWR) core analyses. The results of the prototype code are interpreted in the context of simplified performance models which elucidate key scaling regimes of the parallel algorithm.
Houska, Tobias; Multsch, Sebastian; Kraft, Philipp; Frede, Hans-Georg; Breuer, Lutz
2014-05-01
Computer simulations are widely used to support decision making and planning in the agriculture sector. On the one hand, many plant growth models use simplified hydrological processes and structures, e.g. by the use of a small number of soil layers or by the application of simple water flow approaches. On the other hand, in many hydrological models plant growth processes are poorly represented. Hence, fully coupled models with a high degree of process representation would allow a more detailed analysis of the dynamic behaviour of the soil-plant interface. We used the Python programming language to couple two of such high process oriented independent models and to calibrate both models simultaneously. The Catchment Modelling Framework (CMF) simulated soil hydrology based on the Richards equation and the Van-Genuchten-Mualem retention curve. CMF was coupled with the Plant growth Modelling Framework (PMF), which predicts plant growth on the basis of radiation use efficiency, degree days, water shortage and dynamic root biomass allocation. The Monte Carlo based Generalised Likelihood Uncertainty Estimation (GLUE) method was applied to parameterize the coupled model and to investigate the related uncertainty of model predictions to it. Overall, 19 model parameters (4 for CMF and 15 for PMF) were analysed through 2 x 106 model runs randomly drawn from an equally distributed parameter space. Three objective functions were used to evaluate the model performance, i.e. coefficient of determination (R2), bias and model efficiency according to Nash Sutcliffe (NSE). The model was applied to three sites with different management in Muencheberg (Germany) for the simulation of winter wheat (Triticum aestivum L.) in a cross-validation experiment. Field observations for model evaluation included soil water content and the dry matters of roots, storages, stems and leaves. Best parameter sets resulted in NSE of 0.57 for the simulation of soil moisture across all three sites. The shape
Genius, Margarita; Strazzera, Elisabetta
2005-01-01
A Monte Carlo analysis is conducted to assess the validity of the bivariate modeling approach for detection and correction of different forms of elicitation effects in Double Bound Contingent Valuation data. Alternative univariate and bivariate models are applied to several simulated data sets, each one characterized by a specific elicitation effect, and their performance is assessed using standard selection criteria. The bivariate models include the standard Bivariate Probit model, and an al...
Risk analysis and Monte Carlo simulation applied to the generation of drilling AFE estimates
International Nuclear Information System (INIS)
This paper presents a method for developing an authorization-for-expenditure (AFE)-generating model and illustrates the technique with a specific offshore field development case study. The model combines Monte Carlo simulation and statistical analysis of historical drilling data to generate more accurate, risked, AFE estimates. In addition to the general method, two examples of making AFE time estimates for North Sea wells with the presented techniques are given
A Monte Carlo based spent fuel analysis safeguards strategy assessment
Energy Technology Data Exchange (ETDEWEB)
Fensin, Michael L [Los Alamos National Laboratory; Tobin, Stephen J [Los Alamos National Laboratory; Swinhoe, Martyn T [Los Alamos National Laboratory; Menlove, Howard O [Los Alamos National Laboratory; Sandoval, Nathan P [Los Alamos National Laboratory
2009-01-01
assessment process, the techniques employed to automate the coupled facets of the assessment process, and the standard burnup/enrichment/cooling time dependent spent fuel assembly library. We also clearly define the diversion scenarios that will be analyzed during the standardized assessments. Though this study is currently limited to generic PWR assemblies, it is expected that the results of the assessment will yield an adequate spent fuel analysis strategy knowledge that will help the down-select process for other reactor types.
Falk, Carl F; Cai, Li
2016-06-01
We present a semi-parametric approach to estimating item response functions (IRF) useful when the true IRF does not strictly follow commonly used functions. Our approach replaces the linear predictor of the generalized partial credit model with a monotonic polynomial. The model includes the regular generalized partial credit model at the lowest order polynomial. Our approach extends Liang's (A semi-parametric approach to estimate IRFs, Unpublished doctoral dissertation, 2007) method for dichotomous item responses to the case of polytomous data. Furthermore, item parameter estimation is implemented with maximum marginal likelihood using the Bock-Aitkin EM algorithm, thereby facilitating multiple group analyses useful in operational settings. Our approach is demonstrated on both educational and psychological data. We present simulation results comparing our approach to more standard IRF estimation approaches and other non-parametric and semi-parametric alternatives.
Falk, Carl F; Cai, Li
2016-06-01
We present a semi-parametric approach to estimating item response functions (IRF) useful when the true IRF does not strictly follow commonly used functions. Our approach replaces the linear predictor of the generalized partial credit model with a monotonic polynomial. The model includes the regular generalized partial credit model at the lowest order polynomial. Our approach extends Liang's (A semi-parametric approach to estimate IRFs, Unpublished doctoral dissertation, 2007) method for dichotomous item responses to the case of polytomous data. Furthermore, item parameter estimation is implemented with maximum marginal likelihood using the Bock-Aitkin EM algorithm, thereby facilitating multiple group analyses useful in operational settings. Our approach is demonstrated on both educational and psychological data. We present simulation results comparing our approach to more standard IRF estimation approaches and other non-parametric and semi-parametric alternatives. PMID:25487423
Taheriyoun, Masoud; Moradinejad, Saber
2015-01-01
The reliability of a wastewater treatment plant is a critical issue when the effluent is reused or discharged to water resources. Main factors affecting the performance of the wastewater treatment plant are the variation of the influent, inherent variability in the treatment processes, deficiencies in design, mechanical equipment, and operational failures. Thus, meeting the established reuse/discharge criteria requires assessment of plant reliability. Among many techniques developed in system reliability analysis, fault tree analysis (FTA) is one of the popular and efficient methods. FTA is a top down, deductive failure analysis in which an undesired state of a system is analyzed. In this study, the problem of reliability was studied on Tehran West Town wastewater treatment plant. This plant is a conventional activated sludge process, and the effluent is reused in landscape irrigation. The fault tree diagram was established with the violation of allowable effluent BOD as the top event in the diagram, and the deficiencies of the system were identified based on the developed model. Some basic events are operator's mistake, physical damage, and design problems. The analytical method is minimal cut sets (based on numerical probability) and Monte Carlo simulation. Basic event probabilities were calculated according to available data and experts' opinions. The results showed that human factors, especially human error had a great effect on top event occurrence. The mechanical, climate, and sewer system factors were in subsequent tier. Literature shows applying FTA has been seldom used in the past wastewater treatment plant (WWTP) risk analysis studies. Thus, the developed FTA model in this study considerably improves the insight into causal failure analysis of a WWTP. It provides an efficient tool for WWTP operators and decision makers to achieve the standard limits in wastewater reuse and discharge to the environment.
Comparing statistical data to Monte Carlo simulation - parameter fitting and unfolding
International Nuclear Information System (INIS)
The author presents an introduction to the statistical analysis of experimental data by means of Monte Carlo simulations. After a description of the χ2 test of a hypothesis the least-square and maximum-likelihood fits to Monte Carlo distributions are described. Then unfolding is described. Finally confidence intervals are studied, and the computation of upper and lower limits is discussed from a Bayesian point of view. (HSI)
Number of iterations needed in Monte Carlo Simulation using reliability analysis for tunnel supports
Directory of Open Access Journals (Sweden)
E. Bukaçi
2016-06-01
Full Text Available There are many methods in geotechnical engineering which could take advantage of Monte Carlo Simulation to establish probability of failure, since closed form solutions are almost impossible to use in most cases. The problem that arises with using Monte Carlo Simulation is the number of iterations needed for a particular simulation.This article will show why it’s important to calculate number of iterations needed for Monte Carlo Simulation used in reliability analysis for tunnel supports using convergence – confinement method. Number if iterations needed will be calculated with two methods. In the first method, the analyst has to accept a distribution function for the performance function. The other method suggested by this article is to calculate number of iterations based on the convergence of the factor the analyst is interested in the calculation. Reliability analysis will be performed for the diversion tunnel in Rrëshen, Albania, by using both methods mentioned and results will be confronted
Directory of Open Access Journals (Sweden)
Xisheng Yu
2014-01-01
Full Text Available The paper by Liu (2010 introduces a method termed the canonical least-squares Monte Carlo (CLM which combines a martingale-constrained entropy model and a least-squares Monte Carlo algorithm to price American options. In this paper, we first provide the convergence results of CLM and numerically examine the convergence properties. Then, the comparative analysis is empirically conducted using a large sample of the S&P 100 Index (OEX puts and IBM puts. The results on the convergence show that choosing the shifted Legendre polynomials with four regressors is more appropriate considering the pricing accuracy and the computational cost. With this choice, CLM method is empirically demonstrated to be superior to the benchmark methods of binominal tree and finite difference with historical volatilities.
Monte Carlo Calculation for Landmine Detection using Prompt Gamma Neutron Activation Analysis
Energy Technology Data Exchange (ETDEWEB)
Park, Seungil; Kim, Seong Bong; Yoo, Suk Jae [Plasma Technology Research Center, Gunsan (Korea, Republic of); Shin, Sung Gyun; Cho, Moohyun [POSTECH, Pohang (Korea, Republic of); Han, Seunghoon; Lim, Byeongok [Samsung Thales, Yongin (Korea, Republic of)
2014-05-15
Identification and demining of landmines are a very important issue for the safety of the people and the economic development. To solve the issue, several methods have been proposed in the past. In Korea, National Fusion Research Institute (NFRI) is developing a landmine detector using prompt gamma neutron activation analysis (PGNAA) as a part of the complex sensor-based landmine detection system. In this paper, the Monte Carlo calculation results for this system are presented. Monte Carlo calculation was carried out for the design of the landmine detector using PGNAA. To consider the soil effect, average soil composition is analyzed and applied to the calculation. This results has been used to determine the specification of the landmine detector.
Lazio, T. Joseph W.; Cordes, James M.
1997-01-01
We use radio-wave scattering data to constrain the distribution of ionized gas in the outer Galaxy. Like previous models, our model for the H II disk includes parameters for the radial scale length and scale height of the H II, but we allow the H II disk to warp and flare. Our model also includes the Perseus arm. We use a likelihood analysis on 11 extragalactic sources and 7 pulsars. Scattering in the Perseus arm is no more than 60% of the level contributed by spiral arms in the inner Galaxy,...
West, Anthony C. F.; Novakowski, Kent S.; Gazor, Saeed
2006-06-01
We propose a new method to estimate the transmissivities of bedrock fractures from transmissivities measured in intervals of fixed length along a borehole. We define the scale of a fracture set by the inverse of the density of the Poisson point process assumed to represent their locations along the borehole wall, and we assume a lognormal distribution for their transmissivities. The parameters of the latter distribution are estimated by maximizing the likelihood of a left-censored subset of the data where the degree of censorship depends on the scale of the considered fracture set. We applied the method to sets of interval transmissivities simulated by summing random fracture transmissivities drawn from a specified population. We found the estimated distributions compared well to the transmissivity distributions of similarly scaled subsets of the most transmissive fractures from among the specified population. Estimation accuracy was most sensitive to the variance in the transmissivities of the fracture population. Using the proposed method, we estimated the transmissivities of fractures at increasing scale from hydraulic test data collected at a fixed scale in Smithville, Ontario, Canada. This is an important advancement since the resultant curves of transmissivity parameters versus fracture set scale would only previously have been obtainable from hydraulic tests conducted with increasing test interval length and with degrading equipment precision. Finally, on the basis of the properties of the proposed method, we propose guidelines for the design of fixed interval length hydraulic testing programs that require minimal prior knowledge of the rock.
International Nuclear Information System (INIS)
A theoretical model to describe the photopeak shape function has been developed by introducing an instrument function, which is a convolution of the statistical fluctuation of the charge carriers and the stochastic process of escape of the charge-carrier collection by capture at trapping centers. The photopeak shape function is a convolution of the instrument function and a Poisson probability-density functional representation of a reduced random summing event. The functions have been tested by using three coaxial, high-purity Ge detectors of a conventional type. The parameters were estimated by the maximum-likelihood estimation method. The position indicating the incident photon energy appeared at the centroid of the intrinsic normal distribution. The most probable peak-height position is no more than a ''conventional'' one, though it is commonly used in spectroscopy. The theory predicts the photopeak shape of many photons by folding an input function of the subject. The theory provides standards for the detector and the detection system. (orig.)
Directory of Open Access Journals (Sweden)
Guido W. Grimm
2006-01-01
Full Text Available The multi-copy internal transcribed spacer (ITS region of nuclear ribosomal DNA is widely used to infer phylogenetic relationships among closely related taxa. Here we use maximum likelihood (ML and splits graph analyses to extract phylogenetic information from ~ 600 mostly cloned ITS sequences, representing 81 species and subspecies of Acer, and both species of its sister Dipteronia. Additional analyses compared sequence motifs in Acer and several hundred Anacardiaceae, Burseraceae, Meliaceae, Rutaceae, and Sapindaceae ITS sequences in GenBank. We also assessed the effects of using smaller data sets of consensus sequences with ambiguity coding (accounting for within-species variation instead of the full (partly redundant original sequences. Neighbor-nets and bipartition networks were used to visualize conflict among character state patterns. Species clusters observed in the trees and networks largely agree with morphology-based classifications; of de Jong’s (1994 16 sections, nine are supported in neighbor-net and bipartition networks, and ten by sequence motifs and the ML tree; of his 19 series, 14 are supported in networks, motifs, and the ML tree. Most nodes had higher bootstrap support with matrices of 105 or 40 consensus sequences than with the original matrix. Within-taxon ITS divergence did not differ between diploid and polyploid Acer, and there was little evidence of differentiated parental ITS haplotypes, suggesting that concerted evolution in Acer acts rapidly.
Monte Carlo Model of TRIGA Reactor to Support Neutron Activation Analysis
Energy Technology Data Exchange (ETDEWEB)
Zerovnik, G.; Snoj, L.; Trkov, A. [Reactor Physics Department, Jozef Stefan Institute, Jamova cesta 39, SI-1000 Ljubljana (Slovenia)
2011-07-01
The TRIGA reactor at Jozef Stefan Institute is used as a neutron source for neutron activation analysis. The accuracy of the method depends on the accuracy of the neutron spectrum characterization. Therefore, computational models on different scales have been developed: Monte Carlo full reactor model, model of an irradiation channel and deterministic code for self-shielding factor calculations. The models have been validated by comparing against experiment and thus provide a very strong support for neutron activation analysis of samples irradiated at the TRIGA reactor. (author)
First Monte Carlo analysis of fragmentation functions from single-inclusive $e^+ e^-$ annihilation
Sato, N; Melnitchouk, W; Hirai, M; Kumano, S; Accardi, A
2016-01-01
We perform the first iterative Monte Carlo (IMC) analysis of fragmentation functions constrained by all available data from single-inclusive $e^+ e^-$ annihilation into pions and kaons. The IMC method eliminates potential bias in traditional analyses based on single fits introduced by fixing parameters not well contrained by the data and provides a statistically rigorous determination of uncertainties. Our analysis reveals specific features of fragmentation functions using the new IMC methodology and those obtained from previous analyses, especially for light quarks and for strange quark fragmentation to kaons.
Monte-Carlo Analysis of the Flavour Changing Neutral Current B \\to Gamma at Babar
Energy Technology Data Exchange (ETDEWEB)
Smith, D. [Imperial College, London (United Kingdom)
2001-09-01
The main theme of this thesis is a Monte-Carlo analysis of the rare Flavour Changing Neutral Current (FCNC) decay b→sγ. The analysis develops techniques that could be applied to real data, to discriminate between signal and background events in order to make a measurement of the branching ratio of this rare decay using the BaBar detector. Also included in this thesis is a description of the BaBar detector and the work I have undertaken in the development of the electronic data acquisition system for the Electromagnetic calorimeter (EMC), a subsystem of the BaBar detector.
Uncertainty optimization applied to the Monte Carlo analysis of planetary entry trajectories
Way, David Wesley
2001-10-01
Future robotic missions to Mars, as well as any human missions, will require precise entries to ensure safe landings near science objectives and pre-deployed assets. Planning for these missions will depend heavily on Monte Carlo analyses to evaluate active guidance algorithms, assess the impact of off-nominal conditions, and account for uncertainty. The dependability of Monte Carlo forecasts, however, is limited by the accuracy and completeness of the assumed uncertainties. This is because Monte Carlo analysis is a forward driven problem; beginning with the input uncertainties and proceeding to the forecast output statistics. An improvement to the Monte Carlo analysis is needed that will allow the problem to be worked in reverse. In this way, the largest allowable dispersions that achieve the required mission objectives can be determined quantitatively. This thesis proposes a methodology to optimize the uncertainties in the Monte Carlo analysis of spacecraft landing footprints. A metamodel is used to first write polynomial expressions for the size of the landing footprint as functions of the independent uncertainty extrema. The coefficients of the metamodel are determined by performing experiments. The metamodel is then used in a constrained optimization procedure to minimize a cost-tolerance function. First, a two-dimensional proof-of-concept problem was used to evaluate the feasibility of this optimization method. Next, the optimization method was further demonstrated on the Mars Surveyor Program 2001 Lander. The purpose of this example was to demonstrate that the methodology developed during the proof-of-concept could be scaled to solve larger, more complicated, "real world" problems. This research has shown that is possible to control the size of the landing footprint and establish tolerances for mission uncertainties. A simplified metamodel was developed, which is enabling for realistic problems with more than just a few uncertainties. A confidence interval on
On the likelihood function of Gaussian max-stable processes
Genton, M. G.
2011-05-24
We derive a closed form expression for the likelihood function of a Gaussian max-stable process indexed by ℝd at p≤d+1 sites, d≥1. We demonstrate the gain in efficiency in the maximum composite likelihood estimators of the covariance matrix from p=2 to p=3 sites in ℝ2 by means of a Monte Carlo simulation study. © 2011 Biometrika Trust.
Model Fit after Pairwise Maximum Likelihood.
Barendse, M T; Ligtvoet, R; Timmerman, M E; Oort, F J
2016-01-01
Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed. As maximizing the likelihood of multivariate response patterns is computationally very intensive, the sum of the log-likelihoods of the bivariate response patterns is maximized instead. Little is yet known about how to assess model fit when the analysis is based on such a pairwise maximum likelihood (PML) of two-way contingency tables. We propose new fit criteria for the PML method and conduct a simulation study to evaluate their performance in model selection. With large sample sizes (500 or more), PML performs as well the robust weighted least squares analysis of polychoric correlations. PMID:27148136
MKENO-DAR: a direct angular representation Monte Carlo code for criticality safety analysis
International Nuclear Information System (INIS)
Improving the Monte Carlo code MULTI-KENO, the MKENO-DAR (Direct Angular Representation) code has been developed for criticality safety analysis in detail. A function was added to MULTI-KENO for representing anisotropic scattering strictly. With this function, the scattering angle of neutron is determined not by the average scattering angle μ-bar of the Pl Legendre polynomial but by the random work operation using probability distribution function produced with the higher order Legendre polynomials. This code is avilable for the FACOM-M380 computer. This report is a computer code manual for MKENO-DAR. (author)
Microlens assembly error analysis for light field camera based on Monte Carlo method
Li, Sai; Yuan, Yuan; Zhang, Hao-Wei; Liu, Bin; Tan, He-Ping
2016-08-01
This paper describes numerical analysis of microlens assembly errors in light field cameras using the Monte Carlo method. Assuming that there were no manufacturing errors, home-built program was used to simulate images of coupling distance error, movement error and rotation error that could appear during microlens installation. By researching these images, sub-aperture images and refocus images, we found that the images present different degrees of fuzziness and deformation for different microlens assembly errors, while the subaperture image presents aliasing, obscured images and other distortions that result in unclear refocus images.
Markov chain Monte Carlo linkage analysis of a complex qualitative phenotype.
Hinrichs, A; Lin, J H; Reich, T; Bierut, L; Suarez, B K
1999-01-01
We tested a new computer program, LOKI, that implements a reversible jump Markov chain Monte Carlo (MCMC) technique for segregation and linkage analysis. Our objective was to determine whether this software, designed for use with continuously distributed phenotypes, has any efficacy when applied to the discrete disease states of the simulated data from the Mordor data from GAW Problem 1. Although we were able to identify the genomic location for two of the three quantitative trait loci by repeated application of the software, the MCMC sampler experienced significant mixing problems indicating that the method, as currently formulated in LOKI, was not suitable for the discrete phenotypes in this data set. PMID:10597502
Benchmark analysis of the TRIGA MARK II research reactor using Monte Carlo techniques
Energy Technology Data Exchange (ETDEWEB)
Huda, M.Q. E-mail: quamrul@dhaka.net; Rahman, M.; Sarker, M.M.; Bhuiyan, S.I
2004-07-01
This study deals with the neutronic analysis of the current core configuration of a 3-MW TRIGA MARK II research reactor at Atomic Energy Research Establishment (AERE), Savar, Dhaka, Bangladesh and validation of the results by benchmarking with the experimental, operational and available Final Safety Analysis Report (FSAR) values. The 3-D continuous-energy Monte Carlo code MCNP4C was used to develop a versatile and accurate full-core model of the TRIGA core. The model represents in detail all components of the core with literally no physical approximation. All fresh fuel and control elements as well as the vicinity of the core were precisely described. Continuous energy cross-section data from ENDF/B-VI and ENDF/B-V and S({alpha},{beta}) scattering functions from the ENDF/B-VI library were used. The consistency and accuracy of both the Monte Carlo simulation and neutron transport physics was established by benchmarking the TRIGA experiments. The effective multiplication factor, power distribution and peaking factors, neutron flux distribution, and reactivity experiments comprising control rod worths, critical rod height, excess reactivity and shutdown margin were used in the validation process. The MCNP predictions and the experimentally determined values are found to be in very good agreement, which indicates that the simulation of TRIGA reactor is treated adequately.
Use of Monte Carlo simulations for cultural heritage X-ray fluorescence analysis
Energy Technology Data Exchange (ETDEWEB)
Brunetti, Antonio, E-mail: brunetti@uniss.it [Polcoming Department, University of Sassari (Italy); Golosio, Bruno [Polcoming Department, University of Sassari (Italy); Schoonjans, Tom; Oliva, Piernicola [Chemical and Pharmaceutical Department, University of Sassari (Italy)
2015-06-01
The analytical study of Cultural Heritage objects often requires merely a qualitative determination of composition and manufacturing technology. However, sometimes a qualitative estimate is not sufficient, for example when dealing with multilayered metallic objects. Under such circumstances a quantitative estimate of the chemical contents of each layer is sometimes required in order to determine the technology that was used to produce the object. A quantitative analysis is often complicated by the surface state: roughness, corrosion, incrustations that remain even after restoration, due to efforts to preserve the patina. Furthermore, restorers will often add a protective layer on the surface. In all these cases standard quantitative methods such as the fundamental parameter based approaches are generally not applicable. An alternative approach is presented based on the use of Monte Carlo simulations for quantitative estimation. - Highlights: • We present an application of fast Monte Carlo codes for Cultural Heritage artifact analysis. • We show applications to complex multilayer structures. • The methods allow estimating both the composition and the thickness of multilayer, such as bronze with patina. • The performance in terms of accuracy and uncertainty is described for the bronze samples.
Benchmark analysis of the TRIGA MARK II research reactor using Monte Carlo techniques
International Nuclear Information System (INIS)
This study deals with the neutronic analysis of the current core configuration of a 3-MW TRIGA MARK II research reactor at Atomic Energy Research Establishment (AERE), Savar, Dhaka, Bangladesh and validation of the results by benchmarking with the experimental, operational and available Final Safety Analysis Report (FSAR) values. The 3-D continuous-energy Monte Carlo code MCNP4C was used to develop a versatile and accurate full-core model of the TRIGA core. The model represents in detail all components of the core with literally no physical approximation. All fresh fuel and control elements as well as the vicinity of the core were precisely described. Continuous energy cross-section data from ENDF/B-VI and ENDF/B-V and S(α,β) scattering functions from the ENDF/B-VI library were used. The consistency and accuracy of both the Monte Carlo simulation and neutron transport physics was established by benchmarking the TRIGA experiments. The effective multiplication factor, power distribution and peaking factors, neutron flux distribution, and reactivity experiments comprising control rod worths, critical rod height, excess reactivity and shutdown margin were used in the validation process. The MCNP predictions and the experimentally determined values are found to be in very good agreement, which indicates that the simulation of TRIGA reactor is treated adequately
Neutronic Analysis of the 3 MW TRIGA MARK II Research Reactor, Part I: Monte Carlo Simulation
International Nuclear Information System (INIS)
This study deals with the neutronic analysis of the current core configuration of a 3 MW TRIGA MARK II research reactor at Atomic Energy Research Establishment (AERE), Savar, Dhaka, Bangladesh and validation of the results by benchmarking with the experimental, operational and available Final Safety Analysis Report (FSAR) values. The three-dimensional continuous-energy Monte Carlo code MCNP4C was used to develop a versatile and accurate full-core model of the TRIGA core. The model represents in detail all components of the core with literally no physical approximation. All fresh fuel and control elements as well as the vicinity of the core were precisely described. Continuous energy cross-section data from ENDF/B-VI and S(α, β) scattering functions from the ENDF/B-V library were used. The validation of the model against benchmark experimental results is presented. The MCNP predictions and the experimentally determined values are found to be in very good agreement, which indicates that the Monte Carlo model is correctly simulating the TRIGA reactor. (author)
International Nuclear Information System (INIS)
An electromagnetic sampling calorimeter is under construction in IPNE Bucharest for the determination of the energy of cosmic ray muons in TeV range, consisting of lead (1 cm thick) absorber layer, alternating with scintillator (3 cm thick) layers. The possibility of the estimation of the energy of high-energy cosmic muons is scrutinized using simulations with GEANT code of the response of the detector (30 layers) to incident energies in the range 1-30 TeV. A Maximum Likelihood Method analysis is presented as a procedure to determine the muon energy, being applied to the detector response to the muons of discrete energy and to the muons distributed according to the cosmic ray spectrum. (author) 17 Figs., 2 Tabs., 15 Refs
International Nuclear Information System (INIS)
A positron emission mammography scanner is under development at our Laboratory. The tomograph has a rectangular geometry consisting of four banks of detector modules. For each detector, the system can measure the depth of interaction information inside the crystal. The rectangular geometry leads to irregular radial and angular sampling and spatially variant sensitivity that are different from conventional PET systems. Therefore, it is of importance to study the image properties of the reconstructions. We adapted the theoretical analysis that we had developed for conventional PET systems to the list mode likelihood reconstruction for this tomograph. The local impulse response and covariance of the reconstruction can be easily computed using FFT. These theoretical results are also used with computer observer models to compute the signal-to-noise ratio for lesion detection. The analysis reveals the spatially variant resolution and noise properties of the list mode likelihood reconstruction. The theoretical predictions are in good agreement with Monte Carlo results
Arendonk, van J.A.M.; Tier, B.; Bink, M.C.A.M.; Bovenhuis, H.
1998-01-01
REML for the estimation of location and variance of a single quantitative trait locus, together with polygenic and residual variance, is described for the analysis of a granddaughter design. The method is based on a mixed linear model that includes the allelic effects of the quantitative trait locus
Core-scale solute transport model selection using Monte Carlo analysis
Malama, Bwalya; James, Scott C
2013-01-01
Model applicability to core-scale solute transport is evaluated using breakthrough data from column experiments conducted with conservative tracers tritium (H-3) and sodium-22, and the retarding solute uranium-232. The three models considered are single-porosity, double-porosity with single-rate mobile-immobile mass-exchange, and the multirate model, which is a deterministic model that admits the statistics of a random mobile-immobile mass-exchange rate coefficient. The experiments were conducted on intact Culebra Dolomite core samples. Previously, data were analyzed using single- and double-porosity models although the Culebra Dolomite is known to possess multiple types and scales of porosity, and to exhibit multirate mobile-immobile-domain mass transfer characteristics at field scale. The data are reanalyzed here and null-space Monte Carlo analysis is used to facilitate objective model selection. Prediction (or residual) bias is adopted as a measure of the model structural error. The analysis clearly shows ...
A bottom collider vertex detector design, Monte-Carlo simulation and analysis package
Energy Technology Data Exchange (ETDEWEB)
Lebrun, P.
1990-10-01
A detailed simulation of the BCD vertex detector is underway. Specifications and global design issues are briefly reviewed. The BCD design based on double sided strip detector is described in more detail. The GEANT3-based Monte-Carlo program and the analysis package used to estimate detector performance are discussed in detail. The current status of the expected resolution and signal to noise ratio for the golden'' CP violating mode B{sub d} {yields} {pi}{sup +}{pi}{sup {minus}} is presented. These calculations have been done at FNAL energy ({radical}s = 2.0 TeV). Emphasis is placed on design issues, analysis techniques and related software rather than physics potentials. 20 refs., 46 figs.
Goal-oriented sensitivity analysis for lattice kinetic Monte Carlo simulations
Arampatzis, Georgios; Katsoulakis, Markos A.
2014-03-01
In this paper we propose a new class of coupling methods for the sensitivity analysis of high dimensional stochastic systems and in particular for lattice Kinetic Monte Carlo (KMC). Sensitivity analysis for stochastic systems is typically based on approximating continuous derivatives with respect to model parameters by the mean value of samples from a finite difference scheme. Instead of using independent samples the proposed algorithm reduces the variance of the estimator by developing a strongly correlated-"coupled"- stochastic process for both the perturbed and unperturbed stochastic processes, defined in a common state space. The novelty of our construction is that the new coupled process depends on the targeted observables, e.g., coverage, Hamiltonian, spatial correlations, surface roughness, etc., hence we refer to the proposed method as goal-oriented sensitivity analysis. In particular, the rates of the coupled Continuous Time Markov Chain are obtained as solutions to a goal-oriented optimization problem, depending on the observable of interest, by considering the minimization functional of the corresponding variance. We show that this functional can be used as a diagnostic tool for the design and evaluation of different classes of couplings. Furthermore, the resulting KMC sensitivity algorithm has an easy implementation that is based on the Bortz-Kalos-Lebowitz algorithm's philosophy, where events are divided in classes depending on level sets of the observable of interest. Finally, we demonstrate in several examples including adsorption, desorption, and diffusion Kinetic Monte Carlo that for the same confidence interval and observable, the proposed goal-oriented algorithm can be two orders of magnitude faster than existing coupling algorithms for spatial KMC such as the Common Random Number approach. We also provide a complete implementation of the proposed sensitivity analysis algorithms, including various spatial KMC examples, in a supplementary MATLAB
Monte Carlo simulation for slip rate sensitivity analysis in Cimandiri fault area
International Nuclear Information System (INIS)
Slip rate is used to estimate earthquake recurrence relationship which is the most influence for hazard level. We examine slip rate contribution of Peak Ground Acceleration (PGA), in probabilistic seismic hazard maps (10% probability of exceedance in 50 years or 500 years return period). Hazard curve of PGA have been investigated for Sukabumi using a PSHA (Probabilistic Seismic Hazard Analysis). We observe that the most influence in the hazard estimate is crustal fault. Monte Carlo approach has been developed to assess the sensitivity. Then, Monte Carlo simulations properties have been assessed. Uncertainty and coefficient of variation from slip rate for Cimandiri Fault area has been calculated. We observe that seismic hazard estimates is sensitive to fault slip rate with seismic hazard uncertainty result about 0.25 g. For specific site, we found seismic hazard estimate for Sukabumi is between 0.4904 – 0.8465 g with uncertainty between 0.0847 – 0.2389 g and COV between 17.7% – 29.8%
Monte Carlo simulation for slip rate sensitivity analysis in Cimandiri fault area
Energy Technology Data Exchange (ETDEWEB)
Pratama, Cecep, E-mail: great.pratama@gmail.com [Graduate Program of Earth Science, Faculty of Earth Science and Technology, ITB, JalanGanesa no. 10, Bandung 40132 (Indonesia); Meilano, Irwan [Geodesy Research Division, Faculty of Earth Science and Technology, ITB, JalanGanesa no. 10, Bandung 40132 (Indonesia); Nugraha, Andri Dian [Global Geophysical Group, Faculty of Mining and Petroleum Engineering, ITB, JalanGanesa no. 10, Bandung 40132 (Indonesia)
2015-04-24
Slip rate is used to estimate earthquake recurrence relationship which is the most influence for hazard level. We examine slip rate contribution of Peak Ground Acceleration (PGA), in probabilistic seismic hazard maps (10% probability of exceedance in 50 years or 500 years return period). Hazard curve of PGA have been investigated for Sukabumi using a PSHA (Probabilistic Seismic Hazard Analysis). We observe that the most influence in the hazard estimate is crustal fault. Monte Carlo approach has been developed to assess the sensitivity. Then, Monte Carlo simulations properties have been assessed. Uncertainty and coefficient of variation from slip rate for Cimandiri Fault area has been calculated. We observe that seismic hazard estimates is sensitive to fault slip rate with seismic hazard uncertainty result about 0.25 g. For specific site, we found seismic hazard estimate for Sukabumi is between 0.4904 – 0.8465 g with uncertainty between 0.0847 – 0.2389 g and COV between 17.7% – 29.8%.
Slob W; Hendriksen CFM
1989-01-01
Dit rapport bespreekt de analyse van verdunningsreeksen met maximum likelihood, met als toepassing het in vitro serologisch toetsen van de werkzaamheid van bacteriele vaccins voor de mens. Met computersimulaties wordt aangetoond dat de maximum likelihood methode adequaat is voor de in het werkzaamh
Maximum-Likelihood Approach to Topological Charge Fluctuations in Lattice Gauge Theory
Brower, R C; Fleming, G T; Lin, M F; Neil, E T; Osborn, J C; Rebbi, C; Rinaldi, E; Schaich, D; Schroeder, C; Voronov, G; Vranas, P; Weinberg, E; Witzel, O
2014-01-01
We present a novel technique for the determination of the topological susceptibility (related to the variance of the distribution of global topological charge) from lattice gauge theory simulations, based on maximum-likelihood analysis of the Markov-chain Monte Carlo time series. This technique is expected to be particularly useful in situations where relatively few tunneling events are observed. Restriction to a lattice subvolume on which topological charge is not quantized is explored, and may lead to further improvement when the global topology is poorly sampled. We test our proposed method on a set of lattice data, and compare it to traditional methods.
Refined Monte Carlo analysis of the H.B. Robinson-2 reactor pressure vessel dosimetry benchmark
International Nuclear Information System (INIS)
Highlights: → Activation of in- and ex-vessel radiometric dosimeters is studied with MCNPX. → Influences of neutron source definition and cross-section libraries are examined. → 237Np(n,f) energy cut-off is set at 10 eV to cover the reaction completely. → Different methods for deriving activities from reaction rates are compared. → Uncertainties are evaluated and are below 10%, final C/E ratios being within 15%. - Abstract: Refined analysis, based on use of the Monte Carlo code MCNPX-2.4.0, is presented for the 'H.B. Robinson-2 pressure vessel dosimetry benchmark', which is a part of the Radiation Shielding and Dosimetry Experiments Database (SINBAD). First, the performance of the Monte Carlo methodology is reassessed relative to the reported deterministic results obtained with DORT. Thereby, the analysis is accompanied by a quantitative evaluation of the optimal energy cut-off value for each of the in- and ex-vessel dosimeters that were employed. Second, a more realistic definition of the neutron source is implemented than proposed in the benchmark. Thus, the current procedure for power-to-neutron-source-strength conversion, as also for explicitly considering the burnup-dependent fuel assembly-wise average fission neutron spectrum, is found to affect the calculated values significantly. In addition to the modelling refinements made, different approaches are tested for deriving the dosimeter activities, such that the neutron source time-evolution and the activity decay can be taken into account more accurately. Finally, in order to achieve a certain assessment of uncertainties, several sensitivity studies are carried out, e.g. with respect to the nuclear data used for the dosimeters, as also to the assumed physical location of the dosimeters. In spite of some apparent degradation in the prediction of experimental results when refining the Monte Carlo modelling, the final calculation/experiment (C/E) ratios for the measured dosimeter activities remain
Dubecký, Matúš; Jurečka, Petr; Mitas, Lubos; Hobza, Pavel; Otyepka, Michal
2014-01-01
Reliable theoretical predictions of noncovalent interaction energies, which are important e.g. in drug-design and hydrogen-storage applications, belong to longstanding challenges of contemporary quantum chemistry. In this respect, the fixed-node diffusion Monte Carlo (FN-DMC) is a promising alternative to the commonly used ``gold standard'' coupled-cluster CCSD(T)/CBS method for its benchmark accuracy and favourable scaling, in contrast to other correlated wave function approaches. This work is focused on the analysis of protocols and possible tradeoffs for FN-DMC estimations of noncovalent interaction energies and proposes a significantly more efficient yet accurate computational protocol using simplified explicit correlation terms. Its performance is illustrated on a number of weakly bound complexes, including water dimer, benzene/hydrogen, T-shape benzene dimer and stacked adenine-thymine DNA base pair complex. The proposed protocol achieves excellent agreement ($\\sim$0.2 kcal/mol) with respect to the reli...
Ligand-receptor binding kinetics in surface plasmon resonance cells: A Monte Carlo analysis
Carroll, Jacob; Forsten-Williams, Kimberly; Täuber, Uwe C
2016-01-01
Surface plasmon resonance (SPR) chips are widely used to measure association and dissociation rates for the binding kinetics between two species of chemicals, e.g., cell receptors and ligands. It is commonly assumed that ligands are spatially well mixed in the SPR region, and hence a mean-field rate equation description is appropriate. This approximation however ignores the spatial fluctuations as well as temporal correlations induced by multiple local rebinding events, which become prominent for slow diffusion rates and high binding affinities. We report detailed Monte Carlo simulations of ligand binding kinetics in an SPR cell subject to laminar flow. We extract the binding and dissociation rates by means of the techniques frequently employed in experimental analysis that are motivated by the mean-field approximation. We find major discrepancies in a wide parameter regime between the thus extracted rates and the known input simulation values. These results underscore the crucial quantitative importance of s...
Outlier detection in near-infrared spectroscopic analysis by using Monte Carlo cross-validation
Institute of Scientific and Technical Information of China (English)
LIU ZhiChao; CAI WenSheng; SHAO XueGuang
2008-01-01
An outlier detection method is proposed for near-infrared spectral analysis. The underlying philosophy of the method is that, in random test (Monte Carlo) cross-validation, the probability of outliers pre-senting in good models with smaller prediction residual error sum of squares (PRESS) or in bad mod-els with larger PRESS should be obviously different from normal samples. The method builds a large number of PLS models by using random test cross-validation at first, then the models are sorted by the PRESS, and at last the outliers are recognized according to the accumulative probability of each sam-ple in the sorted models. For validation of the proposed method, four data sets, including three pub-lished data sets and a large data set of tobacco lamina, were investigated. The proposed method was proved to be highly efficient and veracious compared with the conventional leave-one-out (LOO) cross validation method.
Nuclear reactor transient analysis via a quasi-static kinetics Monte Carlo method
Jo, YuGwon; Cho, Bumhee; Cho, Nam Zin
2015-12-01
The predictor-corrector quasi-static (PCQS) method is applied to the Monte Carlo (MC) calculation for reactor transient analysis. To solve the transient fixed-source problem of the PCQS method, fission source iteration is used and a linear approximation of fission source distributions during a macro-time step is introduced to provide delayed neutron source. The conventional particle-tracking procedure is modified to solve the transient fixed-source problem via MC calculation. The PCQS method with MC calculation is compared with the direct time-dependent method of characteristics (MOC) on a TWIGL two-group problem for verification of the computer code. Then, the results on a continuous-energy problem are presented.
Monte Carlo analysis of doppler reactivity coefficient for UO2 pin cell geometry
International Nuclear Information System (INIS)
Monte Carlo analysis has been performed to investigate the impact of the exact resonance elastic scattering model on the Doppler reactivity coefficient for the UO2 pin cell geometry with the parabolic temperature profile. As a result, the exact scattering model affects the coefficient similarly for both the flat and parabolic temperature profiles; it increases the contribution of uranium-238 resonance capture in the energy region from ∼16 eV to ∼150 eV and does uniformly in the radial direction. Then the following conclusions hold for both the exact and asymptotic resonance scattering models. The Doppler reactivity coefficient is well reproduced with the definition of the effective fuel temperature (equivalent flat temperature) proposed by Grandi et al. In addition, the effective fuel temperature volume-averaged over the entire fuel region negatively overestimates the reference Doppler reactivity coefficient but the calculated one can be significantly improved by dividing the fuel region into a few equi-volumes. (author)
Williams, Jessica L.; Bhat, Ramachandra S.; You, Tung-Han
2012-01-01
The Soil Moisture Active Passive (SMAP) mission will perform soil moisture content and freeze/thaw state observations from a low-Earth orbit. The observatory is scheduled to launch in October 2014 and will perform observations from a near-polar, frozen, and sun-synchronous Science Orbit for a 3-year data collection mission. At launch, the observatory is delivered to an Injection Orbit that is biased below the Science Orbit; the spacecraft will maneuver to the Science Orbit during the mission Commissioning Phase. The delta V needed to maneuver from the Injection Orbit to the Science Orbit is computed statistically via a Monte Carlo simulation; the 99th percentile delta V (delta V99) is carried as a line item in the mission delta V budget. This paper details the simulation and analysis performed to compute this figure and the delta V99 computed per current mission parameters.
2D Monte Carlo analysis of radiological risk assessment for the food intake in Korea
International Nuclear Information System (INIS)
Most public health risk assessments assume and combine a series of average, conservative and worst-case values to derive an acceptable point estimate of risk. To improve quality of risk information, insight of uncertainty in the assessments is needed and more emphasis is put on the probabilistic risk assessment. Probabilistic risk assessment studies use probability distributions for one or more variables of the risk equation in order to quantitatively characterize variability and uncertainty. In this study, an advanced technique called the two-dimensional Monte Carlo analysis (2D MCA) is applied to estimation of internal doses from intake of radionuclides in foodstuffs and drinking water in Korea. The variables of the risk model along with the parameters of these variables are described in terms of probability density functions (PDFs). In addition, sensitivity analyses were performed to identify important factors to the radiation doses. (author)
Energy Technology Data Exchange (ETDEWEB)
Davis JE, Eddy MJ, Sutton TM, Altomari TJ
2007-03-01
Solid modeling computer software systems provide for the design of three-dimensional solid models used in the design and analysis of physical components. The current state-of-the-art in solid modeling representation uses a boundary representation format in which geometry and topology are used to form three-dimensional boundaries of the solid. The geometry representation used in these systems is cubic B-spline curves and surfaces--a network of cubic B-spline functions in three-dimensional Cartesian coordinate space. Many Monte Carlo codes, however, use a geometry representation in which geometry units are specified by intersections and unions of half-spaces. This paper describes an algorithm for converting from a boundary representation to a half-space representation.
Contrast to Noise Ratio and Contrast Detail Analysis in Mammography:A Monte Carlo Study
Metaxas, V.; Delis, H.; Kalogeropoulou, C.; Zampakis, P.; Panayiotakis, G.
2015-09-01
The mammographic spectrum is one of the major factors affecting image quality in mammography. In this study, a Monte Carlo (MC) simulation model was used to evaluate image quality characteristics of various mammographic spectra. The anode/filter combinations evaluated, were those traditionally used in mammography, for tube voltages between 26 and 30 kVp. The imaging performance was investigated in terms of Contrast to Noise Ratio (CNR) and Contrast Detail (CD) analysis, by involving human observers, utilizing a mathematical CD phantom. Soft spectra provided the best characteristics in terms of both CNR and CD scores, while tube voltage had a limited effect. W-anode spectra filtered with k-edge filters demonstrated an improved performance, that sometimes was better compared to softer x-ray spectra, produced by Mo or Rh anode. Regarding the filter material, k-edge filters showed superior performance compared to Al filters.
Outlier detection in near-infrared spectroscopic analysis by using Monte Carlo cross-validation
Institute of Scientific and Technical Information of China (English)
2008-01-01
An outlier detection method is proposed for near-infrared spectral analysis. The underlying philosophy of the method is that,in random test(Monte Carlo) cross-validation,the probability of outliers presenting in good models with smaller prediction residual error sum of squares(PRESS) or in bad models with larger PRESS should be obviously different from normal samples. The method builds a large number of PLS models by using random test cross-validation at first,then the models are sorted by the PRESS,and at last the outliers are recognized according to the accumulative probability of each sample in the sorted models. For validation of the proposed method,four data sets,including three published data sets and a large data set of tobacco lamina,were investigated. The proposed method was proved to be highly efficient and veracious compared with the conventional leave-one-out(LOO) cross validation method.
Quasi-Monte Carlo Simulation-Based SFEM for Slope Reliability Analysis
Institute of Scientific and Technical Information of China (English)
Yu Yuzhen; Xie Liquan; Zhang Bingyin
2005-01-01
Considering the stochastic spatial variation of geotechnical parameters over the slope, a Stochastic Finite Element Method (SFEM) is established based on the combination of the Shear Strength Reduction (SSR) concept and quasi-Monte Carlo simulation. The shear strength reduction FEM is superior to the slice method based on the limit equilibrium theory in many ways, so it will be more powerful to assess the reliability of global slope stability when combined with probability theory. To illustrate the performance of the proposed method, it is applied to an example of simple slope. The results of simulation show that the proposed method is effective to perform the reliability analysis of global slope stability without presupposing a potential slip surface.
Enrichment effects on CANDU-SEU spent fuel Monte Carlo shielding analysis
International Nuclear Information System (INIS)
Shielding analyses are an essential component of the nuclear safety, the estimations of radiation doses in order to reduce them under specified limitation values being the main task here. According to IAEA data, more than 10 millions packages containing radioactive materials are annually transported world wide. All the problems arisen from the safe radioactive materials transport assurance must be carefully settled. Last decade, both for operating reactors and future reactor projects, a general trend to raise the discharge fuel burnup has been recorded world wide. For CANDU type reactors, the most attractive solution seems to be SEU and RU fuels utilization. The basic tasks accomplished by the shielding calculations in a nuclear safety analysis consist in dose rates calculation, to prevent any risks both for personnel protection and impact on the environment during the spent fuel manipulation, transport and storage. The paper aims to study the effects induced by fuel enrichment variation on CANDU-SEU spent fuel photon dose rates for a Monte Carlo shielding analysis applied to spent fuel transport after a defined cooling period in the NPP pools. The fuel bundles projects considered here have 43 Zircaloy rods, filled with SEU fuel pellets, the fuel having different enrichment in U-235. All the geometrical and material data related on the cask were considered according to the shipping cask type B model. After a photon source profile calculation by using ORIGEN-S code, in order to perform the shielding calculations, Monte Carlo MORSE-SGC code has been used, both codes being included in the ORNL's SCALE 5 system. The photon dose rates to the shipping cask wall and in air, at different distances from the cask, have been estimated. Finally, a photon dose rates comparison for different fuel enrichments has been performed. (author)
Augmented Likelihood Image Reconstruction.
Stille, Maik; Kleine, Matthias; Hägele, Julian; Barkhausen, Jörg; Buzug, Thorsten M
2016-01-01
The presence of high-density objects remains an open problem in medical CT imaging. Data of projections passing through objects of high density, such as metal implants, are dominated by noise and are highly affected by beam hardening and scatter. Reconstructed images become less diagnostically conclusive because of pronounced artifacts that manifest as dark and bright streaks. A new reconstruction algorithm is proposed with the aim to reduce these artifacts by incorporating information about shape and known attenuation coefficients of a metal implant. Image reconstruction is considered as a variational optimization problem. The afore-mentioned prior knowledge is introduced in terms of equality constraints. An augmented Lagrangian approach is adapted in order to minimize the associated log-likelihood function for transmission CT. During iterations, temporally appearing artifacts are reduced with a bilateral filter and new projection values are calculated, which are used later on for the reconstruction. A detailed evaluation in cooperation with radiologists is performed on software and hardware phantoms, as well as on clinically relevant patient data of subjects with various metal implants. Results show that the proposed reconstruction algorithm is able to outperform contemporary metal artifact reduction methods such as normalized metal artifact reduction.
Institute of Scientific and Technical Information of China (English)
Yan-Yan Wei; Ji-Jun Wang; Chao Yan; Zi-Qiang Li; Xiao Pan; Yi Cui; Tong Su
2016-01-01
Background:Several studies using functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) have indicated that cognitive remediation therapy (CRT) might improve cognitive function by changing brain activations in patients with schizophrenia.However,the results were not consistent in these changed brain areas in different studies.The present activation likelihood estimation (ALE) meta-analysis was conducted to investigate whether cognitive function change was accompanied by the brain activation changes,and where the main areas most related to these changes were in schizophrenia patients after CRT.Analyses of whole-brain studies and whole-brain + region of interest (ROI) studies were compared to explore the effect of the different methodologies on the results.Methods:A computerized systematic search was conducted to collect fMRI and PET studies on brain activation changes in schizophrenia patients from pre-to post-CRT.Nine studies using fMRI techniques were included in the meta-analysis.Ginger ALE 2.3.1 was used to perform meta-analysis across these imaging studies.Results:The main areas with increased brain activation were in frontal and parietal lobe,including left medial frontal gyrus,left inferior frontal gyrus,right middle frontal gyrus,right postcentral gyrus,and inferior parietal lobule in patients after CRT,yet no decreased brain activation was found.Although similar increased activation brain areas were identified in ALE with or without ROI studies,analysis including ROI studies had a higher ALE value.Conclusions:The current findings suggest that CRT might improve the cognition of schizophrenia patients by increasing activations of the frontal and parietal lobe.In addition,it might provide more evidence to confirm results by including ROI studies in ALE meta-analysis.
Monte Carlo Few-Group Constant Generation for CANDU 6 Core Analysis
Directory of Open Access Journals (Sweden)
Seung Yeol Yoo
2015-01-01
Full Text Available The current neutronics design methodology of CANDU-PHWRs based on the two-step calculations requires determining not only homogenized two-group constants for ordinary fuel bundle lattice cells by the WIMS-AECL lattice cell code but also incremental two-group constants arising from the penetration of control devices into the fuel bundle cells by a supercell analysis code like MULTICELL or DRAGON. As an alternative way to generate the two-group constants necessary for the CANDU-PHWR core analysis, this paper proposes utilizing a B1 theory augmented Monte Carlo (MC few-group constant generation method (B1 MC method which has been devised for the PWR fuel assembly analysis method. To examine the applicability of the B1 MC method for the CANDU 6 core analysis, the fuel bundle cell and supercell calculations are performed using it to obtain the two-group constants. By showing that the two-group constants from the B1 MC method agree well with those from WIMS-AECL and that core neutronics calculations for hypothetical CANDU 6 cores by a deterministic diffusion theory code SCAN with B1 MC method generated two-group constants also agree well with whole core MC analyses, it is concluded that the B1 MC method is well qualified for both fuel bundle cell and supercell analyses.
Improving Markov Chain Monte Carlo algorithms in LISA Pathfinder Data Analysis
Karnesis, N.; Nofrarias, M.; Sopuerta, C. F.; Lobo, A.
2012-06-01
The LISA Pathfinder mission (LPF) aims to test key technologies for the future LISA mission. The LISA Technology Package (LTP) on-board LPF will consist of an exhaustive suite of experiments and its outcome will be crucial for the future detection of gravitational waves. In order to achieve maximum sensitivity, we need to have an understanding of every instrument on-board and parametrize the properties of the underlying noise models. The Data Analysis team has developed algorithms for parameter estimation of the system. A very promising one implemented for LISA Pathfinder data analysis is the Markov Chain Monte Carlo. A series of experiments are going to take place during flight operations and each experiment is going to provide us with essential information for the next in the sequence. Therefore, it is a priority to optimize and improve our tools available for data analysis during the mission. Using a Bayesian framework analysis allows us to apply prior knowledge for each experiment, which means that we can efficiently use our prior estimates for the parameters, making the method more accurate and significantly faster. This, together with other algorithm improvements, will lead us to our main goal, which is no other than creating a robust and reliable tool for parameter estimation during the LPF mission.
Empirical likelihood estimation of discretely sampled processes of OU type
Institute of Scientific and Technical Information of China (English)
2009-01-01
This paper presents an empirical likelihood estimation procedure for parameters of the discretely sampled process of Ornstein-Uhlenbeck type. The proposed procedure is based on the condi- tional characteristic function, and the maximum empirical likelihood estimator is proved to be consistent and asymptotically normal. Moreover, this estimator is shown to be asymptotically efficient under some mild conditions. When the background driving Lévy process is of type A or B, we show that the intensity parameter can be exactly recovered, and we study the maximum empirical likelihood estimator with the plug-in estimated intensity parameter. Testing procedures based on the empirical likelihood ratio statistic are developed for parameters and for estimating equations, respectively. Finally, Monte Carlo simulations are conducted to demonstrate the performance of proposed estimators.
Slob W; Hendriksen CFM
1989-01-01
Dit rapport bespreekt de analyse van verdunningsreeksen met maximum likelihood, met als toepassing het in vitro serologisch toetsen van de werkzaamheid van bacteriele vaccins voor de mens. Met computersimulaties wordt aangetoond dat de maximum likelihood methode adequaat is voor de in het werkzaamheidsonderzoek gebruikelijke steekproefomvang. De relatie tussen de antitoxine respons en vaccinverdunning wordt goed beschreven met een rechte lijn op dubbele log-schaal binnen de gebruikelijke expe...
Institute of Scientific and Technical Information of China (English)
ZHANG Jun; GUO Fan
2015-01-01
Tooth modification technique is widely used in gear industry to improve the meshing performance of gearings. However, few of the present studies on tooth modification considers the influence of inevitable random errors on gear modification effects. In order to investigate the uncertainties of tooth modification amount variations on system’s dynamic behaviors of a helical planetary gears, an analytical dynamic model including tooth modification parameters is proposed to carry out a deterministic analysis on the dynamics of a helical planetary gear. The dynamic meshing forces as well as the dynamic transmission errors of the sun-planet 1 gear pair with and without tooth modifications are computed and compared to show the effectiveness of tooth modifications on gear dynamics enhancement. By using response surface method, a fitted regression model for the dynamic transmission error(DTE) fluctuations is established to quantify the relationship between modification amounts and DTE fluctuations. By shifting the inevitable random errors arousing from manufacturing and installing process to tooth modification amount variations, a statistical tooth modification model is developed and a methodology combining Monte Carlo simulation and response surface method is presented for uncertainty analysis of tooth modifications. The uncertainly analysis reveals that the system’s dynamic behaviors do not obey the normal distribution rule even though the design variables are normally distributed. In addition, a deterministic modification amount will not definitely achieve an optimal result for both static and dynamic transmission error fluctuation reduction simultaneously.
Core-scale solute transport model selection using Monte Carlo analysis
Malama, Bwalya; Kuhlman, Kristopher L.; James, Scott C.
2013-06-01
Model applicability to core-scale solute transport is evaluated using breakthrough data from column experiments conducted with conservative tracers tritium (3H) and sodium-22 (22Na ), and the retarding solute uranium-232 (232U). The three models considered are single-porosity, double-porosity with single-rate mobile-immobile mass-exchange, and the multirate model, which is a deterministic model that admits the statistics of a random mobile-immobile mass-exchange rate coefficient. The experiments were conducted on intact Culebra Dolomite core samples. Previously, data were analyzed using single-porosity and double-porosity models although the Culebra Dolomite is known to possess multiple types and scales of porosity, and to exhibit multirate mobile-immobile-domain mass transfer characteristics at field scale. The data are reanalyzed here and null-space Monte Carlo analysis is used to facilitate objective model selection. Prediction (or residual) bias is adopted as a measure of the model structural error. The analysis clearly shows single-porosity and double-porosity models are structurally deficient, yielding late-time residual bias that grows with time. On the other hand, the multirate model yields unbiased predictions consistent with the late-time -5/2 slope diagnostic of multirate mass transfer. The analysis indicates the multirate model is better suited to describing core-scale solute breakthrough in the Culebra Dolomite than the other two models.
Likelihood devices in spatial statistics
Zwet, E.W. van
2001-01-01
One of the main themes of this thesis is the application to spatial data of modern semi- and nonparametric methods. Another, closely related theme is maximum likelihood estimation from spatial data. Maximum likelihood estimation is not common practice in spatial statistics. The method of moments and
Monte Carlo analysis of thermochromatography as a fast separation method for nuclear forensics
International Nuclear Information System (INIS)
Nuclear forensic science has become increasingly important for global nuclear security, and enhancing the timeliness of forensic analysis has been established as an important objective in the field. New, faster techniques must be developed to meet this objective. Current approaches for the analysis of minor actinides, fission products, and fuel-specific materials require time-consuming chemical separation coupled with measurement through either nuclear counting or mass spectrometry. These very sensitive measurement techniques can be hindered by impurities or incomplete separation in even the most painstaking chemical separations. High-temperature gas-phase separation or thermochromatography has been used in the past for the rapid separations in the study of newly created elements and as a basis for chemical classification of that element. This work examines the potential for rapid separation of gaseous species to be applied in nuclear forensic investigations. Monte Carlo modeling has been used to evaluate the potential utility of the thermochromatographic separation method, albeit this assessment is necessarily limited due to the lack of available experimental data for validation.
Monte Carlo analysis of thermochromatography as a fast separation method for nuclear forensics
International Nuclear Information System (INIS)
Nuclear forensic science has become increasingly important for global nuclear security, and enhancing the timeliness of forensic analysis has been established as an important objective in the field. New, faster techniques must be developed to meet this objective. Current approaches for the analysis of minor actinides, fission products, and fuel-specific materials require time-consuming chemical separation coupled with measurement through either nuclear counting or mass spectrometry. These very sensitive measurement techniques can be hindered by impurities or incomplete separation in even the most painstaking chemical separations. High-temperature gas-phase separation or thermochromatography has been used in the past for the rapid separations in the study of newly created elements and as a basis for chemical classification of that element. This work examines the potential for rapid separation of gaseous species to be applied in nuclear forensic investigations. Monte Carlo modeling has been used to evaluate the potential utility of the thermochromatographic separation method, albeit this assessment is necessarily limited due to the lack of available experimental data for validation. (author)
Criticality qualification of a new Monte Carlo code for reactor core analysis
Energy Technology Data Exchange (ETDEWEB)
Catsaros, N. [Institute of Nuclear Technology - Radiation Protection, NCSR ' DEMOKRITOS' , P.O. Box 60228, 15310 Aghia Paraskevi (Greece); Gaveau, B. [MAPS, Universite Paris VI, 4 Place Jussieu, 75005 Paris (France); Jaekel, M. [Laboratoire de Physique Theorique, Ecole Normale Superieure, 24 rue Lhomond, 75231 Paris (France); Maillard, J. [MAPS, Universite Paris VI, 4 Place Jussieu, 75005 Paris (France); CNRS-IDRIS, Bt 506, BP167, 91403 Orsay (France); CNRS-IN2P3, 3 rue Michel Ange, 75794 Paris (France); Maurel, G. [Faculte de Medecine, Universite Paris VI, 27 rue de Chaligny, 75012 Paris (France); MAPS, Universite Paris VI, 4 Place Jussieu, 75005 Paris (France); Savva, P., E-mail: savvapan@ipta.demokritos.g [Institute of Nuclear Technology - Radiation Protection, NCSR ' DEMOKRITOS' , P.O. Box 60228, 15310 Aghia Paraskevi (Greece); Silva, J. [MAPS, Universite Paris VI, 4 Place Jussieu, 75005 Paris (France); Varvayanni, M.; Zisis, Th. [Institute of Nuclear Technology - Radiation Protection, NCSR ' DEMOKRITOS' , P.O. Box 60228, 15310 Aghia Paraskevi (Greece)
2009-11-15
In order to accurately simulate Accelerator Driven Systems (ADS), the utilization of at least two computational tools is necessary (the thermal-hydraulic problem is not considered in the frame of this work), namely: (a) A High Energy Physics (HEP) code system dealing with the 'Accelerator part' of the installation, i.e. the computation of the spectrum, intensity and spatial distribution of the neutrons source created by (p, n) reactions of a proton beam on a target and (b) a neutronics code system, handling the 'Reactor part' of the installation, i.e. criticality calculations, neutron transport, fuel burn-up and fission products evolution. In the present work, a single computational tool, aiming to analyze an ADS in its integrity and also able to perform core analysis for a conventional fission reactor, is proposed. The code is based on the well qualified HEP code GEANT (version 3), transformed to perform criticality calculations. The performance of the code is tested against two qualified neutronics code systems, the diffusion/transport SCALE-CITATION code system and the Monte Carlo TRIPOLI code, in the case of a research reactor core analysis. A satisfactory agreement was exhibited by the three codes.
Directory of Open Access Journals (Sweden)
Cosimo eUrgesi
2014-05-01
Full Text Available Several neurophysiologic and neuroimaging studies suggested that motor and perceptual systems are tightly linked along a continuum rather than providing segregated mechanisms supporting different functions. Using correlational approaches, these studies demonstrated that action observation activates not only visual but also motor brain regions. On the other hand, brain stimulation and brain lesion evidence allows tackling the critical question of whether our action representations are necessary to perceive and understand others’ actions. In particular, recent neuropsychological studies have shown that patients with temporal, parietal and frontal lesions exhibit a number of possible deficits in the visual perception and the understanding of others’ actions. The specific anatomical substrates of such neuropsychological deficits however are still a matter of debate. Here we review the existing literature on this issue and perform an anatomic likelihood estimation meta-analysis of studies using lesion-symptom mapping methods on the causal relation between brain lesions and non-linguistic action perception and understanding deficits. The meta-analysis encompassed data from 361 patients tested in 11 studies and identified regions in the inferior frontal cortex, the inferior parietal cortex and the middle/superior temporal cortex, whose damage is consistently associated with poor performance in action perception and understanding tasks across studies. Interestingly, these areas correspond to the three nodes of the action observation network that are strongly activated in response to visual action perception in neuroimaging research and that have been targeted in previous brain stimulation studies. Thus, brain lesion mapping research provides converging causal evidence that premotor, parietal and temporal regions play a crucial role in action recognition and understanding.
Derivation of landslide-triggering thresholds by Monte Carlo simulation and ROC analysis
Peres, David Johnny; Cancelliere, Antonino
2015-04-01
Rainfall thresholds of landslide-triggering are useful in early warning systems to be implemented in prone areas. Direct statistical analysis of historical records of rainfall and landslide data presents different shortcomings typically due to incompleteness of landslide historical archives, imprecise knowledge of the triggering instants, unavailability of a rain gauge located near the landslides, etc. In this work, a Monte Carlo approach to derive and evaluate landslide triggering thresholds is presented. Such an approach contributes to overcome some of the above mentioned shortcomings of direct empirical analysis of observed data. The proposed Monte Carlo framework consists in the combination of a rainfall stochastic model with hydrological and slope-stability model. Specifically, 1000-years long hourly synthetic rainfall and related slope stability factor of safety data are generated by coupling the Neyman-Scott rectangular pulses model with the TRIGRS unsaturated model (Baum et al., 2008) and a linear-reservoir water table recession model. Triggering and non-triggering rainfall events are then distinguished and analyzed to derive stochastic-input physically based thresholds that optimize the trade-off between correct and wrong predictions. For this purpose, receiver operating characteristic (ROC) indices are used. An application of the method to the highly landslide-prone area of the Peloritani mountains in north-eastern Sicily (Italy) is carried out. A threshold for the area is derived and successfully validated by comparison with thresholds proposed by other researchers. Moreover, the uncertainty in threshold derivation due to variability of rainfall intensity within events and to antecedent rainfall is investigated. Results indicate that variability of intensity during rainfall events influences significantly rainfall intensity and duration associated with landslide triggering. A representation of rainfall as constant-intensity hyetographs globally leads to
In all likelihood statistical modelling and inference using likelihood
Pawitan, Yudi
2001-01-01
Based on a course in the theory of statistics this text concentrates on what can be achieved using the likelihood/Fisherian method of taking account of uncertainty when studying a statistical problem. It takes the concept ot the likelihood as providing the best methods for unifying the demands of statistical modelling and the theory of inference. Every likelihood concept is illustrated by realistic examples, which are not compromised by computational problems. Examples range from asimile comparison of two accident rates, to complex studies that require generalised linear or semiparametric mode
Modified maximum likelihood registration based on information fusion
Institute of Scientific and Technical Information of China (English)
Yongqing Qi; Zhongliang Jing; Shiqiang Hu
2007-01-01
The bias estimation of passive sensors is considered based on information fusion in multi-platform multisensor tracking system. The unobservable problem of bearing-only tracking in blind spot is analyzed. A modified maximum likelihood method, which uses the redundant information of multi-sensor system to calculate the target position, is investigated to estimate the biases. Monte Carlo simulation results show that the modified method eliminates the effect of unobservable problem in the blind spot and can estimate the biases more rapidly and accurately than maximum likelihood method. It is statistically efficient since the standard deviation of bias estimation errors meets the theoretical lower bounds.
Monte-Carlo and Bayesian techniques in gravitational wave burst data analysis
Searle, Antony C
2008-01-01
Monte-Carlo simulations are used in the gravitational wave burst detection community to demonstrate and compare the properties of different search techniques. We note that every Monte-Carlo simulation has a corresponding optimal search technique according to both the non-Bayesian Neyman-Pearson criterion and the Bayesian approach, and that this optimal search technique is the Bayesian statistic. When practical, we recommend deriving the optimal statistic for a credible Monte-Carlo simulation, rather than testing ad hoc statistics against that simulation.
Lim, J. T.; Gold, H. J.; Wilkerson, G. G.; Raper, C. D. Jr; Raper CD, J. r. (Principal Investigator)
1989-01-01
We describe the application of a strategy for conducting a sensitivity analysis for a complex dynamic model. The procedure involves preliminary screening of parameter sensitivities by numerical estimation of linear sensitivity coefficients, followed by generation of a response surface based on Monte Carlo simulation. Application is to a physiological model of the vegetative growth of soybean plants. The analysis provides insights as to the relative importance of certain physiological processes in controlling plant growth. Advantages and disadvantages of the strategy are discussed.
Penalized Splines for Smooth Representation of High-dimensional Monte Carlo Datasets
Whitehorn, Nathan; Lafebre, Sven
2013-01-01
Detector response to a high-energy physics process is often estimated by Monte Carlo simulation. For purposes of data analysis, the results of this simulation are typically stored in large multi-dimensional histograms, which can quickly become both too large to easily store and manipulate and numerically problematic due to unfilled bins or interpolation artifacts. We describe here an application of the penalized spline technique to efficiently compute B-spline representations of such tables and discuss aspects of the resulting B-spline fits that simplify many common tasks in handling tabulated Monte Carlo data in high-energy physics analysis, in particular their use in maximum-likelihood fitting.
Subtracting and Fitting Histograms using Profile Likelihood
D'Almeida, F M L
2008-01-01
It is known that many interesting signals expected at LHC are of unknown shape and strongly contaminated by background events. These signals will be dif cult to detect during the rst years of LHC operation due to the initial low luminosity. In this work, one presents a method of subtracting histograms based on the pro le likelihood function when the background is previously estimated by Monte Carlo events and one has low statistics. Estimators for the signal in each bin of the histogram difference are calculated so as limits for the signals with 68.3% of Con dence Level in a low statistics case when one has a exponential background and a Gaussian signal. The method can also be used to t histograms when the signal shape is known. Our results show a good performance and avoid the problem of negative values when subtracting histograms.
Development of Monte Carlo code for coincidence prompt gamma-ray neutron activation analysis
Han, Xiaogang
Prompt Gamma-Ray Neutron Activation Analysis (PGNAA) offers a non-destructive, relatively rapid on-line method for determination of elemental composition of bulk and other samples. However, PGNAA has an inherently large background. These backgrounds are primarily due to the presence of the neutron excitation source. It also includes neutron activation of the detector and the prompt gamma rays from the structure materials of PGNAA devices. These large backgrounds limit the sensitivity and accuracy of PGNAA. Since most of the prompt gamma rays from the same element are emitted in coincidence, a possible approach for further improvement is to change the traditional PGNAA measurement technique and introduce the gamma-gamma coincidence technique. It is well known that the coincidence techniques can eliminate most of the interference backgrounds and improve the signal-to-noise ratio. A new Monte Carlo code, CEARCPG has been developed at CEAR to simulate gamma-gamma coincidence spectra in PGNAA experiment. Compared to the other existing Monte Carlo code CEARPGA I and CEARPGA II, a new algorithm of sampling the prompt gamma rays produced from neutron capture reaction and neutron inelastic scattering reaction, is developed in this work. All the prompt gamma rays are taken into account by using this new algorithm. Before this work, the commonly used method is to interpolate the prompt gamma rays from the pre-calculated gamma-ray table. This technique works fine for the single spectrum. However it limits the capability to simulate the coincidence spectrum. The new algorithm samples the prompt gamma rays from the nucleus excitation scheme. The primary nuclear data library used to sample the prompt gamma rays comes from ENSDF library. Three cases are simulated and the simulated results are benchmarked with experiments. The first case is the prototype for ETI PGNAA application. This case is designed to check the capability of CEARCPG for single spectrum simulation. The second
The effect of load imbalances on the performance of Monte Carlo algorithms in LWR analysis
Energy Technology Data Exchange (ETDEWEB)
Siegel, A.R., E-mail: siegela@mcs.anl.gov [Argonne National Laboratory, Nuclear Engineering Division (United States); Argonne National Laboratory, Mathematics and Computer Science Division (United States); Smith, K., E-mail: kord@mit.edu [Massachusetts Institute of Technology, Department of Nuclear Science and Engineering (United States); Romano, P.K., E-mail: romano7@mit.edu [Massachusetts Institute of Technology, Department of Nuclear Science and Engineering (United States); Forget, B., E-mail: bforget@mit.edu [Massachusetts Institute of Technology, Department of Nuclear Science and Engineering (United States); Felker, K., E-mail: felker@mcs.anl.gov [Argonne National Laboratory, Mathematics and Computer Science Division (United States)
2013-02-15
A model is developed to predict the impact of particle load imbalances on the performance of domain-decomposed Monte Carlo neutron transport algorithms. Expressions for upper bound performance “penalties” are derived in terms of simple machine characteristics, material characterizations and initial particle distributions. The hope is that these relations can be used to evaluate tradeoffs among different memory decomposition strategies in next generation Monte Carlo codes, and perhaps as a metric for triggering particle redistribution in production codes.
MULTI-KENO: a Monte Carlo code for criticality safety analysis
International Nuclear Information System (INIS)
Modifying the Monte Carlo code KENO-IV, the MULTI-KENO code was developed for criticality safety analysis. The following functions were added to the code; (1) to divide a system into many sub-systems named super boxes where the size of box types in each super box can be selected independently, (2) to output graphical view of a system for examining geometrical input data, (3) to solve fixed source problems, (4) to permit intersection of core boundaries and inner geometries, (5) to output ANISN type neutron balance table. With the above function (1), many cases which had to be applied a general geometry option of KENO-IV, became to be treated as box type geometry. In such a case, input data became simpler and required computer time became shorter than those of KENO-IV. This code is now available for the FACOM-M200 computer and the CDC 6600 computer. This report is a computer code manual for MULTI-KENO. (author)
Monte Carlo analysis of dissociation and recombination behind strong shock waves in nitrogen
Boyd, I. D.
1991-01-01
Computations are presented for the relaxation zone behind strong, 1D shock waves in nitrogen. The analysis is performed with the direct simulation Monte Carlo method (DSMC). The DSMC code is vectorized for efficient use on a supercomputer. The code simulates translational, rotational and vibrational energy exchange and dissociative and recombinative chemical reactions. A model is proposed for the treatment of three body-recombination collisions in the DSMC technique which usually simulates binary collision events. The model improves previous models because it can be employed with a large range of chemical-rate data, does not introduce into the flow field troublesome pairs of atoms which may recombine upon further collision (pseudoparticles) and is compatible with the vectorized code. The computational results are compared with existing experimental data. It is shown that the derivation of chemical-rate coefficients must account for the degree of vibrational nonequilibrium in the flow. A nonequilibrium-chemistry model is employed together with equilibrium-rate data to compute the flow in several different nitrogen shock waves.
International Nuclear Information System (INIS)
The benchmark analysis of reactivity experiments in the TRIGA-II core at the Musashi Institute of Technology Research Reactor (Musashi reactor; 100 kW) was performed by a three-dimensional continuous-energy Monte Carlo code MCNP4A. The reactivity worth and integral reactivity curves of the control rods as well as the reactivity worth distributions of fuel and graphite elements were used in the validation process of the physical model and neutron cross section data from the ENDF/B-V evaluation. The calculated values of integral reactivity curves of the control rods were in agreement with the experimental data obtained by the period method. The integral worth measured by the rod drop method was also consistent with the calculation. The calculated values of the fuel and the graphite element worth distributions were consistent with the measured ones within the statistical error estimates. These results showed that the exact core configuration including the control rod positions to reproduce the fission source distribution in the experiment must be introduced into the calculation core for obtaining the precise solution. It can be concluded that our simulation model of the TRIGA-II core is precise enough to reproduce the control rod worth, fuel and graphite elements reactivity worth distributions. (author)
International Nuclear Information System (INIS)
The Albedo method applied to criticality calculations to nuclear reactors is characterized by following the neutron currents, allowing to make detailed analyses of the physics phenomena about interactions of the neutrons with the core-reflector set, by the determination of the probabilities of reflection, absorption, and transmission. Then, allowing to make detailed appreciations of the variation of the effective neutron multiplication factor, keff. In the present work, motivated for excellent results presented in dissertations applied to thermal reactors and shieldings, was described the methodology to Albedo method for the analysis criticality of thermal reactors by using two energy groups admitting variable core coefficients to each re-entrant current. By using the Monte Carlo KENO IV code was analyzed relation between the total fraction of neutrons absorbed in the core reactor and the fraction of neutrons that never have stayed into the reflector but were absorbed into the core. As parameters of comparison and analysis of the results obtained by the Albedo method were used one dimensional deterministic code ANISN (ANIsotropic SN transport code) and Diffusion method. The keff results determined by the Albedo method, to the type of analyzed reactor, showed excellent agreement. Thus were obtained relative errors of keff values smaller than 0,78% between the Albedo method and code ANISN. In relation to the Diffusion method were obtained errors smaller than 0,35%, showing the effectiveness of the Albedo method applied to criticality analysis. The easiness of application, simplicity and clarity of the Albedo method constitute a valuable instrument to neutronic calculations applied to nonmultiplying and multiplying media. (author)
Synthesizing regression results: a factored likelihood method.
Wu, Meng-Jia; Becker, Betsy Jane
2013-06-01
Regression methods are widely used by researchers in many fields, yet methods for synthesizing regression results are scarce. This study proposes using a factored likelihood method, originally developed to handle missing data, to appropriately synthesize regression models involving different predictors. This method uses the correlations reported in the regression studies to calculate synthesized standardized slopes. It uses available correlations to estimate missing ones through a series of regressions, allowing us to synthesize correlations among variables as if each included study contained all the same variables. Great accuracy and stability of this method under fixed-effects models were found through Monte Carlo simulation. An example was provided to demonstrate the steps for calculating the synthesized slopes through sweep operators. By rearranging the predictors in the included regression models or omitting a relatively small number of correlations from those models, we can easily apply the factored likelihood method to many situations involving synthesis of linear models. Limitations and other possible methods for synthesizing more complicated models are discussed. Copyright © 2012 John Wiley & Sons, Ltd. PMID:26053653
Invariants and Likelihood Ratio Statistics
McCullagh, P.; Cox, D. R.
1986-01-01
Because the likelihood ratio statistic is invariant under reparameterization, it is possible to make a large-sample expansion of the statistic itself and of its expectation in terms of invariants. In particular, the Bartlett adjustment factor can be expressed in terms of invariant combinations of cumulants of the first two log-likelihood derivatives. Such expansions are given, first for a scalar parameter and then for vector parameters. Geometrical interpretation is given where possible and s...
Inference in HIV dynamics models via hierarchical likelihood
Commenges, D; Putter, H; Thiebaut, R
2010-01-01
HIV dynamical models are often based on non-linear systems of ordinary differential equations (ODE), which do not have analytical solution. Introducing random effects in such models leads to very challenging non-linear mixed-effects models. To avoid the numerical computation of multiple integrals involved in the likelihood, we propose a hierarchical likelihood (h-likelihood) approach, treated in the spirit of a penalized likelihood. We give the asymptotic distribution of the maximum h-likelihood estimators (MHLE) for fixed effects, a result that may be relevant in a more general setting. The MHLE are slightly biased but the bias can be made negligible by using a parametric bootstrap procedure. We propose an efficient algorithm for maximizing the h-likelihood. A simulation study, based on a classical HIV dynamical model, confirms the good properties of the MHLE. We apply it to the analysis of a clinical trial.
Monte Carlo-based multiphysics coupling analysis of x-ray pulsar telescope
Li, Liansheng; Deng, Loulou; Mei, Zhiwu; Zuo, Fuchang; Zhou, Hao
2015-10-01
X-ray pulsar telescope (XPT) is a complex optical payload, which involves optical, mechanical, electrical and thermal disciplines. The multiphysics coupling analysis (MCA) plays an important role in improving the in-orbit performance. However, the conventional MCA methods encounter two serious problems in dealing with the XTP. One is that both the energy and reflectivity information of X-ray can't be taken into consideration, which always misunderstands the essence of XPT. Another is that the coupling data can't be transferred automatically among different disciplines, leading to computational inefficiency and high design cost. Therefore, a new MCA method for XPT is proposed based on the Monte Carlo method and total reflective theory. The main idea, procedures and operational steps of the proposed method are addressed in detail. Firstly, it takes both the energy and reflectivity information of X-ray into consideration simultaneously. And formulate the thermal-structural coupling equation and multiphysics coupling analysis model based on the finite element method. Then, the thermalstructural coupling analysis under different working conditions has been implemented. Secondly, the mirror deformations are obtained using construction geometry function. Meanwhile, the polynomial function is adopted to fit the deformed mirror and meanwhile evaluate the fitting error. Thirdly, the focusing performance analysis of XPT can be evaluated by the RMS. Finally, a Wolter-I XPT is taken as an example to verify the proposed MCA method. The simulation results show that the thermal-structural coupling deformation is bigger than others, the vary law of deformation effect on the focusing performance has been obtained. The focusing performances of thermal-structural, thermal, structural deformations have degraded 30.01%, 14.35% and 7.85% respectively. The RMS of dispersion spot are 2.9143mm, 2.2038mm and 2.1311mm. As a result, the validity of the proposed method is verified through
Romania Monte Carlo Methods Application to CANDU Spent Fuel Comparative Analysis
International Nuclear Information System (INIS)
Romania has a single NPP at Cernavoda with 5 PHWR reactors of CANDU6 type of 705 MW(e) each, with Cernavoda Unit1, operational starting from December 1996, Unit2 under construction while the remaining Unit3-5 is being conserved. The nuclear energy world wide development is accompanied by huge quantities of spent nuclear fuel accumulation. Having in view the possible impact upon population and environment, in all activities associated to nuclear fuel cycle, namely transportation, storage, reprocessing or disposal, the spent fuel characteristics must be well known. The paper aim is to apply Monte Carlo methods to CANDU spent fuel analysis, starting from the discharge moment, followed by spent fuel transport after a defined cooling period and finishing with the intermediate dry storage. As radiation source 3 CANDU fuels have been considered: standard 37 rods fuel bundle with natural UO2 and SEU fuels, and 43 rods fuel bundle with SEU fuel. After a criticality calculation using KENO-VI code, the criticality coefficient and the actinides and fission products concentrations are obtained. By using ORIGEN-S code, the photon source profiles are calculated and the spent fuel characteristics estimation is done. For the shielding calculations MORSE-SGC code has been used. Regarding to the spent fuel transport, the photon dose rates to the shipping cask wall and in air, at different distances from the cask, are estimated. The shielding calculation for the spent fuel intermediate dry storage is done and the photon dose rates at the storage basket wall (active element of the Cernavoda NPP intermediate dry storage) are obtained. A comparison between the 3 types of CANDU fuels is presented. (authors)
International Nuclear Information System (INIS)
The Center for Engineering Applications of Radioisotopes (CEAR) has been working for about ten years on the Monte Carlo - Library Least-Squares (MCLLS) approach for treating the nonlinear inverse analysis problem for PGNAA bulk analysis. This approach consists essentially of using Monte Carlo simulation to generate the libraries of all the elements to be analyzed plus any other required libraries. These libraries are then used in the linear Library Least-Squares (LLS) approach with unknown sample spectra to analyze for all elements in the sample. The other libraries include all sources of background which includes: (1) gamma-rays emitted by the neutron source, (2) prompt gamma-rays produced in the analyzer construction materials, (3) natural gamma-rays from K-40 and the uranium and thorium decay chains, and (4) prompt and decay gamma-rays produced in the NaI detector by neutron activation. A number of unforeseen problems have arisen in pursuing this approach including: (1) the neutron activation of the most common detector (NaI) used in bulk analysis PGNAA systems, (2) the nonlinearity of this detector, and (3) difficulties in obtaining detector response functions for this (and other) detectors. These problems have been addressed by CEAR recently and have either been solved or are almost solved at the present time. Development of Monte Carlo simulation for all of the libraries has been finished except the prompt gamma-ray library from the activation of the NaI detector. Treatment for the coincidence schemes for Na and particularly I must be first determined to complete the Monte Carlo simulation of this last library. (author)
International Nuclear Information System (INIS)
, with high vertical resolution, could be generated for many wells. This procedure permits to populate any well location with core-scale estimates of P and P and rock types facilitating the application of geostatistical characterization methods. The first step procedure was to discriminate rock types of similar depositional environment and/or reservoir quality (RQ) using a specific clustering technique. The approach implemented utilized a model-based, probabilistic clustering analysis procedure called GAMLS1,2,3,4 (Geologic Analysis via Maximum Likelihood System) which is based on maximum likelihood principles. During clustering, samples (data at each digitized depth from each well) are probabilistically assigned to a previously specified number of clusters with a fractional probability that varies between zero and one
A Bayesian analysis of rare B decays with advanced Monte Carlo methods
Energy Technology Data Exchange (ETDEWEB)
Beaujean, Frederik
2012-11-12
Searching for new physics in rare B meson decays governed by b {yields} s transitions, we perform a model-independent global fit of the short-distance couplings C{sub 7}, C{sub 9}, and C{sub 10} of the {Delta}B=1 effective field theory. We assume the standard-model set of b {yields} s{gamma} and b {yields} sl{sup +}l{sup -} operators with real-valued C{sub i}. A total of 59 measurements by the experiments BaBar, Belle, CDF, CLEO, and LHCb of observables in B{yields}K{sup *}{gamma}, B{yields}K{sup (*)}l{sup +}l{sup -}, and B{sub s}{yields}{mu}{sup +}{mu}{sup -} decays are used in the fit. Our analysis is the first of its kind to harness the full power of the Bayesian approach to probability theory. All main sources of theory uncertainty explicitly enter the fit in the form of nuisance parameters. We make optimal use of the experimental information to simultaneously constrain theWilson coefficients as well as hadronic form factors - the dominant theory uncertainty. Generating samples from the posterior probability distribution to compute marginal distributions and predict observables by uncertainty propagation is a formidable numerical challenge for two reasons. First, the posterior has multiple well separated maxima and degeneracies. Second, the computation of the theory predictions is very time consuming. A single posterior evaluation requires O(1s), and a few million evaluations are needed. Population Monte Carlo (PMC) provides a solution to both issues; a mixture density is iteratively adapted to the posterior, and samples are drawn in a massively parallel way using importance sampling. The major shortcoming of PMC is the need for cogent knowledge of the posterior at the initial stage. In an effort towards a general black-box Monte Carlo sampling algorithm, we present a new method to extract the necessary information in a reliable and automatic manner from Markov chains with the help of hierarchical clustering. Exploiting the latest 2012 measurements, the fit
A Bayesian analysis of rare B decays with advanced Monte Carlo methods
International Nuclear Information System (INIS)
Searching for new physics in rare B meson decays governed by b → s transitions, we perform a model-independent global fit of the short-distance couplings C7, C9, and C10 of the ΔB=1 effective field theory. We assume the standard-model set of b → sγ and b → sl+l- operators with real-valued Ci. A total of 59 measurements by the experiments BaBar, Belle, CDF, CLEO, and LHCb of observables in B→K*γ, B→K(*)l+l-, and Bs→μ+μ- decays are used in the fit. Our analysis is the first of its kind to harness the full power of the Bayesian approach to probability theory. All main sources of theory uncertainty explicitly enter the fit in the form of nuisance parameters. We make optimal use of the experimental information to simultaneously constrain theWilson coefficients as well as hadronic form factors - the dominant theory uncertainty. Generating samples from the posterior probability distribution to compute marginal distributions and predict observables by uncertainty propagation is a formidable numerical challenge for two reasons. First, the posterior has multiple well separated maxima and degeneracies. Second, the computation of the theory predictions is very time consuming. A single posterior evaluation requires O(1s), and a few million evaluations are needed. Population Monte Carlo (PMC) provides a solution to both issues; a mixture density is iteratively adapted to the posterior, and samples are drawn in a massively parallel way using importance sampling. The major shortcoming of PMC is the need for cogent knowledge of the posterior at the initial stage. In an effort towards a general black-box Monte Carlo sampling algorithm, we present a new method to extract the necessary information in a reliable and automatic manner from Markov chains with the help of hierarchical clustering. Exploiting the latest 2012 measurements, the fit reveals a flipped-sign solution in addition to a standard-model-like solution for the couplings Ci. The two solutions are related
Analysis of uncertainty quantification method by comparing Monte-Carlo method and Wilk's formula
International Nuclear Information System (INIS)
An analysis of the uncertainty quantification related to LBLOCA using the Monte-Carlo calculation has been performed and compared with the tolerance level determined by the Wilks' formula. The uncertainty range and distribution of each input parameter associated with the LOCA phenomena were determined based on previous PIRT results and documentation during the BEMUSE project. Calulations were conducted on 3,500 cases within a 2-week CPU time on a 14-PC cluster system. The Monte-Carlo exercise shows that the 95% upper limit PCT value can be obtained well, with a 95% confidence level using the Wilks' formula, although we have to endure a 5% risk of PCT under-prediction. The results also show that the statistical fluctuation of the limit value using Wilks' first-order is as large as the uncertainty value itself. It is therefore desirable to increase the order of the Wilks' formula to be higher than the second-order to estimate the reliable safety margin of the design features. It is also shown that, with its ever increasing computational capability, the Monte-Carlo method is accessible for a nuclear power plant safety analysis within a realistic time frame.
Lai, Bo-Lun; Sheu, Rong-Jiun; Lin, Uei-Tyng
2015-05-01
Monte Carlo simulations are generally considered the most accurate method for complex accelerator shielding analysis. Simplified models based on point-source line-of-sight approximation are often preferable in practice because they are intuitive and easy to use. A set of shielding data, including source terms and attenuation lengths for several common targets (iron, graphite, tissue, and copper) and shielding materials (concrete, iron, and lead) were generated by performing Monte Carlo simulations for 100-300 MeV protons. Possible applications and a proper use of the data set were demonstrated through a practical case study, in which shielding analysis on a typical proton treatment room was conducted. A thorough and consistent comparison between the predictions of our point-source line-of-sight model and those obtained by Monte Carlo simulations for a 360° dose distribution around the room perimeter showed that the data set can yield fairly accurate or conservative estimates for the transmitted doses, except for those near the maze exit. In addition, this study demonstrated that appropriate coupling between the generated source term and empirical formulae for radiation streaming can be used to predict a reasonable dose distribution along the maze. This case study proved the effectiveness and advantage of applying the data set to a quick shielding design and dose evaluation for proton therapy accelerators. PMID:25811254
Ismail, A E; Rutledge, G C; Ismail, Ahmed E.; Stephanopoulos, George; Rutledge, Gregory C.
2003-01-01
In this paper, we extend our analysis of lattice systems using the wavelet transform to systems for which exact enumeration is impractical. For such systems, we illustrate a wavelet-accelerated Monte Carlo (WAMC) algorithm, which hierarchically coarse-grains a lattice model by computing the probability distribution for successively larger block spins. We demonstrate that although the method perturbs the system by changing its Hamiltonian and by allowing block spins to take on values not permitted for individual spins, the results obtained agree with the analytical results in the preceding paper, and ``converge'' to exact results obtained in the absence of coarse-graining. Additionally, we show that the decorrelation time for the WAMC is no worse than that of Metropolis Monte Carlo (MMC), and that scaling laws can be constructed from data performed in several short simulations to estimate the results that would be obtained from the original simulation. Although the algorithm is not asymptotically faster than t...
Shang, Yilun
2016-08-01
How complex a network is crucially impacts its function and performance. In many modern applications, the networks involved have a growth property and sparse structures, which pose challenges to physicists and applied mathematicians. In this paper, we introduce the forest likelihood as a plausible measure to gauge how difficult it is to construct a forest in a non-preferential attachment way. Based on the notions of admittable labeling and path construction, we propose algorithms for computing the forest likelihood of a given forest. Concrete examples as well as the distributions of forest likelihoods for all forests with some fixed numbers of nodes are presented. Moreover, we illustrate the ideas on real-life networks, including a benzenoid tree, a mathematical family tree, and a peer-to-peer network.
Accurate structural correlations from maximum likelihood superpositions.
Directory of Open Access Journals (Sweden)
Douglas L Theobald
2008-02-01
Full Text Available The cores of globular proteins are densely packed, resulting in complicated networks of structural interactions. These interactions in turn give rise to dynamic structural correlations over a wide range of time scales. Accurate analysis of these complex correlations is crucial for understanding biomolecular mechanisms and for relating structure to function. Here we report a highly accurate technique for inferring the major modes of structural correlation in macromolecules using likelihood-based statistical analysis of sets of structures. This method is generally applicable to any ensemble of related molecules, including families of nuclear magnetic resonance (NMR models, different crystal forms of a protein, and structural alignments of homologous proteins, as well as molecular dynamics trajectories. Dominant modes of structural correlation are determined using principal components analysis (PCA of the maximum likelihood estimate of the correlation matrix. The correlations we identify are inherently independent of the statistical uncertainty and dynamic heterogeneity associated with the structural coordinates. We additionally present an easily interpretable method ("PCA plots" for displaying these positional correlations by color-coding them onto a macromolecular structure. Maximum likelihood PCA of structural superpositions, and the structural PCA plots that illustrate the results, will facilitate the accurate determination of dynamic structural correlations analyzed in diverse fields of structural biology.
Yang, P.; Ng, T. L.; Yang, W.
2015-12-01
Effective water resources management depends on the reliable estimation of the uncertainty of drought events. Confidence intervals (CIs) are commonly applied to quantify this uncertainty. A CI seeks to be at the minimal length necessary to cover the true value of the estimated variable with the desired probability. In drought analysis where two or more variables (e.g., duration and severity) are often used to describe a drought, copulas have been found suitable for representing the joint probability behavior of these variables. However, the comprehensive assessment of the parameter uncertainties of copulas of droughts has been largely ignored, and the few studies that have recognized this issue have not explicitly compared the various methods to produce the best CIs. Thus, the objective of this study to compare the CIs generated using two widely applied uncertainty estimation methods, bootstrapping and Markov Chain Monte Carlo (MCMC). To achieve this objective, (1) the marginal distributions lognormal, Gamma, and Generalized Extreme Value, and the copula functions Clayton, Frank, and Plackett are selected to construct joint probability functions of two drought related variables. (2) The resulting joint functions are then fitted to 200 sets of simulated realizations of drought events with known distribution and extreme parameters and (3) from there, using bootstrapping and MCMC, CIs of the parameters are generated and compared. The effect of an informative prior on the CIs generated by MCMC is also evaluated. CIs are produced for different sample sizes (50, 100, and 200) of the simulated drought events for fitting the joint probability functions. Preliminary results assuming lognormal marginal distributions and the Clayton copula function suggest that for cases with small or medium sample sizes (~50-100), MCMC to be superior method if an informative prior exists. Where an informative prior is unavailable, for small sample sizes (~50), both bootstrapping and MCMC
Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network
Directory of Open Access Journals (Sweden)
Klipp Edda
2009-08-01
Full Text Available Abstract Background Gene Regulatory Networks (GRNs control the differentiation, specification and function of cells at the genomic level. The levels of interactions within large GRNs are of enormous depth and complexity. Details about many GRNs are emerging, but in most cases it is unknown to what extent they control a given process, i.e. the grade of completeness is uncertain. This uncertainty stems from limited experimental data, which is the main bottleneck for creating detailed dynamical models of cellular processes. Parameter estimation for each node is often infeasible for very large GRNs. We propose a method, based on random parameter estimations through Monte-Carlo simulations to measure completeness grades of GRNs. Results We developed a heuristic to assess the completeness of large GRNs, using ODE simulations under different conditions and randomly sampled parameter sets to detect parameter-invariant effects of perturbations. To test this heuristic, we constructed the first ODE model of the whole sea urchin endomesoderm GRN, one of the best studied large GRNs. We find that nearly 48% of the parameter-invariant effects correspond with experimental data, which is 65% of the expected optimal agreement obtained from a submodel for which kinetic parameters were estimated and used for simulations. Randomized versions of the model reproduce only 23.5% of the experimental data. Conclusion The method described in this paper enables an evaluation of network topologies of GRNs without requiring any parameter values. The benefit of this method is exemplified in the first mathematical analysis of the complete Endomesoderm Network Model. The predictions we provide deliver candidate nodes in the network that are likely to be erroneous or miss unknown connections, which may need additional experiments to improve the network topology. This mathematical model can serve as a scaffold for detailed and more realistic models. We propose that our method can
Energy Technology Data Exchange (ETDEWEB)
Brandt, Gerhard [University of Oxford (United Kingdom); Krauss, Frank [IPPP Durham (United Kingdom); Lacker, Heiko; Leyton, Michael; Mamach, Martin; Schulz, Holger; Weyh, Daniel [Humboldt University of Berlin (Germany)
2012-07-01
Monte Carlo (MC) event generators are widely employed in the analysis of experimental data also for LHC in order to predict the features of observables and test analyses with them. These generators rely on phenomenological models containing various parameters which are free in certain ranges. Variations of these parameters relative to their default lead to uncertainties on the predictions of the event generators and, in turn, on the results of any experimental data analysis making use of the event generator. A Generalized method for quantifying a certain class of these generator based uncertainties will be presented in this talk. We study for the SHERPA event generator the effect on the analysis results from uncertainties in the choice of the merging and factorization scale. The quantification is done within an example ATLAS analysis measuring underlying event UE properties in Z-boson production limited to low transverse momenta (p{sub T}{sup Z}<3 GeV) of the Z-boson. The analysis extracts event-shape distributions from charged particles in the event that do not belong to the Z decay for generate Monte Carlo event and data which are unfolded back to the generator level.
Monte Carlo analysis of the accelerator-driven system at Kyoto University Research Reactor Institute
Energy Technology Data Exchange (ETDEWEB)
Kim, Won Kyeong; Lee, Deok Jung [Nuclear Engineering Division, Ulsan National Institute of Science and Technology, Ulsan (Korea, Republic of); Lee, Hyun Chul [VHTR Technology Development Division, Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Pyeon, Cheol Ho [Nuclear Engineering Science Division, Kyoto University Research Reactor Institute, Osaka (Japan); Shin, Ho Cheol [Core and Fuel Analysis Group, Korea Hydro and Nuclear Power Central Research Institute, Daejeon (Korea, Republic of)
2016-04-15
An accelerator-driven system consists of a subcritical reactor and a controllable external neutron source. The reactor in an accelerator-driven system can sustain fission reactions in a subcritical state using an external neutron source, which is an intrinsic safety feature of the system. The system can provide efficient transmutations of nuclear wastes such as minor actinides and long-lived fission products and generate electricity. Recently at Kyoto University Research Reactor Institute (KURRI; Kyoto, Japan), a series of reactor physics experiments was conducted with the Kyoto University Critical Assembly and a Cockcroft-Walton type accelerator, which generates the external neutron source by deuterium-tritium reactions. In this paper, neutronic analyses of a series of experiments have been re-estimated by using the latest Monte Carlo code and nuclear data libraries. This feasibility study is presented through the comparison of Monte Carlo simulation results with measurements.
Evaluation of CASMO-3 and HELIOS for Fuel Assembly Analysis from Monte Carlo Code
Energy Technology Data Exchange (ETDEWEB)
Shim, Hyung Jin; Song, Jae Seung; Lee, Chung Chan
2007-05-15
This report presents a study comparing deterministic lattice physics calculations with Monte Carlo calculations for LWR fuel pin and assembly problems. The study has focused on comparing results from the lattice physics code CASMO-3 and HELIOS against those from the continuous-energy Monte Carlo code McCARD. The comparisons include k{sub inf}, isotopic number densities, and pin power distributions. The CASMO-3 and HELIOS calculations for the k{sub inf}'s of the LWR fuel pin problems show good agreement with McCARD within 956pcm and 658pcm, respectively. For the assembly problems with Gadolinia burnable poison rods, the largest difference between the k{sub inf}'s is 1463pcm with CASMO-3 and 1141pcm with HELIOS. RMS errors for the pin power distributions of CASMO-3 and HELIOS are within 1.3% and 1.5%, respectively.
An analysis on the theory of pulse oximetry by Monte Carlo simulation
Fan, Shangchun; Cai, Rui; Xing, Weiwei; Liu, Changting; Chen, Guangfei; Wang, Junfeng
2008-10-01
The pulse oximetry is a kind of electronic instrument that measures the oxygen saturation of arterial blood and pulse rate by non-invasive techniques. It enables prompt recognition of hypoxemia. In a conventional transmittance type pulse oximeter, the absorption of light by oxygenated and reduced hemoglobin is measured at two wavelength 660nm and 940nm. But the accuracy and measuring range of the pulse oximeter can not meet the requirement of clinical application. There are limitations in the theory of pulse oximetry, which is proved by Monte Carlo method. The mean paths are calculated in the Monte Carlo simulation. The results prove that the mean paths are not the same between the different wavelengths.
A maximum likelihood framework for protein design
Directory of Open Access Journals (Sweden)
Philippe Hervé
2006-06-01
Full Text Available Abstract Background The aim of protein design is to predict amino-acid sequences compatible with a given target structure. Traditionally envisioned as a purely thermodynamic question, this problem can also be understood in a wider context, where additional constraints are captured by learning the sequence patterns displayed by natural proteins of known conformation. In this latter perspective, however, we still need a theoretical formalization of the question, leading to general and efficient learning methods, and allowing for the selection of fast and accurate objective functions quantifying sequence/structure compatibility. Results We propose a formulation of the protein design problem in terms of model-based statistical inference. Our framework uses the maximum likelihood principle to optimize the unknown parameters of a statistical potential, which we call an inverse potential to contrast with classical potentials used for structure prediction. We propose an implementation based on Markov chain Monte Carlo, in which the likelihood is maximized by gradient descent and is numerically estimated by thermodynamic integration. The fit of the models is evaluated by cross-validation. We apply this to a simple pairwise contact potential, supplemented with a solvent-accessibility term, and show that the resulting models have a better predictive power than currently available pairwise potentials. Furthermore, the model comparison method presented here allows one to measure the relative contribution of each component of the potential, and to choose the optimal number of accessibility classes, which turns out to be much higher than classically considered. Conclusion Altogether, this reformulation makes it possible to test a wide diversity of models, using different forms of potentials, or accounting for other factors than just the constraint of thermodynamic stability. Ultimately, such model-based statistical analyses may help to understand the forces
Single pin BWR benchmark problem for coupled Monte Carlo - Thermal hydraulics analysis
Energy Technology Data Exchange (ETDEWEB)
Ivanov, A.; Sanchez, V. [Karlsruhe Inst. of Technology, Inst. for Neutron Physics and Reactor Technology, Herman-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen (Germany); Hoogenboom, J. E. [Delft Univ. of Technology, Faculty of Applied Sciences, Mekelweg 15, 2629 JB Delft (Netherlands)
2012-07-01
As part of the European NURISP research project, a single pin BWR benchmark problem was defined. The aim of this initiative is to test the coupling strategies between Monte Carlo and subchannel codes developed by different project participants. In this paper the results obtained by the Delft Univ. of Technology and Karlsruhe Inst. of Technology will be presented. The benchmark problem was simulated with the following coupled codes: TRIPOLI-SUBCHANFLOW, MCNP-FLICA, MCNP-SUBCHANFLOW, and KENO-SUBCHANFLOW. (authors)
A Markov chain Monte Carlo method family in incomplete data analysis
Directory of Open Access Journals (Sweden)
Vasić Vladimir V.
2003-01-01
Full Text Available A Markov chain Monte Carlo method family is a collection of techniques for pseudorandom draws out of probability distribution function. In recent years, these techniques have been the subject of intensive interest of many statisticians. Roughly speaking, the essence of a Markov chain Monte Carlo method family is generating one or more values of a random variable Z, which is usually multidimensional. Let P(Z = f(Z denote a density function of a random variable Z, which we will refer to as a target distribution. Instead of sampling directly from the distribution f, we will generate [Z(1, Z(2..., Z(t,... ], in which each value is, in a way, dependant upon the previous value and where the stationary distribution will be a target distribution. For a sufficient value of t, Z(t will be approximately random sampling of the distribution f. A Markov chain Monte Carlo method family is useful when direct sampling is difficult, but when sampling of each value is not.
International Nuclear Information System (INIS)
The major aim of this work is a sensitivity analysis related to the influence of the different nuclear data libraries on the k-infinity values and on the void coefficient estimations performed for various CANDU fuel projects, and on the simulations related to the replacement of the original stainless steel adjuster rods by cobalt assemblies in the CANDU reactor core. The computations are performed using the Monte Carlo transport codes MCNP5 and MONTEBURNS 1.0 for the actual, detailed geometry and material composition of the fuel bundles and reactivity devices. Some comparisons with deterministic and probabilistic codes involving the WIMS library are also presented
Energy Technology Data Exchange (ETDEWEB)
Park, Jae Phil; Bahn, Chi Bum [Pusan National University, Busan (Korea, Republic of)
2015-10-15
Probabilistic Fracture Mechanics (PFM) analysis was generally used to consider the scatter and uncertainty of parameters in complex phenomenon. Weld defects could be present in weld regions of Pressurized Water Reactors (PWRs), which cannot be considered by the typical fracture mechanics analysis. It is necessary to evaluate the effects of the pre-existing cracks in welds for the integrity of the welds. In this paper, PFM analysis for pre-existing cracks on Alloy 182 weld in PWR primary water environment was carried out using a Monte Carlo simulation. PFM analysis for pre-existing cracks on Alloy 182 weld in PWR primary water environment was carried out. It was shown that inspection decreases the gradient of the failure probability. And failure probability caused by the pre-existing cracks was stabilized after 15 years of operation time in this input condition.
Ren, Lixia; He, Li; Lu, Hongwei; Chen, Yizhong
2016-08-01
A new Monte Carlo-based interval transformation analysis (MCITA) is used in this study for multi-criteria decision analysis (MCDA) of naphthalene-contaminated groundwater management strategies. The analysis can be conducted when input data such as total cost, contaminant concentration and health risk are represented as intervals. Compared to traditional MCDA methods, MCITA-MCDA has the advantages of (1) dealing with inexactness of input data represented as intervals, (2) mitigating computational time due to the introduction of Monte Carlo sampling method, (3) identifying the most desirable management strategies under data uncertainty. A real-world case study is employed to demonstrate the performance of this method. A set of inexact management alternatives are considered in each duration on the basis of four criteria. Results indicated that the most desirable management strategy lied in action 15 for the 5-year, action 8 for the 10-year, action 12 for the 15-year, and action 2 for the 20-year management.
A viable method for goodness-of-fit test in maximum likelihood fit
Institute of Scientific and Technical Information of China (English)
ZHANG Feng; GAO Yuan-Ning; HUO Lei
2011-01-01
A test statistic is proposed to perform the goodness-of-fit test in the unbinned maximum likelihood fit. Without using a detailed expression of the efficiency function, the test statistic is found to be strongly correlated with the maximum likelihood function if the efficiency function varies smoothly. We point out that the correlation coefficient can be estimated by the Monte Carlo technique. With the established method, two examples are given to illustrate the performance of the test statistic.
阿尔茨海默病脑电信号的同步似然分析%Synchronization likelihood analysis of EEG in Alzheimer's disease
Institute of Scientific and Technical Information of China (English)
李莉; 陈静静; 郑旭媛
2015-01-01
目的：研究阿尔茨海默病（AD）患者脑电时间序列的同步性。方法采集AD患者和正常对照者各8名的16导联脑电图（EEG）数据，分别对2组EEG的全频段、δ（0.5~4 Hz）、θ（4~8 Hz）、α（8~13 Hz）和β频段（13~30 Hz）信号进行同步似然分析，比较不同频段的平均同步似然系数值并进行统计学分析。结果 AD组全频段、θ频段和α频段的平均同步似然系数值均小于对照组，差异有统计学意义（P0.05）。结论 AD组全频段、θ频段和α频段的EEG同步性降低，提示在θ和α频段内AD患者不同脑区的协同信息处理能力下降，为进一步研究AD患者大脑功能连接特性提供支持。%Objective To study the synchronization of electroencephalogram (EEG) in the patients with Alzheimer's disease (AD). Methods Sixteen-channel EEG was recorded under resting, eyes-closed condition in 8 AD patients and 8 control subjects. After data preprocessing, the synchronization likelihood was measured between pairs of EEG channels for the full band,δband (0.5-4 Hz),θband (4-8 Hz),αband (8-13 Hz) andβband (13-30 Hz) in the AD and control groups. Mean synchronization likelihood values of each frequency band were compared between the two groups. Results At the full band,θband andαband, the mean synchronization likelihood values of the AD group were lower than that of the control group (P0.05). Conclusions The EEG synchronization of AD patients decreases at the full band,θandαbands, suggesting that collaborative information processing ability atθandαbands reduces between different brain regions in the AD patients. The research provides support for further study of the brain functional connectivity characteristics in the AD patients.
International Nuclear Information System (INIS)
The analysis of void reactivity effect is prominent interest for Sodium-cooled Fast Reactor (SFR) safety. Indeed, in case of sodium leakage of the primary circuit, void reactivity represents the main passive negative feedback to ensure reactivity control. The core can be designed to maximize neutron leakage and lower the average neutron multiplication factor in the event of sodium disappearing from within assemblies. Thus, the nuclear chain reaction is stopped. The most promising solution is to place a sodium region above the fuel in order for neutrons to be reflected when the region is filled and escape when the region is empty. In terms of simulation, this configuration is a challenge for usual calculation schemes: 1. Deterministic codes are typically limited in their ability to homogenize a sub-critical medium as the sodium plenum. 2. Monte Carlo codes are typically not able to split the total reactivity effect on different components, which prevents to achieve straightforward uncertainty analysis. Furthermore, since experimental values can sometimes be small, Monte Carlo codes may not converge within a reasonable computation time. A new feature recently available in the Monte Carlo TRIPOLI-4® based on the Exact Perturbation Theory allows very small reactivity perturbations to be computed accurately as well as reactivity effect to be estimated on distinct isotopes cross-sections. In the first part of this paper, this new feature of the code is described and then applied in the second part to a core configuration composed of several layers of fuel and fertile zones below a sodium plenum. Reactivity and its contributions from specific reactions and energy groups are calculated and compared with the results of the deterministic code ERANOS. The aim of this work is twofold: (1) Achieve a numerical validation of the new TRIPOLI-4® features and (2) Identify where deterministic codes might be less accurate and why – even when using them at full capacity (S16
Energy Technology Data Exchange (ETDEWEB)
McGraw, David [Desert Research Inst. (DRI), Reno, NV (United States); Hershey, Ronald L. [Desert Research Inst. (DRI), Reno, NV (United States)
2016-06-01
Methods were developed to quantify uncertainty and sensitivity for NETPATH inverse water-rock reaction models and to calculate dissolved inorganic carbon, carbon-14 groundwater travel times. The NETPATH models calculate upgradient groundwater mixing fractions that produce the downgradient target water chemistry along with amounts of mineral phases that are either precipitated or dissolved. Carbon-14 groundwater travel times are calculated based on the upgradient source-water fractions, carbonate mineral phase changes, and isotopic fractionation. Custom scripts and statistical code were developed for this study to facilitate modifying input parameters, running the NETPATH simulations, extracting relevant output, postprocessing the results, and producing graphs and summaries. The scripts read userspecified values for each constituent’s coefficient of variation, distribution, sensitivity parameter, maximum dissolution or precipitation amounts, and number of Monte Carlo simulations. Monte Carlo methods for analysis of parametric uncertainty assign a distribution to each uncertain variable, sample from those distributions, and evaluate the ensemble output. The uncertainty in input affected the variability of outputs, namely source-water mixing, phase dissolution and precipitation amounts, and carbon-14 travel time. Although NETPATH may provide models that satisfy the constraints, it is up to the geochemist to determine whether the results are geochemically reasonable. Two example water-rock reaction models from previous geochemical reports were considered in this study. Sensitivity analysis was also conducted to evaluate the change in output caused by a small change in input, one constituent at a time. Results were standardized to allow for sensitivity comparisons across all inputs, which results in a representative value for each scenario. The approach yielded insight into the uncertainty in water-rock reactions and travel times. For example, there was little
Maximum-likelihood absorption tomography
International Nuclear Information System (INIS)
Maximum-likelihood methods are applied to the problem of absorption tomography. The reconstruction is done with the help of an iterative algorithm. We show how the statistics of the illuminating beam can be incorporated into the reconstruction. The proposed reconstruction method can be considered as a useful alternative in the extreme cases where the standard ill-posed direct-inversion methods fail. (authors)
The applicability of certain Monte Carlo methods to the analysis of interacting polymers
Energy Technology Data Exchange (ETDEWEB)
Krapp, D.M. Jr. [Univ. of California, Berkeley, CA (United States)
1998-05-01
The authors consider polymers, modeled as self-avoiding walks with interactions on a hexagonal lattice, and examine the applicability of certain Monte Carlo methods for estimating their mean properties at equilibrium. Specifically, the authors use the pivoting algorithm of Madras and Sokal and Metroplis rejection to locate the phase transition, which is known to occur at {beta}{sub crit} {approx} 0.99, and to recalculate the known value of the critical exponent {nu} {approx} 0.58 of the system for {beta} = {beta}{sub crit}. Although the pivoting-Metropolis algorithm works well for short walks (N < 300), for larger N the Metropolis criterion combined with the self-avoidance constraint lead to an unacceptably small acceptance fraction. In addition, the algorithm becomes effectively non-ergodic, getting trapped in valleys whose centers are local energy minima in phase space, leading to convergence towards different values of {nu}. The authors use a variety of tools, e.g. entropy estimation and histograms, to improve the results for large N, but they are only of limited effectiveness. Their estimate of {beta}{sub crit} using smaller values of N is 1.01 {+-} 0.01, and the estimate for {nu} at this value of {beta} is 0.59 {+-} 0.005. They conclude that even a seemingly simple system and a Monte Carlo algorithm which satisfies, in principle, ergodicity and detailed balance conditions, can in practice fail to sample phase space accurately and thus not allow accurate estimations of thermal averages. This should serve as a warning to people who use Monte Carlo methods in complicated polymer folding calculations. The structure of the phase space combined with the algorithm itself can lead to surprising behavior, and simply increasing the number of samples in the calculation does not necessarily lead to more accurate results.
International Nuclear Information System (INIS)
We isolate the most computationally expensive steps of a robust nuclear reactor core Monte Carlo particle transport simulation. The hot kernel is then abstracted into a simplified proxy application, designed to mimic the key performance characteristics of the full application. A series of performance verification tests and analyses are carried out to investigate the low-level performance parameters of both the simplified kernel and the full application. The kernel's performance profile is found to closely match that of the application, making it a convenient test bed for performance analyses on cutting edge platforms and experimental next-generation high performance computing architectures. (author)
Application of direct simulation Monte Carlo method for analysis of AVLIS evaporation process
International Nuclear Information System (INIS)
The computation code of the direct simulation Monte Carlo (DSMC) method was developed in order to analyze the atomic vapor evaporation in atomic vapor laser isotope separation (AVLIS). The atomic excitation temperatures of gadolinium atom were calculated for the model with five low lying states. Calculation results were compared with the experiments obtained by laser absorption spectroscopy. Two types of DSMC simulations which were different in inelastic collision procedure were carried out. It was concluded that the energy transfer was forbidden unless the total energy of the colliding atoms exceeds a threshold value. (author)
Energy Technology Data Exchange (ETDEWEB)
Capizzo, M.C.; Persano Adorno, D.; Zarcone, M. [Dipartimento di Fisica e Tecnologie Relative, Viale delle Scienze, Ed. 18, 90128, Palermo (Italy)
2006-08-15
This paper reports the results of Monte Carlo simulations of electronic noise in a GaAs bulk driven by two mixed high-frequency large-amplitude periodic electric fields. Under these conditions, the system response shows some peculiarities in the noise performance, such as a resonant-like enhancement of the spectra near the two frequencies of the applied fields. The relations among the frequency response and the velocity fluctuations as a function of intensities and frequencies of the sub-terahertz mixed excitation fields have been investigated. (copyright 2006 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)
International Nuclear Information System (INIS)
The perturbation source method may be a powerful Monte-Carlo means to calculate small effects in a particle field. In a preceding paper we have formulated this methos in inhomogeneous linear particle transport problems describing the particle fields by solutions of Fredholm integral equations and have derived formulae for the second moment of the difference event point estimator. In the present paper we analyse the general structure of its variance, point out the variance peculiarities, discuss the dependence on certain transport games and on generation procedures of the auxiliary particles and draw conclusions to improve this method
Analysis of skin tissues spatial fluorescence distribution by the Monte Carlo simulation
Churmakov, D Y; Piletsky, S A; Greenhalgh, D A
2003-01-01
A novel Monte Carlo technique of simulation of spatial fluorescence distribution within the human skin is presented. The computational model of skin takes into account the spatial distribution of fluorophores, which would arise due to the structure of collagen fibres, compared to the epidermis and stratum corneum where the distribution of fluorophores is assumed to be homogeneous. The results of simulation suggest that distribution of auto- fluorescence is significantly suppressed in the near-infrared spectral region, whereas the spatial distribution of fluorescence sources within a sensor layer embedded in the epidermis is localized at an effective depth.
Random vibration analysis of switching apparatus based on Monte Carlo method
Institute of Scientific and Technical Information of China (English)
ZHAI Guo-fu; CHEN Ying-hua; REN Wan-bin
2007-01-01
The performance in vibration environment of switching apparatus containing mechanical contact is an important element when judging the apparatus's reliability. A piecewise linear two-degrees-of-freedom mathematical model considering contact loss was built in this work, and the vibration performance of the model under random external Gaussian white noise excitation was investigated by using Monte Carlo simulation in Matlab/Simulink. Simulation showed that the spectral content and statistical characters of the contact force coincided strongly with reality. The random vibration character of the contact system was solved using time (numerical) domain simulation in this paper. Conclusions reached here are of great importance for reliability design of switching apparatus.
Dimension-independent likelihood-informed MCMC
Cui, Tiangang
2015-10-08
Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters that represent the discretization of an underlying function. This work introduces a family of Markov chain Monte Carlo (MCMC) samplers that can adapt to the particular structure of a posterior distribution over functions. Two distinct lines of research intersect in the methods developed here. First, we introduce a general class of operator-weighted proposal distributions that are well defined on function space, such that the performance of the resulting MCMC samplers is independent of the discretization of the function. Second, by exploiting local Hessian information and any associated low-dimensional structure in the change from prior to posterior distributions, we develop an inhomogeneous discretization scheme for the Langevin stochastic differential equation that yields operator-weighted proposals adapted to the non-Gaussian structure of the posterior. The resulting dimension-independent and likelihood-informed (DILI) MCMC samplers may be useful for a large class of high-dimensional problems where the target probability measure has a density with respect to a Gaussian reference measure. Two nonlinear inverse problems are used to demonstrate the efficiency of these DILI samplers: an elliptic PDE coefficient inverse problem and path reconstruction in a conditioned diffusion.
Idiri, Z.; Mazrou, H.; Beddek, S.; Amokrane, A.; Azbouche, A.
2007-07-01
The present paper describes the optimization of sample dimensions of a 241Am-Be neutron source-based Prompt gamma neutron activation analysis (PGNAA) setup devoted for in situ environmental water rejects analysis. The optimal dimensions have been achieved following extensive Monte Carlo neutron flux calculations using MCNP5 computer code. A validation process has been performed for the proposed preliminary setup with measurements of thermal neutron flux by activation technique of indium foils, bare and with cadmium covered sheet. Sensitive calculations were subsequently performed to simulate real conditions of in situ analysis by determining thermal neutron flux perturbations in samples according to chlorine and organic matter concentrations changes. The desired optimal sample dimensions were finally achieved once established constraints regarding neutron damage to semi-conductor gamma detector, pulse pile-up, dead time and radiation hazards were fully met.
Energy Technology Data Exchange (ETDEWEB)
Dupuis, Paul [Brown University
2014-03-14
This proposal is concerned with applications of Monte Carlo to problems in physics and chemistry where rare events degrade the performance of standard Monte Carlo. One class of problems is concerned with computation of various aspects of the equilibrium behavior of some Markov process via time averages. The problem to be overcome is that rare events interfere with the efficient sampling of all relevant parts of phase space. A second class concerns sampling transitions between two or more stable attractors. Here, rare events do not interfere with the sampling of all relevant parts of phase space, but make Monte Carlo inefficient because of the very large number of samples required to obtain variance comparable to the quantity estimated. The project uses large deviation methods for the mathematical analyses of various Monte Carlo techniques, and in particular for algorithmic analysis and design. This is done in the context of relevant application areas, mainly from chemistry and biology.
O'Hagan, Anthony; Stevenson, Matt; Madan, Jason
2007-10-01
Probabilistic sensitivity analysis (PSA) is required to account for uncertainty in cost-effectiveness calculations arising from health economic models. The simplest way to perform PSA in practice is by Monte Carlo methods, which involves running the model many times using randomly sampled values of the model inputs. However, this can be impractical when the economic model takes appreciable amounts of time to run. This situation arises, in particular, for patient-level simulation models (also known as micro-simulation or individual-level simulation models), where a single run of the model simulates the health care of many thousands of individual patients. The large number of patients required in each run to achieve accurate estimation of cost-effectiveness means that only a relatively small number of runs is possible. For this reason, it is often said that PSA is not practical for patient-level models. We develop a way to reduce the computational burden of Monte Carlo PSA for patient-level models, based on the algebra of analysis of variance. Methods are presented to estimate the mean and variance of the model output, with formulae for determining optimal sample sizes. The methods are simple to apply and will typically reduce the computational demand very substantially.
Sanattalab, Ehsan; SalmanOgli, Ahmad; Piskin, Erhan
2016-04-01
We investigated the tumor-targeted nanoparticles that influence heat generation. We suppose that all nanoparticles are fully functionalized and can find the target using active targeting methods. Unlike the commonly used methods, such as chemotherapy and radiotherapy, the treatment procedure proposed in this study is purely noninvasive, which is considered to be a significant merit. It is found that the localized heat generation due to targeted nanoparticles is significantly higher than other areas. By engineering the optical properties of nanoparticles, including scattering, absorption coefficients, and asymmetry factor (cosine scattering angle), the heat generated in the tumor's area reaches to such critical state that can burn the targeted tumor. The amount of heat generated by inserting smart agents, due to the surface Plasmon resonance, will be remarkably high. The light-matter interactions and trajectory of incident photon upon targeted tissues are simulated by MIE theory and Monte Carlo method, respectively. Monte Carlo method is a statistical one by which we can accurately probe the photon trajectories into a simulation area.
Monte Carlo analysis of pion contribution to absorbed dose from Galactic cosmic rays
Energy Technology Data Exchange (ETDEWEB)
Aghara, S.K. [Prairie View A and M University, Chemical Engineering (Nuclear Program), P.O. Box 519, MS 2505, Prairie View, TX 77446 (United States)], E-mail: Sukesh.K.Aghara@nasa.gov; Blattnig, S.R.; Norbury, J.W.; Singleterry, R.C. [NASA Langley Research Center, Hampton, VA 23681 (United States)
2009-04-15
Accurate knowledge of the physics of interaction, particle production and transport is necessary to estimate the radiation damage to equipment used on spacecraft and the biological effects of space radiation. For long duration astronaut missions, both on the International Space Station and the planned manned missions to Moon and Mars, the shielding strategy must include a comprehensive knowledge of the secondary radiation environment. The distribution of absorbed dose and dose equivalent is a function of the type, energy and population of these secondary products. Galactic cosmic rays (GCR) comprised of protons and heavier nuclei have energies from a few MeV per nucleon to the ZeV region, with the spectra reaching flux maxima in the hundreds of MeV range. Therefore, the MeV-GeV region is most important for space radiation. Coincidentally, the pion production energy threshold is about 280 MeV. The question naturally arises as to how important these particles are with respect to space radiation problems. The space radiation transport code, HZETRN (High charge (Z) and Energy TRaNsport), currently used by NASA, performs neutron, proton and heavy ion transport explicitly, but it does not take into account the production and transport of mesons, photons and leptons. In this paper, we present results from the Monte Carlo code MCNPX (Monte Carlo N-Particle eXtended), showing the effect of leptons and mesons when they are produced and transported in a GCR environment.
Monte Carlo Analysis of Pion Contribution to Absorbed Dose from Galactic Cosmic Rays
Aghara, S.K.; Battnig, S.R.; Norbury, J.W.; Singleterry, R.C.
2009-01-01
Accurate knowledge of the physics of interaction, particle production and transport is necessary to estimate the radiation damage to equipment used on spacecraft and the biological effects of space radiation. For long duration astronaut missions, both on the International Space Station and the planned manned missions to Moon and Mars, the shielding strategy must include a comprehensive knowledge of the secondary radiation environment. The distribution of absorbed dose and dose equivalent is a function of the type, energy and population of these secondary products. Galactic cosmic rays (GCR) comprised of protons and heavier nuclei have energies from a few MeV per nucleon to the ZeV region, with the spectra reaching flux maxima in the hundreds of MeV range. Therefore, the MeV - GeV region is most important for space radiation. Coincidentally, the pion production energy threshold is about 280 MeV. The question naturally arises as to how important these particles are with respect to space radiation problems. The space radiation transport code, HZETRN (High charge (Z) and Energy TRaNsport), currently used by NASA, performs neutron, proton and heavy ion transport explicitly, but it does not take into account the production and transport of mesons, photons and leptons. In this paper, we present results from the Monte Carlo code MCNPX (Monte Carlo N-Particle eXtended), showing the effect of leptons and mesons when they are produced and transported in a GCR environment.
Monte Carlo analysis of pion contribution to absorbed dose from Galactic cosmic rays
Aghara, S. K.; Blattnig, S. R.; Norbury, J. W.; Singleterry, R. C.
2009-04-01
Accurate knowledge of the physics of interaction, particle production and transport is necessary to estimate the radiation damage to equipment used on spacecraft and the biological effects of space radiation. For long duration astronaut missions, both on the International Space Station and the planned manned missions to Moon and Mars, the shielding strategy must include a comprehensive knowledge of the secondary radiation environment. The distribution of absorbed dose and dose equivalent is a function of the type, energy and population of these secondary products. Galactic cosmic rays (GCR) comprised of protons and heavier nuclei have energies from a few MeV per nucleon to the ZeV region, with the spectra reaching flux maxima in the hundreds of MeV range. Therefore, the MeV-GeV region is most important for space radiation. Coincidentally, the pion production energy threshold is about 280 MeV. The question naturally arises as to how important these particles are with respect to space radiation problems. The space radiation transport code, HZETRN (High charge (Z) and Energy TRaNsport), currently used by NASA, performs neutron, proton and heavy ion transport explicitly, but it does not take into account the production and transport of mesons, photons and leptons. In this paper, we present results from the Monte Carlo code MCNPX (Monte Carlo N-Particle eXtended), showing the effect of leptons and mesons when they are produced and transported in a GCR environment.
DEFF Research Database (Denmark)
Cherchi, Elisabetta; Guevara, Cristian
2012-01-01
a series of Monte Carlo experiments, evidence suggested four main conclusions: (a) efficiency increased when the true variance-covariance matrix became diagonal, (b) EM was more robust to the curse of dimensionality in regard to efficiency and estimation time, (c) EM did not recover the true scale...... simulated likelihood (MSL) method is compared with the alternative expectation- maximization (EM) method, which does not require simulation. Previous literature had shown that for cross-sectional data, MSL outperforms the EM method in the ability to recover the true parameters and estimation time and that...... EM has more difficulty in recovering the true scale of the coefficients. In this paper, the analysis is extended from cross-sectional data to the less volatile case of panel data to explore the effect on the relative performance of the methods with several realizations of the random coefficients. In...
Recent developments in maximum likelihood estimation of MTMM models for categorical data
Directory of Open Access Journals (Sweden)
Minjeong eJeon
2014-04-01
Full Text Available Maximum likelihood (ML estimation of categorical multitrait-multimethod (MTMM data is challenging because the likelihood involves high-dimensional integrals over the crossed method and trait factors, with no known closed-form solution.The purpose of the study is to introduce three newly developed ML methods that are eligible for estimating MTMM models with categorical responses: Variational maximization-maximization, Alternating imputation posterior, and Monte Carlo local likelihood. Each method is briefly described and its applicability for MTMM models with categorical data are discussed.An illustration is provided using an empirical example.
Uncertainty Propagation Analysis for the Monte Carlo Time-Dependent Simulations
Energy Technology Data Exchange (ETDEWEB)
Shaukata, Nadeem; Shim, Hyung Jin [Seoul National University, Seoul (Korea, Republic of)
2015-10-15
In this paper, a conventional method to control the neutron population for super-critical systems is implemented. Instead of considering the cycles, the simulation is divided in time intervals. At the end of each time interval, neutron population control is applied on the banked neutrons. Randomly selected neutrons are discarded, until the size of neutron population matches the initial neutron histories at the beginning of time simulation. A time-dependent simulation mode has also been implemented in the development version of SERPENT 2 Monte Carlo code. In this mode, sequential population control mechanism has been proposed for modeling of prompt super-critical systems. A Monte Carlo method has been properly used in TART code for dynamic criticality calculations. For super-critical systems, the neutron population is allowed to grow over a period of time. The neutron population is uniformly combed to return it to the neutron population started with at the beginning of time boundary. In this study, conventional time-dependent Monte Carlo (TDMC) algorithm is implemented. There is an exponential growth of neutron population in estimation of neutron density tally for super-critical systems and the number of neutrons being tracked exceed the memory of the computer. In order to control this exponential growth at the end of each time boundary, a conventional time cut-off controlling population strategy is included in TDMC. A scale factor is introduced to tally the desired neutron density at the end of each time boundary. The main purpose of this paper is the quantification of uncertainty propagation in neutron densities at the end of each time boundary for super-critical systems. This uncertainty is caused by the uncertainty resulting from the introduction of scale factor. The effectiveness of TDMC is examined for one-group infinite homogeneous problem (the rod model) and two-group infinite homogeneous problem. The desired neutron density is tallied by the introduction of
Likelihood Functions for Galaxy Cluster Surveys
Holder, G
2006-01-01
Galaxy cluster surveys offer great promise for measuring cosmological parameters, but survey analysis methods have not been widely studied. Using methods developed decades ago for galaxy clustering studies, it is shown that nearly exact likelihood functions can be written down for galaxy cluster surveys. The sparse sampling of the density field by galaxy clusters allows simplifications that are not possible for galaxy surveys. An application to counts in cells is explicitly tested using cluster catalogs from numerical simulations and it is found that the calculated probability distributions are very accurate at masses above several times 10^{14}h^{-1} solar masses at z=0 and lower masses at higher redshift.
Ishisaki, Y; Fujimoto, R; Ozaki, M; Ebisawa, K; Takahashi, T; Ueda, Y; Ogasaka, Y; Ptak, A; Mukai, K; Hamaguchi, K; Hirayama, M; Kotani, T; Kubo, H; Shibata, R; Ebara, M; Furuzawa, A; Iizuka, R; Inoue, H; Mori, H; Okada, S; Yokoyama, Y; Matsumoto, H; Nakajima, H; Yamaguchi, H; Anabuki, N; Tawa, N; Nagai, M; Katsuda, S; Hayashida, K; Bamba, A; Miller, E D; Sato, K; Yamasaki, N Y
2006-01-01
We have developed a framework for the Monte-Carlo simulation of the X-Ray Telescopes (XRT) and the X-ray Imaging Spectrometers (XIS) onboard Suzaku, mainly for the scientific analysis of spatially and spectroscopically complex celestial sources. A photon-by-photon instrumental simulator is built on the ANL platform, which has been successfully used in ASCA data analysis. The simulator has a modular structure, in which the XRT simulation is based on a ray-tracing library, while the XIS simulation utilizes a spectral "Redistribution Matrix File" (RMF), generated separately by other tools. Instrumental characteristics and calibration results, e.g., XRT geometry, reflectivity, mutual alignments, thermal shield transmission, build-up of the contamination on the XIS optical blocking filters (OBF), are incorporated as completely as possible. Most of this information is available in the form of the FITS (Flexible Image Transport System) files in the standard calibration database (CALDB). This simulator can also be ut...
Likelihood estimators for multivariate extremes
Huser, Raphael Georges
2015-11-17
The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.
Nonlinear Stochastic stability analysis of Wind Turbine Wings by Monte Carlo Simulations
DEFF Research Database (Denmark)
Larsen, Jesper Winther; Iwankiewiczb, R.; Nielsen, Søren R.K.
2007-01-01
and inertial contributions. A reduced two-degrees-of-freedom modal expansion is used specifying the modal coordinate of the fundamental blade and edgewise fixed base eigenmodes of the beam. The rotating beam is subjected to harmonic and narrow-banded support point motion from the nacelle displacement...... under narrow-banded excitation, and it is shown that the qualitative behaviour of the strange attractor is very similar for the periodic and almost periodic responses, whereas the strange attractor for the chaotic case loses structure as the excitation becomes narrow-banded. Furthermore......, the characteristic behaviour of the strange attractor is shown to be identifiable by the so-called information dimension. Due to the complexity of the coupled nonlinear structural system all analyses are carried out via Monte Carlo simulations....
Analysis of aerial survey data on Florida manatee using Markov chain Monte Carlo.
Craig, B A; Newton, M A; Garrott, R A; Reynolds, J E; Wilcox, J R
1997-06-01
We assess population trends of the Atlantic coast population of Florida manatee, Trichechus manatus latirostris, by reanalyzing aerial survey data collected between 1982 and 1992. To do so, we develop an explicit biological model that accounts for the method by which the manatees are counted, the mammals' movement between surveys, and the behavior of the population total over time. Bayesian inference, enabled by Markov chain Monte Carlo, is used to combine the survey data with the biological model. We compute marginal posterior distributions for all model parameters and predictive distributions for future counts. Several conclusions, such as a decreasing population growth rate and low sighting probabilities, are consistent across different prior specifications. PMID:9192449
Analysis of Light Transport Features in Stone Fruits Using Monte Carlo Simulation.
Directory of Open Access Journals (Sweden)
Chizhu Ding
Full Text Available The propagation of light in stone fruit tissue was modeled using the Monte Carlo (MC method. Peaches were used as the representative model of stone fruits. The effects of the fruit core and the skin on light transport features in the peaches were assessed. It is suggested that the skin, flesh and core should be separately considered with different parameters to accurately simulate light propagation in intact stone fruit. The detection efficiency was evaluated by the percentage of effective photons and the detection sensitivity of the flesh tissue. The fruit skin decreases the detection efficiency, especially in the region close to the incident point. The choices of the source-detector distance, detection angle and source intensity were discussed. Accurate MC simulations may result in better insight into light propagation in stone fruit and aid in achieving the optimal fruit quality inspection without extensive experimental measurements.
Calculation and analysis of heat source of PWR assemblies based on Monte Carlo method
International Nuclear Information System (INIS)
When fission occurs in nuclear fuel in reactor core, it releases numerous neutron and γ radiation, which takes energy deposition in fuel components and yields many factors such as thermal stressing and radiation damage influencing the safe operation of a reactor. Using the three-dimensional Monte Carlo transport calculation program MCNP and continuous cross-section database based on ENDF/B series to calculate the heat rate of the heat source on reference assemblies of a PWR when loading with 18-month short refueling cycle mode, and get the precise values of the control rod, thimble plug and new burnable poison rod within Gd, so as to provide basis for reactor design and safety verification. (authors)
Improving PWR core simulations by Monte Carlo uncertainty analysis and Bayesian inference
Castro, Emilio; Buss, Oliver; Garcia-Herranz, Nuria; Hoefer, Axel; Porsch, Dieter
2016-01-01
A Monte Carlo-based Bayesian inference model is applied to the prediction of reactor operation parameters of a PWR nuclear power plant. In this non-perturbative framework, high-dimensional covariance information describing the uncertainty of microscopic nuclear data is combined with measured reactor operation data in order to provide statistically sound, well founded uncertainty estimates of integral parameters, such as the boron letdown curve and the burnup-dependent reactor power distribution. The performance of this methodology is assessed in a blind test approach, where we use measurements of a given reactor cycle to improve the prediction of the subsequent cycle. As it turns out, the resulting improvement of the prediction quality is impressive. In particular, the prediction uncertainty of the boron letdown curve, which is of utmost importance for the planning of the reactor cycle length, can be reduced by one order of magnitude by including the boron concentration measurement information of the previous...
Directory of Open Access Journals (Sweden)
Sabitha Gauni
2014-03-01
Full Text Available In the field of Wireless Communication, there is always a demand for reliability, improved range and speed. Many wireless networks such as OFDM, CDMA2000, WCDMA etc., provide a solution to this problem when incorporated with Multiple input- multiple output (MIMO technology. Due to the complexity in signal processing, MIMO is highly expensive in terms of area consumption. In this paper, a method of MIMO receiver design is proposed to reduce the area consumed by the processing elements involved in complex signal processing. In this paper, a solution for area reduction in the Multiple input multiple output(MIMO Maximum Likelihood Receiver(MLE using Sorted QR Decomposition and Unitary transformation method is analyzed. It provides unified approach and also reduces ISI and provides better performance at low cost. The receiver pre-processor architecture based on Minimum Mean Square Error (MMSE is compared while using Iterative SQRD and Unitary transformation method for vectoring. Unitary transformations are transformations of the matrices which maintain the Hermitian nature of the matrix, and the multiplication and addition relationship between the operators. This helps to reduce the computational complexity significantly. The dynamic range of all variables is tightly bound and the algorithm is well suited for fixed point arithmetic.
Hallman, D M; Visvikis, S; Steinmetz, J; Boerwinkle, E
1994-01-01
A new method is described for employing family data to test for significant haplotype effects on continuously distributed variables, using likelihood-ratio tests of linear models in which haplotype effects are parameterized and familial correlations taken into account. The method is applied to the apolipoprotein B (Apo B) gene, using 5 polymorphisms (Insertion/deletion, Bsp1286I, XbaI, MspI, EcoRI) to define haplotypes in 121 French nuclear families. Eleven haplotypes were found, five of which, combined, account for over 95% of the sample. A haplotype phylogeny is proposed, and is used to define a nested set of models for testing the effects of Apo B variation on total-, low-density-lipoprotein (LDL)-, and high-density-lipoprotein (HDL)-cholesterol, triglyceride, and Apo B levels. Apo B haplotype effects account for about 10% of the genetic variance and 5% of the total variance in HDL-cholesterol and triglyceride levels. Clusters of evolutionarily-related haplotypes with similar phenotypic effects are identified for HDL-cholesterol and triglycerides. Single haplotypes with statistically significant effects are identified for cholesterol, LDL-cholesterol, and Apo B levels.
International Nuclear Information System (INIS)
The criticality analysis of the TRIGA-II benchmark experiment at the Musashi Institute of Technology Research Reactor (MuITR, 100kW) was performed by the three-dimensional continuous-energy Monte Carlo code (MCNP4A). To minimize errors due to an inexact geometry model, all fresh fuels and control rods as well as vicinity of the core were precisely modeled. Effective multiplication factors (keff) in the initial core critical experiment and in the excess reactivity adjustment for the several fuel-loading patterns as well as the fuel element reactivity worth distributions were used in the validation process of the physical model and neutron cross section data from the ENDF/B-V evaluation. The calculated keff overestimated the experimental data by about 1.0%Δk/k for both the initial core and the several fuel-loading arrangements (fuels or graphite elements were added only to the outer-ring), but the discrepancy increased to 1.8%Δk/k for the some fuel-loading patterns (graphite elements were inserted into the inner-ring). The comparison result of the fuel element worth distribution showed above tendency. All in all, the agreement between the MCNP predictions and the experimentally determined values is good, which indicates that the Monte Carlo model is enough to simulate criticality of the TRIGA-II reactor. (author)
Institute of Scientific and Technical Information of China (English)
TIAN Bao-guo; SI Ji-tao; ZHAO Yan; WANG Hong-tao; HAO Ji-ming
2007-01-01
This paper deals with the procedure and methodology which can be used to select the optimal treatment and disposal technology of municipal solid waste (MSW), and to provide practical and effective technical support to policy-making, on the basis of study on solid waste management status and development trend in China and abroad. Focusing on various treatment and disposal technologies and processes of MSW, this study established a Monte-Carlo mathematical model of cost minimization for MSW handling subjected to environmental constraints. A new method of element stream (such as C, H, O, N, S) analysis in combination with economic stream analysis of MSW was developed. By following the streams of different treatment processes consisting of various techniques from generation, separation, transfer, transport, treatment, recycling and disposal of the wastes, the element constitution as well as its economic distribution in terms of possibility functions was identified. Every technique step was evaluated economically. The Mont-Carlo method was then conducted for model calibration. Sensitivity analysis was also carried out to identify the most sensitive factors. Model calibration indicated that landfill with power generation of landfill gas was economically the optimal technology at the present stage under the condition of more than 58% of C, H, O, N, S going to landfill. Whether or not to generate electricity was the most sensitive factor. If landfilling cost increases, MSW separation treatment was recommended by screening first followed with incinerating partially and composting partially with residue landfilling. The possibility of incineration model selection as the optimal technology was affected by the city scale. For big cities and metropolitans with large MSW generation, possibility for constructing large-scale incineration facilities increases, whereas, for middle and small cities, the effectiveness of incinerating waste decreases.
Tian, Bao-Guo; Si, Ji-Tao; Zhao, Yan; Wang, Hong-Tao; Hao, Ji-Ming
2007-01-01
This paper deals with the procedure and methodology which can be used to select the optimal treatment and disposal technology of municipal solid waste (MSW), and to provide practical and effective technical support to policy-making, on the basis of study on solid waste management status and development trend in China and abroad. Focusing on various treatment and disposal technologies and processes of MSW, this study established a Monte-Carlo mathematical model of cost minimization for MSW handling subjected to environmental constraints. A new method of element stream (such as C, H, O, N, S) analysis in combination with economic stream analysis of MSW was developed. By following the streams of different treatment processes consisting of various techniques from generation, separation, transfer, transport, treatment, recycling and disposal of the wastes, the element constitution as well as its economic distribution in terms of possibility functions was identified. Every technique step was evaluated economically. The Mont-Carlo method was then conducted for model calibration. Sensitivity analysis was also carried out to identify the most sensitive factors. Model calibration indicated that landfill with power generation of landfill gas was economically the optimal technology at the present stage under the condition of more than 58% of C, H, O, N, S going to landfill. Whether or not to generate electricity was the most sensitive factor. If landfilling cost increases, MSW separation treatment was recommended by screening first followed with incinerating partially and composting partially with residue landfilling. The possibility of incineration model selection as the optimal technology was affected by the city scale. For big cities and metropolitans with large MSW generation, possibility for constructing large-scale incineration facilities increases, whereas, for middle and small cities, the effectiveness of incinerating waste decreases. PMID:17915696
International Nuclear Information System (INIS)
Our aim was to evaluate the sensitivity and uncertainty of mass flow rate in the core on the performance of natural circulation boiling water reactor (NCBWR). This analysis was carried out through Monte Carlo simulations of sizes up to 40,000, and the size, i.e., repetition of 25,000 was considered as valid for routine applications. A simplified boiling water reactor (SBWR) was used as an application example of Monte Carlo method. The numerical code to simulate the SBWR performance considers a one-dimensional thermo-hydraulics model along with non-equilibrium thermodynamics and non-homogeneous flow approximation, one-dimensional fuel rod heat transfer. The neutron processes were simulated with a point reactor kinetics model with six groups of delayed neutrons. The sensitivity was evaluated in terms of 99% confidence intervals of the mean to understand the range of mean values that may represent the entire statistical population of performance variables. The regression analysis with mass flow rate as the predictor variable showed statistically valid linear correlations for both neutron flux and fuel temperature and quadratic relationship for the void fraction. No statistically valid correlation was observed for the total heat flux as a function of the mass flow rate although heat flux at individual nodes was positively correlated with this variable. These correlations are useful for the study, analysis and design of any NCBWR. The uncertainties were propagated as follows: for 10% change in the mass flow rate in the core, the responses for neutron power, total heat flux, average fuel temperature and average void fraction changed by 8.74%, 7.77%, 2.74% and 0.58%, respectively.
Estimation for Non-Gaussian Locally Stationary Processes with Empirical Likelihood Method
Directory of Open Access Journals (Sweden)
Hiroaki Ogata
2012-01-01
Full Text Available An application of the empirical likelihood method to non-Gaussian locally stationary processes is presented. Based on the central limit theorem for locally stationary processes, we give the asymptotic distributions of the maximum empirical likelihood estimator and the empirical likelihood ratio statistics, respectively. It is shown that the empirical likelihood method enables us to make inferences on various important indices in a time series analysis. Furthermore, we give a numerical study and investigate a finite sample property.
Directory of Open Access Journals (Sweden)
Abraão Freires Saraiva Júnior
2011-03-01
Full Text Available The use of mathematical and statistical methods can help managers to deal with decision-making difficulties in the business environment. Some of these decisions are related to productive capacity optimization in order to obtain greater economic gains for the company. Within this perspective, this study aims to present the establishment of metrics to support economic decisions related to process or not orders in a company whose products have great variability in variable direct costs per unit that generates accounting uncertainties. To achieve this objective, is proposed a five-step method built from the integration of Management Accounting and Operations Research techniques, emphasizing the Monte Carlo simulation. The method is applied from a didactic example which uses real data achieved through a field research carried out in a plastic products industry that employ recycled material. Finally, it is concluded that the Monte Carlo simulation is effective for treating variable direct costs per unit variability and that the proposed method is useful to support decision-making related to order acceptance.A utilização de métodos matemáticos e estatísticos pode auxiliar gestores a lidar com dificuldades do processo de tomada de decisão no ambiente de negócios. Algumas dessas decisões estão relacionadas à otimização da utilização da capacidade produtiva visando a obtenção de melhores resultados econômicos para a empresa. Dentro dessa perspectiva, o presente trabalho objetiva apresentar o estabelecimento de métricas que deem suporte à decisão econômica de atender ou não a pedidos em uma empresa cujos produtos têm grande variabilidade de custos variáveis diretos unitários que gera incertezas contábeis. Para cumprir esse objetivo, é proposto um método em cinco etapas, construído a partir da integração de técnicas provindas da contabilidade gerencial e da pesquisa operacional, com destaque à simulação de Monte Carlo. O m
International Nuclear Information System (INIS)
Understanding the dynamics of neural networks is a major challenge in experimental neuroscience. For that purpose, a modelling of the recorded activity that reproduces the main statistics of the data is required. In the first part, we present a review on recent results dealing with spike train statistics analysis using maximum entropy models (MaxEnt). Most of these studies have focused on modelling synchronous spike patterns, leaving aside the temporal dynamics of the neural activity. However, the maximum entropy principle can be generalized to the temporal case, leading to Markovian models where memory effects and time correlations in the dynamics are properly taken into account. In the second part, we present a new method based on Monte Carlo sampling which is suited for the fitting of large-scale spatio-temporal MaxEnt models. The formalism and the tools presented here will be essential to fit MaxEnt spatio-temporal models to large neural ensembles. (paper)
International Nuclear Information System (INIS)
A Monte-Carlo model for the emergence of scintillation-detector-signals is presented that allows for the prediction of certain scintillator-photomultiplier combinations' time and energy resolutions while relying primarily on their basic data-sheet properties like light yield, decay time, quantum efficiency, and transit time spread. At the same time the model provides a deeper understanding of the performance limiting factors and stimulates the development of improved methods for the analysis of detector output signals. The simulation results are compared to high-speed digitizer measurements of signals from a number of widely used scintillation materials like LYSO, BaF2, LaBr3, NaI, and others.
Analysis of the KANT experiment on beryllium using TRIPOLI-4 Monte Carlo code
International Nuclear Information System (INIS)
Beryllium is an important material in fusion technology for multiplying neutrons in blankets. However, beryllium nuclear data are differently presented in modern nuclear data evaluations. Recent investigations with the TRIPOLI-4 Monte Carlo simulation of the tritium breeding ratio (TBR) demonstrated that beryllium reaction data are the main source of the calculation uncertainties between ENDF/B-VII.0 and JEFF-3.1. To clarify the calculation uncertainties from data libraries on beryllium, in this study TRIPOLI-4 calculations of the Karlsruhe Neutron Transmission (KANT) experiment have been performed by using ENDF/B-VII.0 and new JEFF-3.1.1 data libraries. The KANT Experiment on beryllium has been used to validate neutron transport codes and nuclear data libraries. An elaborated KANT experiment benchmark has been compiled and published in the NEA/SINBAD database and it has been used as reference in the present work. The neutron multiplication in bulk beryllium assemblies was considered with a central D-T neutron source. Neutron leakage spectra through the 5, 10, and 17 cm thick spherical beryllium shells were calculated and five-group partial leakage multiplications were reported and discussed. In general, improved C/E ratios on neutron leakage multiplications have been obtained. Both ENDF/B-VII.0 and JEFF-3.1.1 beryllium data libraries of TRIPOLI-4 are acceptable now for fusion neutronics calculations.
Markov chain Monte Carlo based analysis of post-translationally modified VDAC gating kinetics.
Tewari, Shivendra G; Zhou, Yifan; Otto, Bradley J; Dash, Ranjan K; Kwok, Wai-Meng; Beard, Daniel A
2014-01-01
The voltage-dependent anion channel (VDAC) is the main conduit for permeation of solutes (including nucleotides and metabolites) of up to 5 kDa across the mitochondrial outer membrane (MOM). Recent studies suggest that VDAC activity is regulated via post-translational modifications (PTMs). Yet the nature and effect of these modifications is not understood. Herein, single channel currents of wild-type, nitrosated, and phosphorylated VDAC are analyzed using a generalized continuous-time Markov chain Monte Carlo (MCMC) method. This developed method describes three distinct conducting states (open, half-open, and closed) of VDAC activity. Lipid bilayer experiments are also performed to record single VDAC activity under un-phosphorylated and phosphorylated conditions, and are analyzed using the developed stochastic search method. Experimental data show significant alteration in VDAC gating kinetics and conductance as a result of PTMs. The effect of PTMs on VDAC kinetics is captured in the parameters associated with the identified Markov model. Stationary distributions of the Markov model suggest that nitrosation of VDAC not only decreased its conductance but also significantly locked VDAC in a closed state. On the other hand, stationary distributions of the model associated with un-phosphorylated and phosphorylated VDAC suggest a reversal in channel conformation from relatively closed state to an open state. Model analyses of the nitrosated data suggest that faster reaction of nitric oxide with Cys-127 thiol group might be responsible for the biphasic effect of nitric oxide on basal VDAC conductance. PMID:25628567
Markov chain Monte Carlo based analysis of post-translationally modified VDAC1 gating kinetics
Directory of Open Access Journals (Sweden)
Shivendra eTewari
2015-01-01
Full Text Available The voltage-dependent anion channel (VDAC is the main conduit for permeation of solutes (including nucleotides and metabolites of up to 5 kDa across the mitochondrial outer membrane (MOM. Recent studies suggest that VDAC activity is regulated via post-translational modifications (PTMs. Yet the nature and effect of these modifications is not understood. Herein, single channel currents of wild-type, nitrosated and phosphorylated VDAC are analyzed using a generalized continuous-time Markov chain Monte Carlo (MCMC method. This developed method describes three distinct conducting states (open, half-open, and closed of VDAC1 activity. Lipid bilayer experiments are also performed to record single VDAC activity under un-phosphorylated and phosphorylated conditions, and are analyzed using the developed stochastic search method. Experimental data show significant alteration in VDAC gating kinetics and conductance as a result of PTMs. The effect of PTMs on VDAC kinetics is captured in the parameters associated with the identified Markov model. Stationary distributions of the Markov model suggests that nitrosation of VDAC not only decreased its conductance but also significantly locked VDAC in a closed state. On the other hand, stationary distributions of the model associated with un-phosphorylated and phosphorylated VDAC suggest a reversal in channel conformation from relatively closed state to an open state. Model analyses of the nitrosated data suggest that faster reaction of nitric oxide with Cys-127 thiol group might be responsible for the biphasic effect of nitric oxide on basal VDAC conductance.
Healey, S. P.; Patterson, P.; Garrard, C.
2014-12-01
Altered disturbance regimes are likely a primary mechanism by which a changing climate will affect storage of carbon in forested ecosystems. Accordingly, the National Forest System (NFS) has been mandated to assess the role of disturbance (harvests, fires, insects, etc.) on carbon storage in each of its planning units. We have developed a process which combines 1990-era maps of forest structure and composition with high-quality maps of subsequent disturbance type and magnitude to track the impact of disturbance on carbon storage. This process, called the Forest Carbon Management Framework (ForCaMF), uses the maps to apply empirically calibrated carbon dynamics built into a widely used management tool, the Forest Vegetation Simulator (FVS). While ForCaMF offers locally specific insights into the effect of historical or hypothetical disturbance trends on carbon storage, its dependence upon the interaction of several maps and a carbon model poses a complex challenge in terms of tracking uncertainty. Monte Carlo analysis is an attractive option for tracking the combined effects of error in several constituent inputs as they impact overall uncertainty. Monte Carlo methods iteratively simulate alternative values for each input and quantify how much outputs vary as a result. Variation of each input is controlled by a Probability Density Function (PDF). We introduce a technique called "PDF Weaving," which constructs PDFs that ensure that simulated uncertainty precisely aligns with uncertainty estimates that can be derived from inventory data. This hard link with inventory data (derived in this case from FIA - the US Forest Service Forest Inventory and Analysis program) both provides empirical calibration and establishes consistency with other types of assessments (e.g., habitat and water) for which NFS depends upon FIA data. Results from the NFS Northern Region will be used to illustrate PDF weaving and insights gained from ForCaMF about the role of disturbance in carbon
Tapered composite likelihood for spatial max-stable models
Sang, Huiyan
2014-05-01
Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.
Energy Technology Data Exchange (ETDEWEB)
Driscoll, Donald D.; /Case Western Reserve U.
2004-01-01
first use of a beta-eliminating cut based on a maximum-likelihood characterization described above.
Vozinaki, Anthi Eirini K.; Karatzas, George P.; Sibetheros, Ioannis A.; Varouchakis, Emmanouil A.
2014-05-01
Damage curves are the most significant component of the flood loss estimation models. Their development is quite complex. Two types of damage curves exist, historical and synthetic curves. Historical curves are developed from historical loss data from actual flood events. However, due to the scarcity of historical data, synthetic damage curves can be alternatively developed. Synthetic curves rely on the analysis of expected damage under certain hypothetical flooding conditions. A synthetic approach was developed and presented in this work for the development of damage curves, which are subsequently used as the basic input to a flood loss estimation model. A questionnaire-based survey took place among practicing and research agronomists, in order to generate rural loss data based on the responders' loss estimates, for several flood condition scenarios. In addition, a similar questionnaire-based survey took place among building experts, i.e. civil engineers and architects, in order to generate loss data for the urban sector. By answering the questionnaire, the experts were in essence expressing their opinion on how damage to various crop types or building types is related to a range of values of flood inundation parameters, such as floodwater depth and velocity. However, the loss data compiled from the completed questionnaires were not sufficient for the construction of workable damage curves; to overcome this problem, a Weighted Monte Carlo method was implemented, in order to generate extra synthetic datasets with statistical properties identical to those of the questionnaire-based data. The data generated by the Weighted Monte Carlo method were processed via Logistic Regression techniques in order to develop accurate logistic damage curves for the rural and the urban sectors. A Python-based code was developed, which combines the Weighted Monte Carlo method and the Logistic Regression analysis into a single code (WMCLR Python code). Each WMCLR code execution
Energy Technology Data Exchange (ETDEWEB)
Vega C, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Cipres No. 10, Fracc. La Penuela, 98068 Zacatecas (Mexico); Mendez V, R. [Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas, Av. Complutense 40, 28040 Madrid (Spain); Guzman G, K. A., E-mail: fermineutron@yahoo.com [Universidad Politecnica de Madrid, Departamento de Ingenieria Nuclear, C. Jose Gutierrez Abascal 2, 28006 Madrid (Spain)
2014-10-15
By means of Monte Carlo methods was characterized the neutrons field produced by calibration sources in the Neutron Standards Laboratory of the Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas (CIEMAT). The laboratory has two neutron calibration sources: {sup 241}AmBe and {sup 252}Cf which are stored in a water pool and are placed on the calibration bench using controlled systems at distance. To characterize the neutrons field was built a three-dimensional model of the room where it was included the stainless steel bench, the irradiation table and the storage pool. The sources model included double encapsulated of steel, as cladding. With the purpose of determining the effect that produces the presence of the different components of the room, during the characterization the neutrons spectra, the total flow and the rapidity of environmental equivalent dose to 100 cm of the source were considered. The presence of the walls, floor and ceiling of the room is causing the most modification in the spectra and the integral values of the flow and the rapidity of environmental equivalent dose. (Author)
International Nuclear Information System (INIS)
BOT3P consists of a set of standard Fortran 77 language programs that gives the users of the deterministic transport codes DORT, TORT, TWODANT, THREEDANT, PARTISN and the sensitivity code SUSD3D some useful diagnostic tools to prepare and check the geometry of their input data files for both Cartesian and cylindrical geometries, including graphical display modules. Users can produce the geometrical and material distribution data for all the cited codes for both two-dimensional and three-dimensional applications and, only in 3-dimensional Cartesian geometry, for the Monte Carlo Transport Code MCNP, starting from the same BOT3P input. Moreover, BOT3P stores the fine mesh arrays and the material zone map in a binary file, the content of which can be easily interfaced to any deterministic and Monte Carlo transport code. This makes it possible to compare directly for the same geometry the effects stemming from the use of different data libraries and solution approaches on transport analysis results. BOT3P Version 5.0 lets users optionally and with the desired precision compute the area/volume error of material zones with respect to the theoretical values, if any, because of the stair-cased representation of the geometry, and automatically update material densities on the whole zone domains to conserve masses. A local (per mesh) density correction approach is also available. BOT3P is designed to run on Linux/UNIX platforms and is publicly available from the Organization for Economic Cooperation and Development (OECD/NEA)/Nuclear Energy Agency Data Bank. Through the use of BOT3P, radiation transport problems with complex 3-dimensional geometrical structures can be modelled easily, as a relatively small amount of engineer-time is required and refinement is achieved by changing few parameters. This tool is useful for solving very large challenging problems, as successfully demonstrated not only in some complex neutron shielding and criticality benchmarks but also in a power
Lin, Chin; Chu, Chi-Ming; Su, Sui-Lung
2016-01-01
Conventional genome-wide association studies (GWAS) have been proven to be a successful strategy for identifying genetic variants associated with complex human traits. However, there is still a large heritability gap between GWAS and transitional family studies. The "missing heritability" has been suggested to be due to lack of studies focused on epistasis, also called gene-gene interactions, because individual trials have often had insufficient sample size. Meta-analysis is a common method for increasing statistical power. However, sufficient detailed information is difficult to obtain. A previous study employed a meta-regression-based method to detect epistasis, but it faced the challenge of inconsistent estimates. Here, we describe a Markov chain Monte Carlo-based method, called "Epistasis Test in Meta-Analysis" (ETMA), which uses genotype summary data to obtain consistent estimates of epistasis effects in meta-analysis. We defined a series of conditions to generate simulation data and tested the power and type I error rates in ETMA, individual data analysis and conventional meta-regression-based method. ETMA not only successfully facilitated consistency of evidence but also yielded acceptable type I error and higher power than conventional meta-regression. We applied ETMA to three real meta-analysis data sets. We found significant gene-gene interactions in the renin-angiotensin system and the polycyclic aromatic hydrocarbon metabolism pathway, with strong supporting evidence. In addition, glutathione S-transferase (GST) mu 1 and theta 1 were confirmed to exert independent effects on cancer. We concluded that the application of ETMA to real meta-analysis data was successful. Finally, we developed an R package, etma, for the detection of epistasis in meta-analysis [etma is available via the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/etma/index.html].
Lambert, Ronald J W; Mytilinaios, Ioannis; Maitland, Luke; Brown, Angus M
2012-08-01
This study describes a method to obtain parameter confidence intervals from the fitting of non-linear functions to experimental data, using the SOLVER and Analysis ToolPaK Add-In of the Microsoft Excel spreadsheet. Previously we have shown that Excel can fit complex multiple functions to biological data, obtaining values equivalent to those returned by more specialized statistical or mathematical software. However, a disadvantage of using the Excel method was the inability to return confidence intervals for the computed parameters or the correlations between them. Using a simple Monte-Carlo procedure within the Excel spreadsheet (without recourse to programming), SOLVER can provide parameter estimates (up to 200 at a time) for multiple 'virtual' data sets, from which the required confidence intervals and correlation coefficients can be obtained. The general utility of the method is exemplified by applying it to the analysis of the growth of Listeria monocytogenes, the growth inhibition of Pseudomonas aeruginosa by chlorhexidine and the further analysis of the electrophysiological data from the compound action potential of the rodent optic nerve.
Simulation and the Monte Carlo method
Rubinstein, Reuven Y
2016-01-01
Simulation and the Monte Carlo Method, Third Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simulation since the publication of the classic First Edition over more than a quarter of a century ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences. The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including: Markov Chain Monte Carlo, variance reduction techniques such as the transform likelihood ratio...
Lin, Chin; Chu, Chi-Ming; Su, Sui-Lung
2016-01-01
Conventional genome-wide association studies (GWAS) have been proven to be a successful strategy for identifying genetic variants associated with complex human traits. However, there is still a large heritability gap between GWAS and transitional family studies. The “missing heritability” has been suggested to be due to lack of studies focused on epistasis, also called gene–gene interactions, because individual trials have often had insufficient sample size. Meta-analysis is a common method for increasing statistical power. However, sufficient detailed information is difficult to obtain. A previous study employed a meta-regression-based method to detect epistasis, but it faced the challenge of inconsistent estimates. Here, we describe a Markov chain Monte Carlo-based method, called “Epistasis Test in Meta-Analysis” (ETMA), which uses genotype summary data to obtain consistent estimates of epistasis effects in meta-analysis. We defined a series of conditions to generate simulation data and tested the power and type I error rates in ETMA, individual data analysis and conventional meta-regression-based method. ETMA not only successfully facilitated consistency of evidence but also yielded acceptable type I error and higher power than conventional meta-regression. We applied ETMA to three real meta-analysis data sets. We found significant gene–gene interactions in the renin–angiotensin system and the polycyclic aromatic hydrocarbon metabolism pathway, with strong supporting evidence. In addition, glutathione S-transferase (GST) mu 1 and theta 1 were confirmed to exert independent effects on cancer. We concluded that the application of ETMA to real meta-analysis data was successful. Finally, we developed an R package, etma, for the detection of epistasis in meta-analysis [etma is available via the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/etma/index.html]. PMID:27045371
The Dynamic Monte Carlo Method for Transient Analysis of Nuclear Reactors
Sjenitzer, B.L.
2013-01-01
In this thesis a new method for the analysis of power transients in a nuclear reactor is developed, which is more accurate than the present state-of-the-art methods. Transient analysis is important tool when designing nuclear reactors, since they predict the behaviour of a reactor during changing co
Bayesian Analysis of Multivariate Probit Models
Siddhartha Chib; Edward Greenberg
1996-01-01
This paper provides a unified simulation-based Bayesian and non-Bayesian analysis of correlated binary data using the multivariate probit model. The posterior distribution is simulated by Markov chain Monte Carlo methods, and maximum likelihood estimates are obtained by a Markov chain Monte Carlo version of the E-M algorithm. Computation of Bayes factors from the simulation output is also considered. The methods are applied to a bivariate data set, to a 534-subject, four-year longitudinal dat...
Monte Carlo Error Analysis Applied to Core Formation: The Single-stage Model Revived
Cottrell, E.; Walter, M. J.
2009-12-01
The last decade has witnessed an explosion of studies that scrutinize whether or not the siderophile element budget of the modern mantle can plausibly be explained by metal-silicate equilibration in a deep magma ocean during core formation. The single-stage equilibrium scenario is seductive because experiments that equilibrate metal and silicate can then serve as a proxy for the early earth, and the physical and chemical conditions of core formation can be identified. Recently, models have become more complex as they try to accommodate the proliferation of element partitioning data sets, each of which sets its own limits on the pressure, temperature, and chemistry of equilibration. The ability of single stage models to explain mantle chemistry has subsequently been challenged, resulting in the development of complex multi-stage core formation models. Here we show that the extent to which extant partitioning data are consistent with single-stage core formation depends heavily upon (1) the assumptions made when regressing experimental partitioning data (2) the certainty with which regression coefficients are known and (3) the certainty with which the core/mantle concentration ratios of the siderophile elements are known. We introduce a Monte Carlo algorithm coded in MATLAB that samples parameter space in pressure and oxygen fugacity for a given mantle composition (nbo/t) and liquidus, and returns the number of equilibrium single-stage liquidus “solutions” that are permissible, taking into account the uncertainty in regression parameters and range of acceptable core/mantle ratios. Here we explore the consequences of regression parameter uncertainty and the impact of regression construction on model outcomes. We find that the form of the partition coefficient (Kd with enforced valence state, or D) and the handling of the temperature effect (based on 1-atm free energy data or high P-T experimental observations) critically affects model outcomes. We consider the most
Likelihood based testing for no fractional cointegration
DEFF Research Database (Denmark)
Lasak, Katarzyna
We consider two likelihood ratio tests, so-called maximum eigenvalue and trace tests, for the null of no cointegration when fractional cointegration is allowed under the alternative, which is a first step to generalize the so-called Johansen's procedure to the fractional cointegration case....... The standard cointegration analysis only considers the assumption that deviations from equilibrium can be integrated of order zero, which is very restrictive in many cases and may imply an important loss of power in the fractional case. We consider the alternative hypotheses with equilibrium deviations...... that can be mean reverting with order of integration possibly greater than zero. Moreover, the degree of fractional cointegration is not assumed to be known, and the asymptotic null distribution of both tests is found when considering an interval of possible values. The power of the proposed tests under...
Armaghani, Danial Jahed; Mahdiyar, Amir; Hasanipanah, Mahdi; Faradonbeh, Roohollah Shirani; Khandelwal, Manoj; Amnieh, Hassan Bakhshandeh
2016-09-01
Flyrock is considered as one of the main causes of human injury, fatalities, and structural damage among all undesirable environmental impacts of blasting. Therefore, it seems that the proper prediction/simulation of flyrock is essential, especially in order to determine blast safety area. If proper control measures are taken, then the flyrock distance can be controlled, and, in return, the risk of damage can be reduced or eliminated. The first objective of this study was to develop a predictive model for flyrock estimation based on multiple regression (MR) analyses, and after that, using the developed MR model, flyrock phenomenon was simulated by the Monte Carlo (MC) approach. In order to achieve objectives of this study, 62 blasting operations were investigated in Ulu Tiram quarry, Malaysia, and some controllable and uncontrollable factors were carefully recorded/calculated. The obtained results of MC modeling indicated that this approach is capable of simulating flyrock ranges with a good level of accuracy. The mean of simulated flyrock by MC was obtained as 236.3 m, while this value was achieved as 238.6 m for the measured one. Furthermore, a sensitivity analysis was also conducted to investigate the effects of model inputs on the output of the system. The analysis demonstrated that powder factor is the most influential parameter on fly rock among all model inputs. It is noticeable that the proposed MR and MC models should be utilized only in the studied area and the direct use of them in the other conditions is not recommended.
Xu, Wenhao; Yang, Jichu; Hu, Yinyu
2009-04-01
Configurational-bias Monte Carlo simulations in the isobaric-isothermal ensemble using the TraPPE-UA force field were performed to study the microscopic structures and molecular interactions of mixtures containing supercritical carbon dioxide (scCO(2)) and ethanol (EtOH). The binary vapor-liquid coexisting curves were calculated at 298.17, 333.2, and 353.2 K and are in excellent agreement with experimental results. For the first time, three important interactions, i.e., EtOH-EtOH hydrogen bonding, EtOH-CO(2) hydrogen bonding, and EtOH-CO(2) electron donor-acceptor (EDA) bonding, in the mixtures were fully analyzed and compared. The EtOH mole fraction, temperature, and pressure effect on the three interactions was investigated and then explained by the competition of interactions between EtOH and CO(2) molecules. Analysis of the microscopic structures indicates a strong preference for the formation of EtOH-CO(2) hydrogen-bonded tetramers and pentamers at higher EtOH compositions. The distribution of aggregation sizes and types shows that a very large EtOH-EtOH hydrogen-bonded network exists in the mixtures, while only linear EtOH-CO(2) hydrogen-bonded and EDA-bonded dimers and trimers are present. Further analysis shows that EtOH-CO(2) EDA complex is more stable than the hydrogen-bonded one.
Biotic indices have been used ot assess biological condition by dividing index scores into condition categories. Historically the number of categories has been based on professional judgement. Alternatively, statistical methods such as power analysis can be used to determine the ...
Vestige: Maximum likelihood phylogenetic footprinting
Directory of Open Access Journals (Sweden)
Maxwell Peter
2005-05-01
Full Text Available Abstract Background Phylogenetic footprinting is the identification of functional regions of DNA by their evolutionary conservation. This is achieved by comparing orthologous regions from multiple species and identifying the DNA regions that have diverged less than neutral DNA. Vestige is a phylogenetic footprinting package built on the PyEvolve toolkit that uses probabilistic molecular evolutionary modelling to represent aspects of sequence evolution, including the conventional divergence measure employed by other footprinting approaches. In addition to measuring the divergence, Vestige allows the expansion of the definition of a phylogenetic footprint to include variation in the distribution of any molecular evolutionary processes. This is achieved by displaying the distribution of model parameters that represent partitions of molecular evolutionary substitutions. Examination of the spatial incidence of these effects across regions of the genome can identify DNA segments that differ in the nature of the evolutionary process. Results Vestige was applied to a reference dataset of the SCL locus from four species and provided clear identification of the known conserved regions in this dataset. To demonstrate the flexibility to use diverse models of molecular evolution and dissect the nature of the evolutionary process Vestige was used to footprint the Ka/Ks ratio in primate BRCA1 with a codon model of evolution. Two regions of putative adaptive evolution were identified illustrating the ability of Vestige to represent the spatial distribution of distinct molecular evolutionary processes. Conclusion Vestige provides a flexible, open platform for phylogenetic footprinting. Underpinned by the PyEvolve toolkit, Vestige provides a framework for visualising the signatures of evolutionary processes across the genome of numerous organisms simultaneously. By exploiting the maximum-likelihood statistical framework, the complex interplay between mutational
Mun, Johnathan; Housel, Thomas
2010-01-01
In this quick primer, advanced quantitative risk-based concepts will be introduced--namely, the hands-on applications of Monte Carlo simulation, real options analysis, stochastic forecasting, portfolio optimization, and knowledge value added. These methodologies rely on common metrics and existing techniques (e.g., return on investment, discounted cash flow, cost-based analysis, and so forth), and complement these traditional techniques by pushing the envelope of analytics, not replacing them...
Factor Analysis with Ordinal Indicators: A Monte Carlo Study Comparing DWLS and ULS Estimation
Forero, Carlos G.; Maydeu-Olivares, Alberto; Gallardo-Pujol, David
2009-01-01
Factor analysis models with ordinal indicators are often estimated using a 3-stage procedure where the last stage involves obtaining parameter estimates by least squares from the sample polychoric correlations. A simulation study involving 324 conditions (1,000 replications per condition) was performed to compare the performance of diagonally…
DEFF Research Database (Denmark)
Salling, Kim Bang; Leleur, Steen
2007-01-01
This paper presents an appraisal study of three different airport proposals in Greenland by the use of an adapted version of the Danish CBA-DK model. The assessment model is based on both a deterministic calculation by the use of conventional cost-benefit analysis and a stochastic calculation...
Analysis of Unknown Radioactive Samples Spectra by Using (K0IAEA) Monte Carlo Code
International Nuclear Information System (INIS)
Studying and measuring of gamma-ray energies emitted from radionuclides are very important field of radiation physics, and have many applications in different fields of sciences such as in the study of nuclear structure, identification of radioisotopes and their activities, estimating absorbed doses, and determination of nuclear reaction cross sections. The new developments in gamma-ray spectrometry have expanded and have been applied in diverse fields such as astrophysics and medical therapy for which highly accurate measurements of gamma-rays are needed. This has been achieved by way of tracing the interaction of gamma-rays in the semiconductor and scintillation detectors and the energy deposited within. This thesis is concerned with the detector Full Energy Peak Efficiency (FEPE). The peak efficiency assumes only those interactions that deposit the full energy of the incident radiation and counted in a differential pulse height distribution. These full energy events are normally evidence by a peak that appears at the highest end of the spectrum. Events that deposit only part of the incident radiation energy then will appear farther to the left in the spectrum. The number of full energy events can be obtained by simply integrating the total area under the peak. In this work, two identical isotropic neutron sources of Am-Be type, each having an activity of about (175 GBq), were used for irradiating and analyzing unknown samples. Two types of foils,175In and 197Au, were used for monitoring the thermal neutron flux by the foil activation method. A hyper pure germanium (HPGe) detector was used in view of its good energy resolution and good signal-to-noise ratio. The γ-lines with highest intensity were selected and the induced activity analysis was done using Genie 2000 software. The analysis of the γ- spectra was carried out by using K0-IAEA and ETNA programs. The validity of the analysis was confirmed by neutron activation analysis of known and unknown samples.
Monte Carlo analysis of a lateral IBIC experiment on a 4H-SiC Schottky diode
Olivero, P.; Forneris, J.; Gamarra, P.; Jakšić, M.; Giudice, A. Lo; Manfredotti, C.; Pastuović, Ž.; Skukan, N.; Vittone, E.
2011-10-01
The transport properties of a 4H-SiC Schottky diode have been investigated by the ion beam induced charge (IBIC) technique in lateral geometry through the analysis of the charge collection efficiency (CCE) profile at a fixed applied reverse bias voltage. The cross section of the sample orthogonal to the electrodes was irradiated by a rarefied 4 MeV proton microbeam and the charge pulses have been recorded as function of incident proton position with a spatial resolution of 2 μm. The CCE profile shows a broad plateau with CCE values close to 100% occurring at the depletion layer, whereas in the neutral region, the exponentially decreasing profile indicates the dominant role played by the diffusion transport mechanism. Mapping of charge pulses was accomplished by a novel computational approach, which consists in mapping the Gunn's weighting potential by solving the electrostatic problem by finite element method and hence evaluating the induced charge at the sensing electrode by a Monte Carlo method. The combination of these two computational methods enabled an exhaustive interpretation of the experimental profiles and allowed an accurate evaluation both of the electrical characteristics of the active region (e.g. electric field profiles) and of basic transport parameters (i.e. diffusion length and minority carrier lifetime).
A Monte Carlo (MC) based individual calibration method for in vivo x-ray fluorescence analysis (XRF)
Hansson, Marie; Isaksson, Mats
2007-04-01
X-ray fluorescence analysis (XRF) is a non-invasive method that can be used for in vivo determination of thyroid iodine content. System calibrations with phantoms resembling the neck may give misleading results in the cases when the measurement situation largely differs from the calibration situation. In such cases, Monte Carlo (MC) simulations offer a possibility of improving the calibration by better accounting for individual features of the measured subjects. This study investigates the prospects of implementing MC simulations in a calibration procedure applicable to in vivo XRF measurements. Simulations were performed with Penelope 2005 to examine a procedure where a parameter, independent of the iodine concentration, was used to get an estimate of the expected detector signal if the thyroid had been measured outside the neck. An attempt to increase the simulation speed and reduce the variance by exclusion of electrons and by implementation of interaction forcing was conducted. Special attention was given to the geometry features: analysed volume, source-sample-detector distances, thyroid lobe size and position in the neck. Implementation of interaction forcing and exclusion of electrons had no obvious adverse effect on the quotients while the simulation time involved in an individual calibration was low enough to be clinically feasible.
Directory of Open Access Journals (Sweden)
Ahmed M.A. Sattar
2014-01-01
Full Text Available While construction of the Aswan High Dam (AHD has stopped concurrent flooding events, River Nile is still subject to low intensity flood waves resulting from controlled release of water from the dam reservoir. Analysis of flow released from New Naga-Hammadi Barrage, which is located at 3460 km downstream AHD indicated an increase in magnitude of flood released from the barrage in the past 10 years. A 2D numerical mobile bed model is utilized to investigate the possible morphological changes in the downstream of Naga-Hammadi Barrage from possible higher flood releases. Monte Carlo simulation analyses (MCS is applied to the deterministic results of the 2D model to account for and assess the uncertainty of sediment parameters and formulations in addition to sacristy of field measurements. Results showed that the predicted volume of erosion yielded the highest uncertainty and variation from deterministic run, while navigation velocity yielded the least uncertainty. Furthermore, the error budget method is used to rank various sediment parameters for their contribution in the total prediction uncertainty. It is found that the suspended sediment contributed to output uncertainty more than other sediment parameters followed by bed load with 10% less order of magnitude.
Sattar, Ahmed M A; Raslan, Yasser M
2014-01-01
While construction of the Aswan High Dam (AHD) has stopped concurrent flooding events, River Nile is still subject to low intensity flood waves resulting from controlled release of water from the dam reservoir. Analysis of flow released from New Naga-Hammadi Barrage, which is located at 3460 km downstream AHD indicated an increase in magnitude of flood released from the barrage in the past 10 years. A 2D numerical mobile bed model is utilized to investigate the possible morphological changes in the downstream of Naga-Hammadi Barrage from possible higher flood releases. Monte Carlo simulation analyses (MCS) is applied to the deterministic results of the 2D model to account for and assess the uncertainty of sediment parameters and formulations in addition to sacristy of field measurements. Results showed that the predicted volume of erosion yielded the highest uncertainty and variation from deterministic run, while navigation velocity yielded the least uncertainty. Furthermore, the error budget method is used to rank various sediment parameters for their contribution in the total prediction uncertainty. It is found that the suspended sediment contributed to output uncertainty more than other sediment parameters followed by bed load with 10% less order of magnitude. PMID:25685476
Monte Carlo analysis of a lateral IBIC experiment on a 4H-SiC Schottky diode
Olivero, P; Gamarra, P; Jaksic, M; Giudice, A Lo; Manfredotti, C; Pastuovic, Z; Skukan, N; Vittone, E
2016-01-01
The transport properties of a 4H-SiC Schottky diode have been investigated by the Ion Beam Induced Charge (IBIC) technique in lateral geometry through the analysis of the charge collection efficiency (CCE) profile at a fixed applied reverse bias voltage. The cross section of the sample orthogonal to the electrodes was irradiated by a rarefied 4 MeV proton microbeam and the charge pulses have been recorded as function of incident proton position with a spatial resolution of 2 um. The CCE profile shows a broad plateau with CCE values close to 100% occurring at the depletion layer, whereas in the neutral region, the exponentially decreasing profile indicates the dominant role played by the diffusion transport mechanism. Mapping of charge pulses was accomplished by a novel computational approach, which consists in mapping the Gunn's weighting potential by solving the electrostatic problem by finite element method and hence evaluating the induced charge at the sensing electrode by a Monte Carlo method. The combina...
International Nuclear Information System (INIS)
There are many problems related to multi-step Monte Carlo (MC) calculation. Surface Source Reading (SSR) and Surface Source Writing (SSW) options in MCNP, MC depletion calculation, accelerator shielding analysis using secondary particle source term calculation, and residual particle transport calculation caused by activation are the examples of the simulations. In these problems, the average values estimated from the MC result in the previous step are used as sources of MC simulation in the next step. Hence, the uncertainties of the results in previous step are usually not considered for calculating that of next step MC simulation even though they are propagated as the stepwise progression. In this study, a new method using the forward-adjoint calculation and the union tally is proposed for the estimation of real uncertainty. For the activation benchmark problems the responses and real uncertainties were estimated by using the proposed method. And, the results were compared with those estimated by the brute force technique and the adjoint-based approach. The result shows that the proposed approach gives an accurate result comparing with the reference results
Schirmer, M.; Ghasemizade, M.; Radny, D.
2014-12-01
Many different methods and approaches have been suggested for simulation of preferential flows. However, most of these methods have been tested in lab scales where boundary conditions and material properties are known and under control. The focus of this study is to compare two different approaches for simulating preferential flows in a weighing lysimeter where the scale of simulation is closer to field scales than simulations done in labs. To do so, we applied dual permeability and spatially distributed heterogeneity as two competitive approaches for simulating slow and rapid flow out of a lysimeter. While the dual permeability approach assumes that there is a structure among soil aggregates and that can be captured as a fraction of the porosity, the other method attributes the existence of preferential flows to heterogeneity distributed within the domain. The two aforementioned approaches were used in order to simulate daily recharge values of a lysimeter. The analysis included a calibration phase, which started from March 2012 until March 2013, and a validation phase which lasted a year following the calibration period. The simulations were performed based on the numerical and 3-D physically based model HydroGeoSphere. The nonlinear uncertainty analysis of the results indicate that they are comparable.
International Nuclear Information System (INIS)
In the field of shielding, the requirement of radiation transport calculations in severe conditions, characterized by irreducible three-dimensional geometries has increased the use of the Monte Carlo method. The latter has proved to be the only rigorous and appropriate calculational method in such conditions. However, further efforts at optimization are still necessary to render the technique practically efficient, despite recent improvements in the Monte Carlo codes, the progress made in the field of computers and the availability of accurate nuclear data. Moreover, the personal experience acquired in the field and the control of sophisticated calculation procedures are of the utmost importance. The aim of the work which has been carried out is the gathering of all the necessary elements and features that would lead to an efficient utilization of the Monte Carlo method used in connection with shielding problems. The study of the general aspects of the method and the exploitation techniques of the MORSE code, which has proved to be one of the most comprehensive of the Monte Carlo codes, lead to a successful analysis of an actual case. In fact, the severe conditions and difficulties met have been overcome using such a stochastic simulation code. Finally, a critical comparison between calculated and high-accuracy experimental results has allowed the final confirmation of the methodology used by us
Efficient Strategies for Calculating Blockwise Likelihoods Under the Coalescent.
Lohse, Konrad; Chmelik, Martin; Martin, Simon H; Barton, Nicholas H
2016-02-01
The inference of demographic history from genome data is hindered by a lack of efficient computational approaches. In particular, it has proved difficult to exploit the information contained in the distribution of genealogies across the genome. We have previously shown that the generating function (GF) of genealogies can be used to analytically compute likelihoods of demographic models from configurations of mutations in short sequence blocks (Lohse et al. 2011). Although the GF has a simple, recursive form, the size of such likelihood calculations explodes quickly with the number of individuals and applications of this framework have so far been mainly limited to small samples (pairs and triplets) for which the GF can be written by hand. Here we investigate several strategies for exploiting the inherent symmetries of the coalescent. In particular, we show that the GF of genealogies can be decomposed into a set of equivalence classes that allows likelihood calculations from nontrivial samples. Using this strategy, we automated blockwise likelihood calculations for a general set of demographic scenarios in Mathematica. These histories may involve population size changes, continuous migration, discrete divergence, and admixture between multiple populations. To give a concrete example, we calculate the likelihood for a model of isolation with migration (IM), assuming two diploid samples without phase and outgroup information. We demonstrate the new inference scheme with an analysis of two individual butterfly genomes from the sister species Heliconius melpomene rosina and H. cydno. PMID:26715666
Predictive uncertainty analysis of a saltwater intrusion model using null-space Monte Carlo
DEFF Research Database (Denmark)
Herckenrath, Daan; Langevin, Christian D.; Doherty, John
2011-01-01
. Random noise was added to the observations to approximate realistic field conditions. The NSMC method was used to calculate 1000 calibration-constrained parameter fields. If the dimensionality of the solution space was set appropriately, the estimated uncertainty range from the NSMC analysis encompassed...... the truth. Several variants of the method were implemented to investigate their effect on the efficiency of the NSMC method. Reducing the dimensionality of the null-space for the processing of the random parameter sets did not result in any significant gains in efficiency and compromised the ability...... variability did not make a noticeable difference to the uncertainty of the prediction. With this higher level of heterogeneity, however, the computational burden of generating calibration-constrained parameter fields approximately doubled. Predictive uncertainty variance computed through the NSMC method...
Planck intermediate results: XVI. Profile likelihoods for cosmological parameters
DEFF Research Database (Denmark)
Bartlett, J.G.; Cardoso, J.-F.; Delabrouille, J.;
2014-01-01
mass distribution. By applying the Feldman-Cousins prescription, we again obtain results very similar to those of the Bayesian methodology. However, the profile-likelihood analysis of the cosmic microwave background (CMB) combination (Planck+WP+highL) reveals a minimum well within the unphysical...
Maximum likelihood estimation of phase-type distributions
DEFF Research Database (Denmark)
Esparza, Luz Judith R
This work is concerned with the statistical inference of phase-type distributions and the analysis of distributions with rational Laplace transform, known as matrix-exponential distributions. The thesis is focused on the estimation of the maximum likelihood parameters of phase-type distributions ...
Troubled Conception: Negotiating the Likelihood of Having Children
May, Marian
2007-01-01
In the context of low fertility and Australia's ageing population, a national longitudinal telephone survey, "Negotiating the Life Course" (NLC), asks women about their childbearing intentions. This paper uses conversation analysis (CA) to examine interaction between an interviewer and respondents on one NLC question about the likelihood of having…
Directory of Open Access Journals (Sweden)
TEMITOPE RAPHAEL AYODELE
2016-04-01
Full Text Available Monte Carlo simulation using Simple Random Sampling (SRS technique is popularly known for its ability to handle complex uncertainty problems. However, to produce a reasonable result, it requires huge sample size. This makes it to be computationally expensive, time consuming and unfit for online power system applications. In this article, the performance of Latin Hypercube Sampling (LHS technique is explored and compared with SRS in term of accuracy, robustness and speed for small signal stability application in a wind generator-connected power system. The analysis is performed using probabilistic techniques via eigenvalue analysis on two standard networks (Single Machine Infinite Bus and IEEE 16–machine 68 bus test system. The accuracy of the two sampling techniques is determined by comparing their different sample sizes with the IDEAL (conventional. The robustness is determined based on a significant variance reduction when the experiment is repeated 100 times with different sample sizes using the two sampling techniques in turn. Some of the results show that sample sizes generated from LHS for small signal stability application produces the same result as that of the IDEAL values starting from 100 sample size. This shows that about 100 sample size of random variable generated using LHS method is good enough to produce reasonable results for practical purpose in small signal stability application. It is also revealed that LHS has the least variance when the experiment is repeated 100 times compared to SRS techniques. This signifies the robustness of LHS over that of SRS techniques. 100 sample size of LHS produces the same result as that of the conventional method consisting of 50000 sample size. The reduced sample size required by LHS gives it computational speed advantage (about six times over the conventional method.
International Nuclear Information System (INIS)
Sheet metal stamping is one of the most commonly used manufacturing processes, and hence, much research has been carried for economic gain. Searching through the literatures, however, it is found that there are still a lots of problems unsolved. For example, it is well known that for a same press, same workpiece material, and same set of die, the product quality may vary owing to a number of factors, such as the inhomogeneous of the workpice material, the loading error, the lubrication, and etc. Presently, few seem able to predict the quality variation, not to mention what contribute to the quality variation. As a result, trial-and-error is still needed in the shop floor, causing additional cost and time delay. This paper introduces a new approach to predict the product quality variation and identify the sensitive design / process parameters. The new approach is based on a combination of inverse Finite Element Modeling (FEM) and Monte Carlo Simulation (more specifically, the Latin Hypercube Sampling (LHS) approach). With an acceptable accuracy, the inverse FEM (also called one-step FEM) requires much less computation load than that of the usual incremental FEM and hence, can be used to predict the quality variations under various conditions. LHS is a statistical method, through which the sensitivity analysis can be carried out. The result of the sensitivity analysis has clear physical meaning and can be used to optimize the die design and / or the process design. Two simulation examples are presented including drawing a rectangular box and drawing a two-step rectangular box
Zhang, Junlong; Li, Yongping; Huang, Guohe; Chen, Xi; Bao, Anming
2016-07-01
Without a realistic assessment of parameter uncertainty, decision makers may encounter difficulties in accurately describing hydrologic processes and assessing relationships between model parameters and watershed characteristics. In this study, a Markov-Chain-Monte-Carlo-based multilevel-factorial-analysis (MCMC-MFA) method is developed, which can not only generate samples of parameters from a well constructed Markov chain and assess parameter uncertainties with straightforward Bayesian inference, but also investigate the individual and interactive effects of multiple parameters on model output through measuring the specific variations of hydrological responses. A case study is conducted for addressing parameter uncertainties in the Kaidu watershed of northwest China. Effects of multiple parameters and their interactions are quantitatively investigated using the MCMC-MFA with a three-level factorial experiment (totally 81 runs). A variance-based sensitivity analysis method is used to validate the results of parameters' effects. Results disclose that (i) soil conservation service runoff curve number for moisture condition II (CN2) and fraction of snow volume corresponding to 50% snow cover (SNO50COV) are the most significant factors to hydrological responses, implying that infiltration-excess overland flow and snow water equivalent represent important water input to the hydrological system of the Kaidu watershed; (ii) saturate hydraulic conductivity (SOL_K) and soil evaporation compensation factor (ESCO) have obvious effects on hydrological responses; this implies that the processes of percolation and evaporation would impact hydrological process in this watershed; (iii) the interactions of ESCO and SNO50COV as well as CN2 and SNO50COV have an obvious effect, implying that snow cover can impact the generation of runoff on land surface and the extraction of soil evaporative demand in lower soil layers. These findings can help enhance the hydrological model
Composite analysis with Monte Carlo methods: an example with cosmic rays and clouds
Directory of Open Access Journals (Sweden)
Laken B.A.
2013-09-01
Full Text Available The composite (superposed epoch analysis technique has been frequently employed to examine a hypothesized link between solar activity and the Earth’s atmosphere, often through an investigation of Forbush decrease (Fd events (sudden high-magnitude decreases in the flux cosmic rays impinging on the upper-atmosphere lasting up to several days. This technique is useful for isolating low-amplitude signals within data where background variability would otherwise obscure detection. The application of composite analyses to investigate the possible impacts of Fd events involves a statistical examination of time-dependent atmospheric responses to Fds often from aerosol and/or cloud datasets. Despite the publication of numerous results within this field, clear conclusions have yet to be drawn and much ambiguity and disagreement still remain. In this paper, we argue that the conflicting findings of composite studies within this field relate to methodological differences in the manner in which the composites have been constructed and analyzed. Working from an example, we show how a composite may be objectively constructed to maximize signal detection, robustly identify statistical significance, and quantify the lower-limit uncertainty related to hypothesis testing. Additionally, we also demonstrate how a seemingly significant false positive may be obtained from non-significant data by minor alterations to methodological approaches.
Acoustic effects analysis utilizing speckle pattern with fixed-particle Monte Carlo
Vakili, Ali; Hollmann, Joseph A.; Holt, R. Glynn; DiMarzio, Charles A.
2016-03-01
Optical imaging in a turbid medium is limited because of multiple scattering a photon undergoes while traveling through the medium. Therefore, optical imaging is unable to provide high resolution information deep in the medium. In the case of soft tissue, acoustic waves unlike light, can travel through the medium with negligible scattering. However, acoustic waves cannot provide medically relevant contrast as good as light. Hybrid solutions have been applied to use the benefits of both imaging methods. A focused acoustic wave generates a force inside an acoustically absorbing medium known as acoustic radiation force (ARF). ARF induces particle displacement within the medium. The amount of displacement is a function of mechanical properties of the medium and the applied force. To monitor the displacement induced by the ARF, speckle pattern analysis can be used. The speckle pattern is the result of interfering optical waves with different phases. As light travels through the medium, it undergoes several scattering events. Hence, it generates different scattering paths which depends on the location of the particles. Light waves that travel along these paths have different phases (different optical path lengths). ARF induces displacement to scatterers within the acoustic focal volume, and changes the optical path length. In addition, temperature rise due to conversion of absorbed acoustic energy to heat, changes the index of refraction and therefore, changes the optical path length of the scattering paths. The result is a change in the speckle pattern. Results suggest that the average change in the speckle pattern measures the displacement of particles and temperature rise within the acoustic wave focal area, hence can provide mechanical and thermal properties of the medium.
International Nuclear Information System (INIS)
Full text: A recently developed method for fitting a Monte Carlo computer simulation model to observed single crystal diffuse X-ray scattering data has been used to study the diffuse scattering in benzil. The analysis has shown that the diffuse lines, that feature so prominently in the observed diffraction patterns, are due to strong longitudinal displacement correlations transmitted from molecule to molecule via a network of contacts involving hydrogen bonding
Accelerated maximum likelihood parameter estimation for stochastic biochemical systems
Directory of Open Access Journals (Sweden)
Daigle Bernie J
2012-05-01
Full Text Available Abstract Background A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs. MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence. Results We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM2: an accelerated method for calculating MLEs that combines advances in rare event simulation with a computationally efficient version of the Monte Carlo expectation-maximization (MCEM algorithm. Our method requires no prior knowledge regarding parameter values, and it automatically provides a multivariate parameter uncertainty estimate. We applied the method to five stochastic systems of increasing complexity, progressing from an analytically tractable pure-birth model to a computationally demanding model of yeast-polarization. Our results demonstrate that MCEM2 substantially accelerates MLE computation on all tested models when compared to a stand-alone version of MCEM. Additionally, we show how our method identifies parameter values for certain classes of models more accurately than two recently proposed computationally efficient methods
The Maximum Likelihood Threshold of a Graph
Gross, Elizabeth; Sullivant, Seth
2014-01-01
The maximum likelihood threshold of a graph is the smallest number of data points that guarantees that maximum likelihood estimates exist almost surely in the Gaussian graphical model associated to the graph. We show that this graph parameter is connected to the theory of combinatorial rigidity. In particular, if the edge set of a graph $G$ is an independent set in the $n-1$-dimensional generic rigidity matroid, then the maximum likelihood threshold of $G$ is less than or equal to $n$. This c...
Likelihood-ratio ranking of gravitational-wave candidates in a non-Gaussian background
Biswas, R.; Brady, P.; Burguet-Castell, J.; Cannon, K.; J. Clayton; Dietz, A; Fotopoulos, N.; Goggin, L.; Keppel, D.; Pankow, C.; Price, L.; Vaulin, R.
2012-01-01
We describe a general approach to detection of transient gravitational-wave signals in the presence of non-Gaussian background noise. We prove that under quite general conditions, the ratio of the likelihood of observed data to contain a signal to the likelihood of it being a noise fluctuation provides optimal ranking for the candidate events found in an experiment. The likelihood-ratio ranking allows us to combine different kinds of data into a single analysis. We apply the gener...
CosmoPMC: Cosmology Population Monte Carlo
Kilbinger, Martin; Cappe, Olivier; Cardoso, Jean-Francois; Fort, Gersende; Prunet, Simon; Robert, Christian P; Wraith, Darren
2011-01-01
We present the public release of the Bayesian sampling algorithm for cosmology, CosmoPMC (Cosmology Population Monte Carlo). CosmoPMC explores the parameter space of various cosmological probes, and also provides a robust estimate of the Bayesian evidence. CosmoPMC is based on an adaptive importance sampling method called Population Monte Carlo (PMC). Various cosmology likelihood modules are implemented, and new modules can be added easily. The importance-sampling algorithm is written in C, and fully parallelised using the Message Passing Interface (MPI). Due to very little overhead, the wall-clock time required for sampling scales approximately with the number of CPUs. The CosmoPMC package contains post-processing and plotting programs, and in addition a Monte-Carlo Markov chain (MCMC) algorithm. The sampling engine is implemented in the library pmclib, and can be used independently. The software is available for download at http://www.cosmopmc.info.
Likelihood free inference for Markov processes: a comparison.
Owen, Jamie; Wilkinson, Darren J; Gillespie, Colin S
2015-04-01
Approaches to Bayesian inference for problems with intractable likelihoods have become increasingly important in recent years. Approximate Bayesian computation (ABC) and "likelihood free" Markov chain Monte Carlo techniques are popular methods for tackling inference in these scenarios but such techniques are computationally expensive. In this paper we compare the two approaches to inference, with a particular focus on parameter inference for stochastic kinetic models, widely used in systems biology. Discrete time transition kernels for models of this type are intractable for all but the most trivial systems yet forward simulation is usually straightforward. We discuss the relative merits and drawbacks of each approach whilst considering the computational cost implications and efficiency of these techniques. In order to explore the properties of each approach we examine a range of observation regimes using two example models. We use a Lotka-Volterra predator-prey model to explore the impact of full or partial species observations using various time course observations under the assumption of known and unknown measurement error. Further investigation into the impact of observation error is then made using a Schlögl system, a test case which exhibits bi-modal state stability in some regions of parameter space. PMID:25720092
Maximum-likelihood method in quantum estimation
Paris, M G A; Sacchi, M F
2001-01-01
The maximum-likelihood method for quantum estimation is reviewed and applied to the reconstruction of density matrix of spin and radiation as well as to the determination of several parameters of interest in quantum optics.
Introductory statistical inference with the likelihood function
Rohde, Charles A
2014-01-01
This textbook covers the fundamentals of statistical inference and statistical theory including Bayesian and frequentist approaches and methodology possible without excessive emphasis on the underlying mathematics. This book is about some of the basic principles of statistics that are necessary to understand and evaluate methods for analyzing complex data sets. The likelihood function is used for pure likelihood inference throughout the book. There is also coverage of severity and finite population sampling. The material was developed from an introductory statistical theory course taught by the author at the Johns Hopkins University’s Department of Biostatistics. Students and instructors in public health programs will benefit from the likelihood modeling approach that is used throughout the text. This will also appeal to epidemiologists and psychometricians. After a brief introduction, there are chapters on estimation, hypothesis testing, and maximum likelihood modeling. The book concludes with secti...
Asymptotic Likelihood Distribution for Correlated & Constrained Systems
Agarwal, Ujjwal
2016-01-01
It describes my work as summer student at CERN. The report discusses the asymptotic distribution of the likelihood ratio for total no. of parameters being h and 2 out of these being are constrained and correlated.
Efficient scatter modelling for incorporation in maximum likelihood reconstruction
International Nuclear Information System (INIS)
Definition of a simplified model of scatter which can be incorporated in maximum likelihood reconstruction for single-photon emission tomography (SPET) continues to be appealing; however, implementation must be efficient for it to be clinically applicable. In this paper an efficient algorithm for scatter estimation is described in which the spatial scatter distribution is implemented as a spatially invariant convolution for points of constant depth in tissue. The scatter estimate is weighted by a space-dependent build-up factor based on the measured attenuation in tissue. Monte Carlo simulation of a realistic thorax phantom was used to validate this approach. Further efficiency was introduced by estimating scatter once after a small number of iterations using the ordered subsets expectation maximisation (OSEM) reconstruction algorithm. The scatter estimate was incorporated as a constant term in subsequent iterations rather than modifying the scatter estimate each iteration. Monte Carlo simulation was used to demonstrate that the scatter estimate does not change significantly provided at least two iterations OSEM reconstruction, subset size 8, is used. Complete scatter-corrected reconstruction of 64 projections of 40 x 128 pixels was achieved in 38 min using a Sun Sparc20 computer. (orig.)
Maximum likelihood window for time delay estimation
International Nuclear Information System (INIS)
Time delay estimation for the detection of leak location in underground pipelines is critically important. Because the exact leak location depends upon the precision of the time delay between sensor signals due to leak noise and the speed of elastic waves, the research on the estimation of time delay has been one of the key issues in leak lovating with the time arrival difference method. In this study, an optimal Maximum Likelihood window is considered to obtain a better estimation of the time delay. This method has been proved in experiments, which can provide much clearer and more precise peaks in cross-correlation functions of leak signals. The leak location error has been less than 1 % of the distance between sensors, for example the error was not greater than 3 m for 300 m long underground pipelines. Apart from the experiment, an intensive theoretical analysis in terms of signal processing has been described. The improved leak locating with the suggested method is due to the windowing effect in frequency domain, which offers a weighting in significant frequencies.
Tani, Yuji
2016-01-01
Background Consistent with the “attention, interest, desire, memory, action” (AIDMA) model of consumer behavior, patients collect information about available medical institutions using the Internet to select information for their particular needs. Studies of consumer behavior may be found in areas other than medical institution websites. Such research uses Web access logs for visitor search behavior. At this time, research applying the patient searching behavior model to medical institution website visitors is lacking. Objective We have developed a hospital website search behavior model using a Bayesian approach to clarify the behavior of medical institution website visitors and determine the probability of their visits, classified by search keyword. Methods We used the website data access log of a clinic of internal medicine and gastroenterology in the Sapporo suburbs, collecting data from January 1 through June 31, 2011. The contents of the 6 website pages included the following: home, news, content introduction for medical examinations, mammography screening, holiday person-on-duty information, and other. The search keywords we identified as best expressing website visitor needs were listed as the top 4 headings from the access log: clinic name, clinic name + regional name, clinic name + medical examination, and mammography screening. Using the search keywords as the explaining variable, we built a binomial probit model that allows inspection of the contents of each purpose variable. Using this model, we determined a beta value and generated a posterior distribution. We performed the simulation using Markov Chain Monte Carlo methods with a noninformation prior distribution for this model and determined the visit probability classified by keyword for each category. Results In the case of the keyword “clinic name,” the visit probability to the website, repeated visit to the website, and contents page for medical examination was positive. In the case of the
Bodnar, Olha
2009-01-01
Abstract We derive several multivariate control charts to monitor the mean vector of multivariate GARCH processes under the presence of changes, by means of maximizing the generalized likelihood ratio. This presentation is rounded up by a comparative performance study based on extensive Monte Carlo simulations. An empirical illustration shows how the obtained results can be applied to real data.
Maintained Individual Data Distributed Likelihood Estimation (MIDDLE).
Boker, Steven M; Brick, Timothy R; Pritikin, Joshua N; Wang, Yang; von Oertzen, Timo; Brown, Donald; Lach, John; Estabrook, Ryne; Hunter, Michael D; Maes, Hermine H; Neale, Michael C
2015-01-01
Maintained Individual Data Distributed Likelihood Estimation (MIDDLE) is a novel paradigm for research in the behavioral, social, and health sciences. The MIDDLE approach is based on the seemingly impossible idea that data can be privately maintained by participants and never revealed to researchers, while still enabling statistical models to be fit and scientific hypotheses tested. MIDDLE rests on the assumption that participant data should belong to, be controlled by, and remain in the possession of the participants themselves. Distributed likelihood estimation refers to fitting statistical models by sending an objective function and vector of parameters to each participant's personal device (e.g., smartphone, tablet, computer), where the likelihood of that individual's data is calculated locally. Only the likelihood value is returned to the central optimizer. The optimizer aggregates likelihood values from responding participants and chooses new vectors of parameters until the model converges. A MIDDLE study provides significantly greater privacy for participants, automatic management of opt-in and opt-out consent, lower cost for the researcher and funding institute, and faster determination of results. Furthermore, if a participant opts into several studies simultaneously and opts into data sharing, these studies automatically have access to individual-level longitudinal data linked across all studies. PMID:26717128
International Nuclear Information System (INIS)
The Savannah River Laboratory LTRIIA slightly-enriched uranium-D2O critical experiment was analyzed with ENDF/B-IV data and the RCP01 Monte Carlo program, which modeled the entire assembly in explicit detail. The integral parameters delta25 and delta28 showed good agreement with experiment. However, calculated K/sub eff/ was 2 to 3% low, due primarily to an overprediction of U238 capture. This is consistent with results obtained in similar analyses of the H2O-moderated TRX critical experiments. In comparisons with the VIM and MCNP2 Monte Carlo programs, good agreement was observed for calculated reeaction rates in the B2=0 cell
Teng, Ming; Johnson, Timothy; Nathoo, Farouk
2016-01-01
We consider Bayesian computation for a Bayesian fMRI time series model with spatial priors. A previously derived variational Bayes (VB) algorithm based on a mean field approximation is currently implemented in the Statistical Parametric Mapping (SPM) software. To examine the accuracy of this VB approximation we derive Hamiltonian Monte Carlo (HMC) for this model and conduct simulation studies to compare its performance with VB in terms of estimation accuracy, posterior variability, the spatia...
Development and Application of MCNP5 and KENO-VI Monte Carlo Models for the Atucha-2 PHWR Analysis
O. Mazzantini; F. D'Auria; M. Pecchia; Parisi, C
2011-01-01
The geometrical complexity and the peculiarities of Atucha-2 PHWR require the adoption of advanced Monte Carlo codes for performing realistic neutronic simulations. Core models of Atucha-2 PHWR were developed using both MCNP5 and KENO-VI codes. The developed models were applied for calculating reactor criticality states at beginning of life, reactor cell constants, and control rods volumes. The last two applications were relevant for performing successive three dimensional neutron kinetic ana...
Likelihood inference for a nonstationary fractional autoregressive model
DEFF Research Database (Denmark)
Johansen, Søren; Ørregård Nielsen, Morten
2010-01-01
This paper discusses model-based inference in an autoregressive model for fractional processes which allows the process to be fractional of order d or d-b. Fractional differencing involves infinitely many past values and because we are interested in nonstationary processes we model the data X1......,...,X_{T} given the initial values X_{-n}, n=0,1,..., as is usually done. The initial values are not modeled but assumed to be bounded. This represents a considerable generalization relative to all previous work where it is assumed that initial values are zero. For the statistical analysis we assume...... the conditional Gaussian likelihood and for the probability analysis we also condition on initial values but assume that the errors in the autoregressive model are i.i.d. with suitable moment conditions. We analyze the conditional likelihood and its derivatives as stochastic processes in the parameters, including...
A quantum framework for likelihood ratios
Bond, Rachael L; Ormerod, Thomas C
2015-01-01
The ability to calculate precise likelihood ratios is fundamental to many STEM areas, such as decision-making theory, biomedical science, and engineering. However, there is no assumption-free statistical methodology to achieve this. For instance, in the absence of data relating to covariate overlap, the widely used Bayes' theorem either defaults to the marginal probability driven "naive Bayes' classifier", or requires the use of compensatory expectation-maximization techniques. Equally, the use of alternative statistical approaches, such as multivariate logistic regression, may be confounded by other axiomatic conditions, e.g., low levels of co-linearity. This article takes an information-theoretic approach in developing a new statistical formula for the calculation of likelihood ratios based on the principles of quantum entanglement. In doing so, it is argued that this quantum approach demonstrates: that the likelihood ratio is a real quality of statistical systems; that the naive Bayes' classifier is a spec...
Likelihood inference for unions of interacting discs
DEFF Research Database (Denmark)
Møller, Jesper; Helisová, Katarina
is specified with respect to a given marked Poisson model (i.e. a Boolean model). We show how edge effects and other complications can be handled by considering a certain conditional likelihood. Our methodology is illustrated by analyzing Peter Diggle's heather dataset, where we discuss the results......To the best of our knowledge, this is the first paper which discusses likelihood inference or a random set using a germ-grain model, where the individual grains are unobservable edge effects occur, and other complications appear. We consider the case where the grains form a disc process modelled...... of simulation-based maximum likelihood inference and the effect of specifying different reference Poisson models....
Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants
Jin, Ick Hoon
2014-03-01
Statistical inference for the models with intractable normalizing constants has attracted much attention. During the past two decades, various approximation- or simulation-based methods have been proposed for the problem, such as the Monte Carlo maximum likelihood method and the auxiliary variable Markov chain Monte Carlo methods. The Bayesian stochastic approximation Monte Carlo algorithm specifically addresses this problem: It works by sampling from a sequence of approximate distributions with their average converging to the target posterior distribution, where the approximate distributions can be achieved using the stochastic approximation Monte Carlo algorithm. A strong law of large numbers is established for the Bayesian stochastic approximation Monte Carlo estimator under mild conditions. Compared to the Monte Carlo maximum likelihood method, the Bayesian stochastic approximation Monte Carlo algorithm is more robust to the initial guess of model parameters. Compared to the auxiliary variable MCMC methods, the Bayesian stochastic approximation Monte Carlo algorithm avoids the requirement for perfect samples, and thus can be applied to many models for which perfect sampling is not available or very expensive. The Bayesian stochastic approximation Monte Carlo algorithm also provides a general framework for approximate Bayesian analysis. © 2012 Elsevier B.V. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Gallardo, S.; Querol, A.; Ortiz, J.; Rodenas, J.; Verdu, G.
2014-07-01
In this paper the use of Monte Carlo code SWORD-GEANT is proposed to simulate an ultra pure germanium detector High Purity Germanium detector (HPGe) detector ORTEC specifically GMX40P4, coaxial geometry. (Author)
Institute of Scientific and Technical Information of China (English)
周密; 张韩秋; 韦帆; 丁仁杰; 王成权
2011-01-01
目的 探讨采用自主研发的似然比率计算器进行ITO法亲缘关系鉴定的可行性.方法 针对GoldeneyeTM DNA ID System 20A系统的19个常染色体STR基因座分型数据,使用自主研发的似然比率计算器,人工模拟100 000对无关个体及100 000个家系的父子、同胞、半同胞、一、二代堂表兄弟,应用ITO法,自动分析计算亲权指数(PI)和亲缘关系概率(RCP).结果 父子和无关个体关系概率界值下无重叠,具有显著差异.同胞关系概率大于99.99％时,同胞占66.48％,无关个体占0％,具有显著差异；概率小于99.99％时,同胞个体和无关个体有部分重叠,具有一定差异.半同胞关系概率大于99.99％时,半同胞占1.52％,无关个体占0％,具有显著差异；概率小于99.99％时,两组有部分重叠,具有一定差异.一、二代堂表兄弟关系概率为10％ ～ 90％范围内时,无关个体分别占90％,99.98％,无法推断.结论 本文方法可用于推断父子关系、同胞关系、半同胞关系.%Objective To evaluate the feasibility of genetic relationship analysis with ITO method using selfdeveloped likelihood ratio (LR) calculator. Methods For the typing data of 19 autosomal STR loci with the Goldeneye? DNA ID System 20A, the paternity index (PI) and relative chance of paternity (RCP) of 100 000 pairs of unrelated individuals, parent-child, full-sibling, halfsibling, first cousin, and second cousin from manual simulation were automatically analyzed with ITO method using selfdeveloped likelihood ratio (LR) calculator. Results Parent-child and unrelated individual never overlapped at all the range of parent-child probability ( RCPpc ) , and the difference was significant. Full-sibling probability ( RCPre ) of 66. 48% full-sibling were more than 99.99% while RCPR of unrelated individual pairs were 0%. Halfsibling probability ( RCP^) of 1. 52% halfsibling were more than 99. 99% while RCP^ of unrelated individual pairs were0%. In addition
Likelihood inference for unions of interacting discs
DEFF Research Database (Denmark)
Møller, Jesper; Helisova, K.
2010-01-01
with respect to a given marked Poisson model (i.e. a Boolean model). We show how edge effects and other complications can be handled by considering a certain conditional likelihood. Our methodology is illustrated by analysing Peter Diggle's heather data set, where we discuss the results of simulation...
Nonparametric (smoothed) likelihood and integral equations
Groeneboom, Piet
2012-01-01
We show that there is an intimate connection between the theory of nonparametric (smoothed) maximum likelihood estimators for certain inverse problems and integral equations. This is illustrated by estimators for interval censoring and deconvolution problems. We also discuss the asymptotic efficiency of the MLE for smooth functionals in these models.
Maximum likelihood estimation of search costs
Moraga González, José; Wildenbeest, Matthijs R.
2008-01-01
In a recent paper Hong and Shum [2006. Using price distributions to estimate search costs. Rand Journal of Economics 37, 257-275] present a structural method to estimate search cost distributions. We extend their approach to the case of oligopoly and present a new maximum likelihood method to estima
Maximum Likelihood Estimation of Search Costs
J.L. Moraga-Gonzalez (José Luis); M.R. Wildenbeest (Matthijs)
2006-01-01
textabstractIn a recent paper Hong and Shum (forthcoming) present a structural methodology to estimate search cost distributions. We extend their approach to the case of oligopoly and present a maximum likelihood estimate of the search cost distribution. We apply our method to a data set of online p
Maximum likelihood estimation for integrated diffusion processes
DEFF Research Database (Denmark)
Baltazar-Larios, Fernando; Sørensen, Michael
EM-algorithm to obtain maximum likelihood estimates of the parameters in the diffusion model. As part of the algorithm, we use a recent simple method for approximate simulation of diffusion bridges. In simulation studies for the Ornstein-Uhlenbeck process and the CIR process the proposed method works...
Growing local likelihood network: Emergence of communities
Chen, S.; Small, M.
2015-10-01
In many real situations, networks grow only via local interactions. New nodes are added to the growing network with information only pertaining to a small subset of existing nodes. Multilevel marketing, social networks, and disease models can all be depicted as growing networks based on local (network path-length) distance information. In these examples, all nodes whose distance from a chosen center is less than d form a subgraph. Hence, we grow networks with information only from these subgraphs. Moreover, we use a likelihood-based method, where at each step we modify the networks by changing their likelihood to be closer to the expected degree distribution. Combining the local information and the likelihood method, we grow networks that exhibit novel features. We discover that the likelihood method, over certain parameter ranges, can generate networks with highly modulated communities, even when global information is not available. Communities and clusters are abundant in real-life networks, and the method proposed here provides a natural mechanism for the emergence of communities in scale-free networks. In addition, the algorithmic implementation of network growth via local information is substantially faster than global methods and allows for the exploration of much larger networks.
Energy Technology Data Exchange (ETDEWEB)
Anim-Sampong, S.; Akaho, E.H.K.; Maakuu, B.T.; Gbadago, J.K. [Ghana Research Reactor-1 Centre, Dept. of Nuclear Engineering and Materials Science, National Nuclear Research Institute, Ghana Atomic Energy Commission, Legon, Accra (Ghana); Andam, A. [Kwame Nkrumah Univ. of Science and Technology, Dept. of Physics (Ghana); Liaw, J.J.R.; Matos, J.E. [Argonne National Lab., RERTR Programme, Div. of Nuclear Engineering (United States)
2007-07-01
Monte Carlo particle transport methods and software (MCNP) have been applied to the modelling, simulation and neutronic analysis for the conversion of the HEU-fuelled (high enrichment uranium) core of the Ghana Research Reactor-1 (GHARR-1) facility. The results show that the MCNP model of the GHARR-1 facility, which is a commercial version of the Miniature Neutron Source Reactor (MNSR) is good as the simulated neutronic and other reactor physics parameters agree with very well with experimental and zero power results. Three UO{sub 2} LEU (low enrichment uranium) fuels with different enrichments (12.6% and 19.75%), core configurations, core loadings were utilized in the conversion studies. The nuclear criticality and kinetic parameters obtained from the Monte Carlo simulation and neutronic analysis using three UO{sub 2} LEU fuels are in close agreement with results obtained for the reference 90.2% U-Al HEU core. The neutron flux variation in the core, fission chamber and irradiation channels for the LEU UO{sub 2} fuels show the same trend as the HEU core as presented in the paper. The Monte Carlo model confirms a reduction (8% max) in the peak neutron fluxes simulated in the irradiation channels which are utilized for experimental and commercial activities. However, the reductions or 'losses' in the flux levels neither affects the criticality safety, reactor operations and safety nor utilization of the reactor. Employing careful core loading optimization techniques and fuel loadings and enrichment, it is possible to eliminate the apparent reductions or 'losses' in the neutron fluxes as suggested in this paper. Concerning neutronics, it can be concluded that all the 3 LEU fuels qualify as LEU candidates for core conversion of the GHARR-1 facility.
Hart, D.; Yoon, H.; McKenna, S. A.
2011-12-01
Quantification of prediction uncertainty resulting from estimated parameters is critical to provide accurate predictive models for field-scale groundwater flow and transport problems. We examine and compare two approaches to defining predictive uncertainty where both approaches utilize pilot points to parameterize spatially heterogeneous fields. The first approach is the independent calibration of multiple initial "seed" fields created through geostatistical simulation and conditioned to observation data, resulting in an ensemble of calibrated property fields that defines uncertainty in the calibrated parameters. The second approach is the null-space Monte Carlo (NSMC) method that employs a decomposition of the Jacobian matrix from a single calibration to define a minimum number of linear combinations of parameters that account for the majority of the sensitivity of the overall calibration to the observed data. Random vectors are applied to the remaining linear combinations of parameters, the null space, to create an ensemble of fields, each of which remains calibrated to the data. We compare these two approaches using a highly-parameterized groundwater model of the Culebra dolomite in southeastern New Mexico. Observation data include two decades of steady-state head measurements and pumping test results. The predictive performance measure is advective travel time from a point to a prescribed boundary. Calibrated parameters at a set of pilot points include transmissivity, the horizontal hydraulic anisotropy, the storativity, and a section of recharge (> 1200 parameters in total). First, we calibrate 200 multiple random seed fields generated through geostatistical simulation conditioned to observation data. The 11 fields that contain the best and worst scenarios in terms of calibration and travel time analysis among the best 100 calibrated results provide a basis for the NSMC method. The NSMC method is used to generate 200 calibration-constrained parameter fields
Analysis of the TRIGA MARK-II benchmark IEU-COMP-THERM-003 with Monte Carlo code MVP
Mahmood, M. S.; 長家 康展; 森 貴正
2004-01-01
The benchmark experiments of the TRIGA Mark-II reactor in the ICSBEP handbook have been analyzed with the Monte Carlo code MVP using the cross section libraries based on JENDL-3.3, JENDL-3.2 and ENDF/B-VI.8. MCNP calculations have been also performed with the ENDF/B-VI.6 library for comparison between the MVP and MCNP results. For both cores labeled 132 and 133, which have different core configurations, the ratio of the calculated to the experimental results (C/E) for keff obtained by the MVP...
International Nuclear Information System (INIS)
A description is given of a method for calculating the penetration and energy deposition of gamma radiation, based on Monte Carlo techniques. The essential feature is the application of the exponential transformation to promote the transport of penetrating quanta and to balance the steep spatial variations of the source distributions which appear in secondary gamma emission problems. The estimated statistical errors in a number of sample problems, involving concrete shields with thicknesses up to 500 cm, are shown to be quite favorable, even at relatively short computing times. A practical reactor shielding problem is also shown and the predictions compared with measurements
International Nuclear Information System (INIS)
The perturbation source method is used in the Monte Carlo method in calculating small effects in a particle field. It offers primising possibilities for introducing positive correlation between subtracting estimates even in the cases where other methods fail, in the case of geometrical variations of a given arrangement. The perturbation source method is formulated on the basis of integral equations for the particle fields. The formulae for the second moment of the difference of events are derived. Explicity a certain class of transport games and different procedures for generating the so-called perturbation particles are considered
On the approximate maximum likelihood estimation for diffusion processes
Chang, Jinyuan; 10.1214/11-AOS922
2012-01-01
The transition density of a diffusion process does not admit an explicit expression in general, which prevents the full maximum likelihood estimation (MLE) based on discretely observed sample paths. A\\"{\\i}t-Sahalia [J. Finance 54 (1999) 1361--1395; Econometrica 70 (2002) 223--262] proposed asymptotic expansions to the transition densities of diffusion processes, which lead to an approximate maximum likelihood estimation (AMLE) for parameters. Built on A\\"{\\i}t-Sahalia's [Econometrica 70 (2002) 223--262; Ann. Statist. 36 (2008) 906--937] proposal and analysis on the AMLE, we establish the consistency and convergence rate of the AMLE, which reveal the roles played by the number of terms used in the asymptotic density expansions and the sampling interval between successive observations. We find conditions under which the AMLE has the same asymptotic distribution as that of the full MLE. A first order approximation to the Fisher information matrix is proposed.
Smoothed log-concave maximum likelihood estimation with applications
Chen, Yining
2011-01-01
We study the smoothed log-concave maximum likelihood estimator of a probability distribution on $\\mathbb{R}^d$. This is a fully automatic nonparametric density estimator, obtained as a canonical smoothing of the log-concave maximum likelihood estimator. We demonstrate its attractive features both through an analysis of its theoretical properties and a simulation study. Moreover, we show how the estimator can be used as an intermediate stage of more involved procedures, such as constructing a classifier or estimating a functional of the density. Here again, the use of the estimator can be justified both on theoretical grounds and through its finite sample performance, and we illustrate its use in a breast cancer diagnosis (classification) problem.
MLEP: an R package for exploring the maximum likelihood estimates of penetrance parameters
Directory of Open Access Journals (Sweden)
Sugaya Yuki
2012-08-01
Full Text Available Abstract Background Linkage analysis is a useful tool for detecting genetic variants that regulate a trait of interest, especially genes associated with a given disease. Although penetrance parameters play an important role in determining gene location, they are assigned arbitrary values according to the researcher’s intuition or as estimated by the maximum likelihood principle. Several methods exist by which to evaluate the maximum likelihood estimates of penetrance, although not all of these are supported by software packages and some are biased by marker genotype information, even when disease development is due solely to the genotype of a single allele. Findings Programs for exploring the maximum likelihood estimates of penetrance parameters were developed using the R statistical programming language supplemented by external C functions. The software returns a vector of polynomial coefficients of penetrance parameters, representing the likelihood of pedigree data. From the likelihood polynomial supplied by the proposed method, the likelihood value and its gradient can be precisely computed. To reduce the effect of the supplied dataset on the likelihood function, feasible parameter constraints can be introduced into maximum likelihood estimates, thus enabling flexible exploration of the penetrance estimates. An auxiliary program generates a perspective plot allowing visual validation of the model’s convergence. The functions are collectively available as the MLEP R package. Conclusions Linkage analysis using penetrance parameters estimated by the MLEP package enables feasible localization of a disease locus. This is shown through a simulation study and by demonstrating how the package is used to explore maximum likelihood estimates. Although the input dataset tends to bias the likelihood estimates, the method yields accurate results superior to the analysis using intuitive penetrance values for disease with low allele frequencies. MLEP is
Energy Technology Data Exchange (ETDEWEB)
Idiri, Z. [Centre de Recherche Nucleaire d' Alger (CRNA), 02 Boulevard Frantz-Fanon, B.P. 399, 16000 Alger (Algeria)]. E-mail: zmidiri@yahoo.fr; Mazrou, H. [Centre de Recherche Nucleaire d' Alger (CRNA), 02 Boulevard Frantz-Fanon, B.P. 399, 16000 Alger (Algeria); Beddek, S. [Centre de Recherche Nucleaire d' Alger (CRNA), 02 Boulevard Frantz-Fanon, B.P. 399, 16000 Alger (Algeria); Amokrane, A. [Faculte de Physique, Universite des Sciences et de la Technologie Houari-Boumediene (USTHB), Alger (Algeria); Azbouche, A. [Centre de Recherche Nucleaire d' Alger (CRNA), 02 Boulevard Frantz-Fanon, B.P. 399, 16000 Alger (Algeria)
2007-07-21
The present paper describes the optimization of sample dimensions of a {sup 241}Am-Be neutron source-based Prompt gamma neutron activation analysis (PGNAA) setup devoted for in situ environmental water rejects analysis. The optimal dimensions have been achieved following extensive Monte Carlo neutron flux calculations using MCNP5 computer code. A validation process has been performed for the proposed preliminary setup with measurements of thermal neutron flux by activation technique of indium foils, bare and with cadmium covered sheet. Sensitive calculations were subsequently performed to simulate real conditions of in situ analysis by determining thermal neutron flux perturbations in samples according to chlorine and organic matter concentrations changes. The desired optimal sample dimensions were finally achieved once established constraints regarding neutron damage to semi-conductor gamma detector, pulse pile-up, dead time and radiation hazards were fully met.
Energy Technology Data Exchange (ETDEWEB)
Bankovic, A., E-mail: ana.bankovic@gmail.com [Institute of Physics, University of Belgrade, Pregrevica 118, 11080 Belgrade (Serbia); Dujko, S. [Institute of Physics, University of Belgrade, Pregrevica 118, 11080 Belgrade (Serbia); Centrum Wiskunde and Informatica (CWI), P.O. Box 94079, 1090 GB Amsterdam (Netherlands); ARC Centre for Antimatter-Matter Studies, School of Engineering and Physical Sciences, James Cook University, Townsville, QLD 4810 (Australia); White, R.D. [ARC Centre for Antimatter-Matter Studies, School of Engineering and Physical Sciences, James Cook University, Townsville, QLD 4810 (Australia); Buckman, S.J. [ARC Centre for Antimatter-Matter Studies, Australian National University, Canberra, ACT 0200 (Australia); Petrovic, Z.Lj. [Institute of Physics, University of Belgrade, Pregrevica 118, 11080 Belgrade (Serbia)
2012-05-15
This work reports on a new series of calculations of positron transport properties in molecular hydrogen under the influence of spatially homogeneous electric field. Calculations are performed using a Monte Carlo simulation technique and multi term theory for solving the Boltzmann equation. Values and general trends of the mean energy, drift velocity and diffusion coefficients as a function of the reduced electric field E/n{sub 0} are reported here. Emphasis is placed on the explicit and implicit effects of positronium (Ps) formation on the drift velocity and diffusion coefficients. Two important phenomena arise; first, for certain regions of E/n{sub 0} the bulk and flux components of the drift velocity and longitudinal diffusion coefficient are markedly different, both qualitatively and quantitatively. Second, and contrary to previous experience in electron swarm physics, there is negative differential conductivity (NDC) effect in the bulk drift velocity component with no indication of any NDC for the flux component. In order to understand this atypical manifestation of the drift and diffusion of positrons in H{sub 2} under the influence of electric field, the spatially dependent positron transport properties such as number of positrons, average energy and velocity and spatially resolved rate for Ps formation are calculated using a Monte Carlo simulation technique. The spatial variation of the positron average energy and extreme skewing of the spatial profile of positron swarm are shown to play a central role in understanding the phenomena.
De Vega, H J
2004-01-01
The self-gravitating gas in the presence of a positive cosmological constant Lambda is studied in thermal equilibrium by Monte Carlo simulations and by the mean field approach. We find excellent agreement between both approaches already for N = 1000 particles on a volume $V$ [The mean field is exact in the infinite N limit]. The domain of stability of the gas is found to increase when the cosmological constant increases. The particle density is shown to be an increasing (decreasing) function of the distance when the dark energy dominates over self-gravity (and vice-versa).We confirm the validity of the thermodynamic limit: N, V -> infty with N/V^{1/3} and Lambda V^{2/3} fixed. In such dilute limit extensive thermodynamic quantities like energy, free energy, entropy turn to be proportional to N. We find that the gas is stable till the isothermal compressibility diverges. Beyond this point the gas becomes a extremely dense object whose properties are studied by Monte Carlo.
Field, E. H.
2001-12-01
Given problems with virtually all previous earthquake-forecast models for southern California, and a current lack of consensus on how such models should be constructed, a joint SCEC-USGS sponsored working group for the development of Regional Earthquake Likelihood Models (RELM) has been established (www.relm.org). The goals are as follows: 1) To develop and test a range of viable earthquake-potential models for southern California (not just one "consensus" model); 2) To examine and compare the implications of each model with respect to probabilistic seismic-hazard estimates (which will not only quantify existing hazard uncertainties, but will also indicate how future research should be focused in order to reduce the uncertainties); and 3) To design and document conclusive tests of each model with respect to existing and future geophysical observations. The variety of models under development reflects the variety of geophysical constraints available; these include geological fault information, historical seismicity, geodetic observations, stress-transfer interactions, and foreshock/aftershock statistics. One reason for developing and testing a range of models is to evaluate the extent to which any one can be exported to another region where the options are more limited. RELM is not intended to be a one-time effort. Rather, we are building an infrastructure that will facilitate an ongoing incorporation of new scientific findings into seismic-hazard models. The effort involves the development of several community models and databases, one of which is new Java-based code for probabilistic seismic hazard analysis (PSHA). Although several different PSHA codes presently exist, none are open source, well documented, and written in an object-oriented programming language (which is ideally suited for PSHA). Furthermore, we need code that is flexible enough to accommodate the wide range of models currently under development in RELM. The new code is being developed under
Shrinkage Effect in Ancestral Maximum Likelihood
Mossel, Elchanan; Steel, Mike
2008-01-01
Ancestral maximum likelihood (AML) is a method that simultaneously reconstructs a phylogenetic tree and ancestral sequences from extant data (sequences at the leaves). The tree and ancestral sequences maximize the probability of observing the given data under a Markov model of sequence evolution, in which branch lengths are also optimized but constrained to take the same value on any edge across all sequence sites. AML differs from the more usual form of maximum likelihood (ML) in phylogenetics because ML averages over all possible ancestral sequences. ML has long been known to be statistically consistent -- that is, it converges on the correct tree with probability approaching 1 as the sequence length grows. However, the statistical consistency of AML has not been formally determined, despite informal remarks in a literature that dates back 20 years. In this short note we prove a general result that implies that AML is statistically inconsistent. In particular we show that AML can `shrink' short edges in a t...
Dunn, William L
2012-01-01
Exploring Monte Carlo Methods is a basic text that describes the numerical methods that have come to be known as "Monte Carlo." The book treats the subject generically through the first eight chapters and, thus, should be of use to anyone who wants to learn to use Monte Carlo. The next two chapters focus on applications in nuclear engineering, which are illustrative of uses in other fields. Five appendices are included, which provide useful information on probability distributions, general-purpose Monte Carlo codes for radiation transport, and other matters. The famous "Buffon's needle proble
What Determines the Likelihood of Structural Reforms?
Agnello, Luca; Castro, Vitor; Jalles, João Tovar; Sousa, Ricardo M.
2014-01-01
We use data for a panel of 60 countries over the period 1980-2005 to investigate the main drivers of the likelihood of structural reforms. We find that: (i) external debt crises are the main trigger of financial and banking reforms; (ii) inflation and banking crises are the key drivers of external capital account reforms; (iii) banking crises also hasten financial reforms; and (iv) economic recessions play an important role in promoting the necessary consensus for financial, capital, banking ...
International Nuclear Information System (INIS)
A detailed Monte Carlo N-Particle Transport Code (MCNP5) model of the University of Missouri research reactor (MURR) has been developed. The ability of the model to accurately predict isotope production rates was verified by comparing measured and calculated neutron- capture reaction rates for numerous isotopes. In addition to thermal (1/v) monitors, the benchmarking included a number of isotopes whose (n, γ) reaction rates are very sensitive to the epithermal portion of the neutron spectrum. Using the most recent neutron libraries (ENDF/ B-VII.0), the model was able to accurately predict the measured reaction rates in all cases. The model was then combined with ORIGEN 2.2, via MONTEBURNS 2.0, to calculate production of 99Mo from fission of low-enriched uranium foils. The model was used to investigate both annular and plate LEU foil targets in a variety of arrangements in a graphite irradiation wedge to optimize the production of 99Mo. (author)
Development and Application of MCNP5 and KENO-VI Monte Carlo Models for the Atucha-2 PHWR Analysis
Directory of Open Access Journals (Sweden)
M. Pecchia
2011-01-01
Full Text Available The geometrical complexity and the peculiarities of Atucha-2 PHWR require the adoption of advanced Monte Carlo codes for performing realistic neutronic simulations. Core models of Atucha-2 PHWR were developed using both MCNP5 and KENO-VI codes. The developed models were applied for calculating reactor criticality states at beginning of life, reactor cell constants, and control rods volumes. The last two applications were relevant for performing successive three dimensional neutron kinetic analyses since it was necessary to correctly evaluate the effect of each oblique control rod in each cell discretizing the reactor. These corrective factors were then applied to the cell cross sections calculated by the two-dimensional deterministic lattice physics code HELIOS. These results were implemented in the RELAP-3D model to perform safety analyses for the licensing process.
Development and Application of MCNP5 and KENO-VI Monte Carlo Models for the Atucha-2 PHWR Analysis
International Nuclear Information System (INIS)
The geometrical complexity and the peculiarities of Atucha-2 PHWR require the adoption of advanced Monte Carlo codes for performing realistic neutronic simulations. Core models of Atucha-2 PHWR were developed using both MCNP5 and KENO-VI codes. The developed models were applied for calculating reactor criticality states at beginning of life, reactor cell constants, and control rods volumes. The last two applications were relevant for performing successive three dimensional neutron kinetic analyses since it was necessary to correctly evaluate the effect of each oblique control rod in each cell discretizing the reactor. These corrective factors were then applied to the cell cross sections calculated by the two-dimensional deterministic lattice physics code Helios. These results were implemented in the RELAP-3D model to perform safety analyses for the licensing process.
On-board Dose Measurement and its Monte Carlo Analysis in a Low Level Waste Shipping Vessel
International Nuclear Information System (INIS)
On-board dose measurements were made in a shipping vessel for low level radioactive wastes, the Seiei Maru. The measured values are much smaller than the regulation values both on the hatch covers and in the accommodation area. The dose equivalent rates on the hatch cover are analysed by using a continuous energy Monte Carlo code, MCNP 4B, with two kinds of calculational models. One is the detailed model with the geometry of containers and LLW drums, and an asymmetrical source distribution. The results of the detailed calculation approached the shape of the measured dose rate distribution graphs. The other is the simplified model that mixes source volume uniformly. The calculated values obtained with the simplified model are twice as large as those calculated with the detailed model. (author)
Quantifying uncertainty, variability and likelihood for ordinary differential equation models
LENUS (Irish Health Repository)
Weisse, Andrea Y
2010-10-28
Abstract Background In many applications, ordinary differential equation (ODE) models are subject to uncertainty or variability in initial conditions and parameters. Both, uncertainty and variability can be quantified in terms of a probability density function on the state and parameter space. Results The partial differential equation that describes the evolution of this probability density function has a form that is particularly amenable to application of the well-known method of characteristics. The value of the density at some point in time is directly accessible by the solution of the original ODE extended by a single extra dimension (for the value of the density). This leads to simple methods for studying uncertainty, variability and likelihood, with significant advantages over more traditional Monte Carlo and related approaches especially when studying regions with low probability. Conclusions While such approaches based on the method of characteristics are common practice in other disciplines, their advantages for the study of biological systems have so far remained unrecognized. Several examples illustrate performance and accuracy of the approach and its limitations.
The variability in likelihood ratios due to different mechanisms.
Bright, Jo-Anne; Stevenson, Kate E; Curran, James M; Buckleton, John S
2015-01-01
Recently there has been a drive towards standardisation of forensic DNA interpretation methods resulting in the uptake of probabilistic interpretation software. Some of these software solutions utilise Markov chain Monte Carlo techniques (MCMC). They will not produce an identical answer after repeat interpretations of the same evidence profile because of the Monte Carlo aspect. This is a new source of variability within the forensic DNA analysis process. In this paper we explore the size of the MCMC variability within the interpretation software STRmix™ compared to other sources of variability in forensic DNA profiling including PCR, capillary electrophoresis load and injection, and the makeup of allele frequency databases. The MCMC variability within STRmix™ was shown to be the smallest source of variability in this process. PMID:25450791
ROESSEL, ROBERT A., JR.
THE FIRST SECTION OF THIS BOOK COVERS THE HISTORICAL AND CULTURAL BACKGROUND OF THE SAN CARLOS APACHE INDIANS, AS WELL AS AN HISTORICAL SKETCH OF THE DEVELOPMENT OF THEIR FORMAL EDUCATIONAL SYSTEM. THE SECOND SECTION IS DEVOTED TO THE PROBLEMS OF TEACHERS OF THE INDIAN CHILDREN IN GLOBE AND SAN CARLOS, ARIZONA. IT IS DIVIDED INTO THREE PARTS--(1)…
DEFF Research Database (Denmark)
Holm, Bent
2005-01-01
En indplacering af operahuset San Carlo i en kulturhistorisk repræsentationskontekst med særligt henblik på begrebet napolalità.......En indplacering af operahuset San Carlo i en kulturhistorisk repræsentationskontekst med særligt henblik på begrebet napolalità....
Bashkatov, A. N.; Genina, Elina A.; Kochubei, V. I.; Tuchin, Valerii V.
2006-12-01
Based on the digital image analysis and inverse Monte-Carlo method, the proximate analysis method is deve-loped and the optical properties of hairs of different types are estimated in three spectral ranges corresponding to three colour components. The scattering and absorption properties of hairs are separated for the first time by using the inverse Monte-Carlo method. The content of different types of melanin in hairs is estimated from the absorption coefficient. It is shown that the dominating type of melanin in dark hairs is eumelanin, whereas in light hairs pheomelanin dominates.
Comparisons of likelihood and machine learning methods of individual classification
Guinand, B.; Topchy, A.; Page, K.S.; Burnham-Curtis, M. K.; Punch, W.F.; Scribner, K.T.
2002-01-01
Classification methods used in machine learning (e.g., artificial neural networks, decision trees, and k-nearest neighbor clustering) are rarely used with population genetic data. We compare different nonparametric machine learning techniques with parametric likelihood estimations commonly employed in population genetics for purposes of assigning individuals to their population of origin (“assignment tests”). Classifier accuracy was compared across simulated data sets representing different levels of population differentiation (low and high FST), number of loci surveyed (5 and 10), and allelic diversity (average of three or eight alleles per locus). Empirical data for the lake trout (Salvelinus namaycush) exhibiting levels of population differentiation comparable to those used in simulations were examined to further evaluate and compare classification methods. Classification error rates associated with artificial neural networks and likelihood estimators were lower for simulated data sets compared to k-nearest neighbor and decision tree classifiers over the entire range of parameters considered. Artificial neural networks only marginally outperformed the likelihood method for simulated data (0–2.8% lower error rates). The relative performance of each machine learning classifier improved relative likelihood estimators for empirical data sets, suggesting an ability to “learn” and utilize properties of empirical genotypic arrays intrinsic to each population. Likelihood-based estimation methods provide a more accessible option for reliable assignment of individuals to the population of origin due to the intricacies in development and evaluation of artificial neural networks. In recent years, characterization of highly polymorphic molecular markers such as mini- and microsatellites and development of novel methods of analysis have enabled researchers to extend investigations of ecological and evolutionary processes below the population level to the level of
On divergences tests for composite hypotheses under composite likelihood
Martin, Nirian; Pardo, Leandro; Zografos, Konstantinos
2016-01-01
It is well-known that in some situations it is not easy to compute the likelihood function as the datasets might be large or the model is too complex. In that contexts composite likelihood, derived by multiplying the likelihoods of subjects of the variables, may be useful. The extension of the classical likelihood ratio test statistics to the framework of composite likelihoods is used as a procedure to solve the problem of testing in the context of composite likelihood. In this paper we intro...
Efficient maximum likelihood parameterization of continuous-time Markov processes
McGibbon, Robert T
2015-01-01
Continuous-time Markov processes over finite state-spaces are widely used to model dynamical processes in many fields of natural and social science. Here, we introduce an maximum likelihood estimator for constructing such models from data observed at a finite time interval. This estimator is drastically more efficient than prior approaches, enables the calculation of deterministic confidence intervals in all model parameters, and can easily enforce important physical constraints on the models such as detailed balance. We demonstrate and discuss the advantages of these models over existing discrete-time Markov models for the analysis of molecular dynamics simulations.
MAXIMUM LIKELIHOOD ESTIMATION FOR PERIODIC AUTOREGRESSIVE MOVING AVERAGE MODELS.
Vecchia, A.V.
1985-01-01
A useful class of models for seasonal time series that cannot be filtered or standardized to achieve second-order stationarity is that of periodic autoregressive moving average (PARMA) models, which are extensions of ARMA models that allow periodic (seasonal) parameters. An approximation to the exact likelihood for Gaussian PARMA processes is developed, and a straightforward algorithm for its maximization is presented. The algorithm is tested on several periodic ARMA(1, 1) models through simulation studies and is compared to moment estimation via the seasonal Yule-Walker equations. Applicability of the technique is demonstrated through an analysis of a seasonal stream-flow series from the Rio Caroni River in Venezuela.
Efficient computations with the likelihood ratio distribution.
Kruijver, Maarten
2015-01-01
What is the probability that the likelihood ratio exceeds a threshold t, if a specified hypothesis is true? This question is asked, for instance, when performing power calculations for kinship testing, when computing true and false positive rates for familial searching and when computing the power of discrimination of a complex mixture. Answering this question is not straightforward, since there is are a huge number of possible genotypic combinations to consider. Different solutions are found in the literature. Several authors estimate the threshold exceedance probability using simulation. Corradi and Ricciardi [1] propose a discrete approximation to the likelihood ratio distribution which yields a lower and upper bound on the probability. Nothnagel et al. [2] use the normal distribution as an approximation to the likelihood ratio distribution. Dørum et al. [3] introduce an algorithm that can be used for exact computation, but this algorithm is computationally intensive, unless the threshold t is very large. We present three new approaches to the problem. Firstly, we show how importance sampling can be used to make the simulation approach significantly more efficient. Importance sampling is a statistical technique that turns out to work well in the current context. Secondly, we present a novel algorithm for computing exceedance probabilities. The algorithm is exact, fast and can handle relatively large problems. Thirdly, we introduce an approach that combines the novel algorithm with the discrete approximation of Corradi and Ricciardi. This last approach can be applied to very large problems and yields a lower and upper bound on the exceedance probability. The use of the different approaches is illustrated with examples from forensic genetics, such as kinship testing, familial searching and mixture interpretation. The algorithms are implemented in an R-package called DNAprofiles, which is freely available from CRAN.
International Nuclear Information System (INIS)
Best estimate computer codes are increasingly used in nuclear industry for the accident management procedures and have been planned to be used for the licensing procedures. Contrary to conservative codes which are supposed to give penalizing results, best estimate codes attempt to calculate accidental transients in a realistic way. It becomes therefore of prime importance, in particular for technical organization as IRSN in charge of safety assessment, to know the uncertainty on the results of such codes. Thus, CSNI has sponsored few years ago (published in 1998) the Uncertainty Methods Study (UMS) program on uncertainty methodologies used for a SBLOCA transient (LSTF-CL-18) and is now supporting the BEMUSE program for a LBLOCA transient (LOFT-L2-5). The large majority of BEMUSE participants (9 out of 10) use uncertainty methodologies based on a probabilistic modelling and all of them use Monte-Carlo simulations to propagate the uncertainties through their computer codes. Also, all of 'probabilistic participants' intend to use order statistics to determine the sampling size of the Monte-Carlo simulation and to derive the uncertainty ranges associated to their computer calculations. The first aim of this paper is to remind the advantages and also the assumptions of the probabilistic modelling and more specifically of order statistics (as Wilks' formula) in uncertainty methodologies. Indeed Monte-Carlo methods provide flexible and extremely powerful techniques for solving many of the uncertainty propagation problems encountered in nuclear safety analysis. However it is important to keep in mind that probabilistic methods are data intensive. That means, probabilistic methods cannot produce robust results unless a considerable body of information has been collected. A main interest of the use of order statistics results is to allow to take into account an unlimited number of uncertain parameters and, from a restricted number of code calculations to provide statistical
Energy Technology Data Exchange (ETDEWEB)
Pavlova, Olga; Tsibulya, Anatoly [FSUE ' SSC RF-IPPE' , 249033, Bondarenko Square 1, Obninsk (Russian Federation)
2008-07-01
The critical facility BFS-1 critical facility was built at the Institute of Physics and Power Engineering (Obninsk, Russia) for full-scale modeling of fast-reactor cores, blankets, in-vessel shielding, and storage. Whereas BFS-1 is a fast-reactor assembly; however, it is a very flexible assembly that can easily be reconfigured to represent numerous other types of reactor designs. This paper describes specific problems with calculation of evaluation neutron physics characteristics of integral experiments performed on BFS facility. The analysis available integral experiments performed on different critical configuration of BFS facility were performed. Calculations of criticality, central reaction rate ratios, and fission rate distributions were carried out by the MCNP5 Monte-Carlo code with different files of evaluated nuclear data. MCNP calculations with multigroup library with 299 energy groups were also made for comparison with pointwise library calculations. (authors)
Multi-Channel Maximum Likelihood Pitch Estimation
DEFF Research Database (Denmark)
Christensen, Mads Græsbøll
2012-01-01
. This essentially means that the model allows for different conditions in the various channels, like different signal-to-noise ratios, microphone characteristics and reverberation. Moreover, the method does not assume that a certain array structure is used but rather relies on a more general model and......In this paper, a method for multi-channel pitch estimation is proposed. The method is a maximum likelihood estimator and is based on a parametric model where the signals in the various channels share the same fundamental frequency but can have different amplitudes, phases, and noise characteristics...
Monte carlo simulation for soot dynamics
Zhou, Kun
2012-01-01
A new Monte Carlo method termed Comb-like frame Monte Carlo is developed to simulate the soot dynamics. Detailed stochastic error analysis is provided. Comb-like frame Monte Carlo is coupled with the gas phase solver Chemkin II to simulate soot formation in a 1-D premixed burner stabilized flame. The simulated soot number density, volume fraction, and particle size distribution all agree well with the measurement available in literature. The origin of the bimodal distribution of particle size distribution is revealed with quantitative proof.
Talbo, V.; Mateos, J.; González, T.; Lechaux, Y.; Wichmann, N.; Bollaert, S.; Vasallo, B. G.
2015-10-01
Impact-ionization metal-oxide-semiconductor FETs (I-MOSFETs) are in competition with tunnel FETs (TFETs) in order to achieve the best behaviour for low power logic circuits. Concretely, III-V I-MOSFETs are being explored as promising devices due to the proper reliability, since the impact ionization events happen away from the gate oxide, and the high cutoff frequency, due to high electron mobility. To facilitate the design process from the physical point of view, a Monte Carlo (MC) model which includes both impact ionization and band-to-band tunnel is presented. Two ungated InGaAs and InAlAs/InGaAs 100 nm PIN diodes have been simulated. In both devices, the tunnel processes are more frequent than impact ionizations, so that they are found to be appropriate for TFET structures and not for I- MOSFETs. According to our simulations, other narrow bandgap candidates for the III-V heterostructure, such as InAs or GaSb, and/or PININ structures must be considered for a correct I-MOSFET design.
Monte Carlo design of a system for the detection of explosive materials and analysis of the dose
International Nuclear Information System (INIS)
The problems associated with insecurity and terrorism have forced to designing systems for detecting nuclear materials, drugs and explosives that are installed on roads, ports and airports. Organic materials are composed of C, H, O and N; similarly the explosive materials are manufactured which can be distinguished by the concentration of these elements. Its elemental composition, particularly the concentration of hydrogen and oxygen, allow distinguish them from other organic substances. When these materials are irradiated with neutrons nuclear reactions (n, γ) are produced, where the emitted photons are ready gamma rays whose energy is characteristic of each element and its abundance allows estimating their concentration. The aim of this study was designed using Monte Carlo methods a system with neutron source, gamma rays detector and moderator able to distinguish the presence of Rdx and urea. In design were used as moderators: paraffin, light water, polyethylene and graphite; as detectors were used HPGe and the NaI(Tl). The design that showed the best performance was the moderator of light water and HPGe, with a source of 241AmBe. For this design, the values of ambient dose equivalent around the system were calculated. (Author)
Low-Luminosity Gamma-Ray Bursts as a Distinct GRB Population:A Monte Carlo Analysis
Virgili, Francisco; Zhang, Bing
2008-01-01
The intriguing observations of {Swift}/BAT GRB 060218 and CGRO/BATSE burst 980425, both with much lower luminosity and redshift compared to other observed bursts, lead naturally to the question whether these low-luminosity (LL) bursts constitute a separate population from high-luminosity (HL) bursts. Utilizing Monte Carlo simulations we compare various single-component luminosity function (LF)models (single power law or broken power law) with the two-component luminosity function model proposed by Liang et al. Using various criteria, we demonstrate that the single-component LF models have great difficulty in simultaneously reproducing both the high local LL-GRB rate and the oberved distributions of redshift, luminosity, and $\\log N-\\log P$ for HL-GRBs. We argue that the two-component LF model is necessary, and we use the observed BATSE and Swift $\\log N-\\log P$ distributions to add constrains to the LL and HL-LF parameters. The LL-LF can be modeled by a smoothed, broken power law with a break at around $10^{4...
Energy Technology Data Exchange (ETDEWEB)
Han, Gi Young; Seo, Bo Kyun [Korea Institute of Nuclear Safety,, Daejeon (Korea, Republic of); Kim, Do Hyun; Shin, Chang Ho; Kim, Song Hyun [Dept. of Nuclear Engineering, Hanyang University, Seoul (Korea, Republic of); Sun, Gwang Min [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2016-06-15
In analyzing residual radiation, researchers generally use a two-step Monte Carlo (MC) simulation. The first step (MC1) simulates neutron transport, and the second step (MC2) transports the decay photons emitted from the activated materials. In this process, the stochastic uncertainty estimated by the MC2 appears only as a final result, but it is underestimated because the stochastic error generated in MC1 cannot be directly included in MC2. Hence, estimating the true stochastic uncertainty requires quantifying the propagation degree of the stochastic error in MC1. The brute force technique is a straightforward method to estimate the true uncertainty. However, it is a costly method to obtain reliable results. Another method, called the adjoint-based method, can reduce the computational time needed to evaluate the true uncertainty; however, there are limitations. To address those limitations, we propose a new strategy to estimate uncertainty propagation without any additional calculations in two-step MC simulations. To verify the proposed method, we applied it to activation benchmark problems and compared the results with those of previous methods. The results show that the proposed method increases the applicability and user-friendliness preserving accuracy in quantifying uncertainty propagation. We expect that the proposed strategy will contribute to efficient and accurate two-step MC calculations.
A Monte Carlo Analysis of Weight Data from UF_{6} Cylinder Feed and Withdrawal Stations
Energy Technology Data Exchange (ETDEWEB)
Garner, James R [ORNL; Whitaker, J Michael [ORNL
2015-01-01
As the number of nuclear facilities handling uranium hexafluoride (UF_{6}) cylinders (e.g., UF_{6} production, enrichment, and fuel fabrication) increase in number and throughput, more automated safeguards measures will likely be needed to enable the International Atomic Energy Agency (IAEA) to achieve its safeguards objectives in a fiscally constrained environment. Monitoring the process data from the load cells built into the cylinder feed and withdrawal (F/W) stations (i.e., cylinder weight data) can significantly increase the IAEA’s ability to efficiently achieve the fundamental safeguards task of confirming operations as declared (i.e., no undeclared activities). Researchers at the Oak Ridge National Laboratory, Los Alamos National Laboratory, the Joint Research Center (in Ispra, Italy), and University of Glasgow are investigating how this weight data can be used for IAEA safeguards purposes while fully protecting the operator’s proprietary and sensitive information related to operations. A key question that must be resolved is, what is the necessary frequency of recording data from the process F/W stations to achieve safeguards objectives? This paper summarizes Monte Carlo simulations of typical feed, product, and tails withdrawal cycles and evaluates longer sampling frequencies to determine the expected errors caused by low-frequency sampling and its impact on material balance calculations.
Lee, Y. K.; Malouch, F.
2009-08-01
In order to assess the possibility of swelling of austenitic steels for the core internals of pressurized water reactors (PWR), a multi-year irradiation program, called GONDOLE, is ongoing in the OSIRIS material testing reactor at the CEA-Saclay site. This experiment consists in the irradiation of several density specimens at high temperature (> 350 °C). The first phase of GONDOLE irradiation run was completed in January 2006 after six reactor cycles of twenty days and the surveillance dosimetry results of the first phase were available by the end of 2006. The purpose of this paper is to present the neutron calculation methodology performed for GONDOLE program by using the continuous-energy Monte Carlo 3D-transport code TRDPOLI-4. For the specimens of virgin materials and the dosimeters located at the core mid-plane, the calculation and measurement results of the first phase of irradiation run will be presented. In addition, prediction calculation of helium gas production in the virgin materials will be introduced.
International Nuclear Information System (INIS)
A good model on experimental data (criticality, control rod worth, and fuel element worth distributions) is encouraged to provide from the Musashi-TRIGA Mark 2 reactor. In the previous paper, as the keff values for different fuel loading patterns had been provided ranging from the minimum core to the full one, the data would be candidate for an ICSBEP evaluation. Evaluation of the control rod worth and fuel element worth distributions presented in this paper could be an excellent benchmark data applicable for validation of calculation technique used in the field of modern research reactor. As a result of simulation on the TRIGA-2 benchmark experiment, which was performed by three-dimensional continuous-energy Monte Carlo code (MCNP4A), it was found that the MCNP calculated values of control rod worth were consisted to the experimental data for both rod-drop and period methods. And for the fuel and the graphite element worth distributions, the MCNP calculated values agreed well with the measured ones though consideration of real control rod positions was needed for calculating fuel element reactivity positioned in inner ring. (G.K.)
Directory of Open Access Journals (Sweden)
Alan Jackson
Full Text Available PURPOSE: To compare semi-quantitative (SQ and pharmacokinetic (PK parameters for analysis of dynamic contrast enhanced MR data (DCE-MRI and investigate error-propagation in SQ parameters. METHODS: Clinical data was collected from five patients with type 2-neurofibromatosis (NF2 receiving anti-angiogenic therapy for rapidly growing vestibular schwannoma (VS. There were 7 VS and 5 meningiomas. Patients were scanned prior to therapy and at days 3 and 90 of treatment. Data was collected using a dual injection technique to permit direct comparison of SQ and PK parameters. Monte Carlo modeling was performed to assess potential measurement errors in SQ parameters in persistent, washout, and weakly enhancing tissues. The simulation predictions for five semi-quantitative parameters were tested using the clinical DCE-MRI data. RESULTS: In VS, SQ parameters and Ktrans showed close correlation and demonstrated similar therapy induced reductions. In meningioma, only the denoised Signal Enhancement Ratio (Rse1/se2(DN showed a significant therapy induced reduction (p<0.05. Simulation demonstrated: 1 Precision of SQ metrics normalized to the pre-contrast-baseline values (MSErel and ∑MSErel is improved by use of an averaged value from multiple baseline scans; 2 signal enhancement ratio Rmse1/mse2 shows considerable susceptibility to noise; 3 removal of outlier values to produce a new parameter, Rmse1/mse2(DN, improves precision and sensitivity to therapy induced changes. Direct comparison of in-vivo analysis with Monte Carlo simulation supported the simulation predicted error distributions of semi-quantitative metrics. CONCLUSION: PK and SQ parameters showed similar sensitivity to anti-angiogenic therapy induced changes in VS. Modeling studies confirmed the benefits of averaging baseline signal from multiple images for normalized SQ metrics and demonstrated poor noise tolerance in the widely used signal enhancement ratio, which is corrected by removal of
Maximum likelihood polynomial regression for robust speech recognition
Institute of Scientific and Technical Information of China (English)
LU Yong; WU Zhenyang
2011-01-01
The linear hypothesis is the main disadvantage of maximum likelihood linear re- gression （MLLR）. This paper applies the polynomial regression method to model adaptation and establishes a nonlinear model adaptation algorithm using maximum likelihood polyno
Composite likelihood and two-stage estimation in family studies
DEFF Research Database (Denmark)
Andersen, Elisabeth Anne Wreford
2002-01-01
Composite likelihood; Two-stage estimation; Family studies; Copula; Optimal weights; All possible pairs......Composite likelihood; Two-stage estimation; Family studies; Copula; Optimal weights; All possible pairs...
A.S. Kalwij
2000-01-01
This paper proposes an alternative estimation procedure for a panel data Tobit model with individual specific effects based on taking first differences of the equation of interest. This helps to alleviate the sensitivity of the estimates to a specific parameterization of the individual specific effects and some Monte Carlo evidence is provided in support of this. To allow for arbitrary serial correlation estimation takes place in two steps: Maximum Likelihood is applied to each pair of consec...
Maximum likelihood resampling of noisy, spatially correlated data
Goff, J.; Jenkins, C.
2005-12-01
In any geologic application, noisy data are sources of consternation for researchers, inhibiting interpretability and marring images with unsightly and unrealistic artifacts. Filtering is the typical solution to dealing with noisy data. However, filtering commonly suffers from ad hoc (i.e., uncalibrated, ungoverned) application, which runs the risk of erasing high variability components of the field in addition to the noise components. We present here an alternative to filtering: a newly developed methodology for correcting noise in data by finding the "best" value given the data value, its uncertainty, and the data values and uncertainties at proximal locations. The motivating rationale is that data points that are close to each other in space cannot differ by "too much", where how much is "too much" is governed by the field correlation properties. Data with large uncertainties will frequently violate this condition, and in such cases need to be corrected, or "resampled." The best solution for resampling is determined by the maximum of the likelihood function defined by the intersection of two probability density functions (pdf): (1) the data pdf, with mean and variance determined by the data value and square uncertainty, respectively, and (2) the geostatistical pdf, whose mean and variance are determined by the kriging algorithm applied to proximal data values. A Monte Carlo sampling of the data probability space eliminates non-uniqueness, and weights the solution toward data values with lower uncertainties. A test with a synthetic data set sampled from a known field demonstrates quantitatively and qualitatively the improvement provided by the maximum likelihood resampling algorithm. The method is also applied to three marine geology/geophysics data examples: (1) three generations of bathymetric data on the New Jersey shelf with disparate data uncertainties; (2) mean grain size data from the Adriatic Sea, which is combination of both analytic (low uncertainty
Waldmann, Patrik; Hallander, Jon; Hoti, Fabian; Sillanpää, Mikko J.
2008-01-01
Accurate and fast computation of quantitative genetic variance parameters is of great importance in both natural and breeding populations. For experimental designs with complex relationship structures it can be important to include both additive and dominance variance components in the statistical model. In this study, we introduce a Bayesian Gibbs sampling approach for estimation of additive and dominance genetic variances in the traditional infinitesimal model. The method can handle general pedigrees without inbreeding. To optimize between computational time and good mixing of the Markov chain Monte Carlo (MCMC) chains, we used a hybrid Gibbs sampler that combines a single site and a blocked Gibbs sampler. The speed of the hybrid sampler and the mixing of the single-site sampler were further improved by the use of pretransformed variables. Two traits (height and trunk diameter) from a previously published diallel progeny test of Scots pine (Pinus sylvestris L.) and two large simulated data sets with different levels of dominance variance were analyzed. We also performed Bayesian model comparison on the basis of the posterior predictive loss approach. Results showed that models with both additive and dominance components had the best fit for both height and diameter and for the simulated data with high dominance. For the simulated data with low dominance, we needed an informative prior to avoid the dominance variance component becoming overestimated. The narrow-sense heritability estimates in the Scots pine data were lower compared to the earlier results, which is not surprising because the level of dominance variance was rather high, especially for diameter. In general, the hybrid sampler was considerably faster than the blocked sampler and displayed better mixing properties than the single-site sampler. PMID:18558655
International Nuclear Information System (INIS)
Reactivity components composing the sodium void reactivity in a FBR core are analyzed by group-wise Monte Carlo Code GMVP, which has been developed by JAEA. The typical way to analyze the reactivity components is to use the perturbation method based on the diffusion calculations, while the diffusion approximation cannot be appropriately applied to some types of FBR cores containing large cavity regions. But, in order to prospect the optimized FBR core with negative sodium void reactivity, we need to the components of the sodium void reactivity of cores which have a small void reactivity, which cores are sometimes accompanied with adjacent large cavity regions or gas plenum zones. In this study, we have employed GMVP to simulate the cavity region exactly in geometry and to evaluate the neutron behavior rigorously in reactor physics. The cross section library used is JFS-3-J3.3 70 group constant set that is complied from JENDL-3.3 library. The objective core is a 'step type' two zone core, which has a lower inner core height relative to the height of the outer core, and the upper axial blanket is eliminated to enhance the neutron leakage in the upper ward at void conditions. The reactivity component by neutron leakage is derived from the difference of the k-effective of direct calculation of GMVP between the intact and void cores, and that of non-leakage components evaluated by using real and adjoint flux that are calculated with GMVP. In the paper, the change of the contributions of the both components is presented when the core height is changed along with the void reactivity of the cores. (author)
Waldmann, Patrik; Hallander, Jon; Hoti, Fabian; Sillanpää, Mikko J
2008-06-01
Accurate and fast computation of quantitative genetic variance parameters is of great importance in both natural and breeding populations. For experimental designs with complex relationship structures it can be important to include both additive and dominance variance components in the statistical model. In this study, we introduce a Bayesian Gibbs sampling approach for estimation of additive and dominance genetic variances in the traditional infinitesimal model. The method can handle general pedigrees without inbreeding. To optimize between computational time and good mixing of the Markov chain Monte Carlo (MCMC) chains, we used a hybrid Gibbs sampler that combines a single site and a blocked Gibbs sampler. The speed of the hybrid sampler and the mixing of the single-site sampler were further improved by the use of pretransformed variables. Two traits (height and trunk diameter) from a previously published diallel progeny test of Scots pine (Pinus sylvestris L.) and two large simulated data sets with different levels of dominance variance were analyzed. We also performed Bayesian model comparison on the basis of the posterior predictive loss approach. Results showed that models with both additive and dominance components had the best fit for both height and diameter and for the simulated data with high dominance. For the simulated data with low dominance, we needed an informative prior to avoid the dominance variance component becoming overestimated. The narrow-sense heritability estimates in the Scots pine data were lower compared to the earlier results, which is not surprising because the level of dominance variance was rather high, especially for diameter. In general, the hybrid sampler was considerably faster than the blocked sampler and displayed better mixing properties than the single-site sampler. PMID:18558655
Luo, Yuqun; Lin, Shili
2003-07-01
Genetic data from founder populations are advantageous for studies of complex traits that are often plagued by the problem of genetic heterogeneity. However, the desire to analyze large and complex pedigrees that often arise from such populations, coupled with the need to handle many linked and highly polymorphic loci simultaneously, poses challenges to current standard approaches. A viable alternative to solving such problems is via Markov chain Monte Carlo (MCMC) procedures, where a Markov chain, defined on the state space of a latent variable (e.g., genotypic configuration or inheritance vector), is constructed. However, finding starting points for the Markov chains is a difficult problem when the pedigree is not single-locus peelable; methods proposed in the literature have not yielded completely satisfactory solutions. We propose a generalization of the heated Gibbs sampler with relaxed penetrances (HGRP) of Lin et al., ([1993] IMA J. Math. Appl. Med. Biol. 10:1-17) to search for starting points. HGRP guarantees that a starting point will be found if there is no error in the data, but the chain usually needs to be run for a long time if the pedigree is extremely large and complex. By introducing a forcing step, the current algorithm substantially reduces the state space, and hence effectively speeds up the process of finding a starting point. Our algorithm also has a built-in preprocessing procedure for Mendelian error detection. The algorithm has been applied to both simulated and real data on two large and complex Hutterite pedigrees under many settings, and good results are obtained. The algorithm has been implemented in a user-friendly package called START. PMID:12813723
International Nuclear Information System (INIS)
Monte Carlo simulations of nuclear criticality eigenvalue problems are often performed by general purpose radiation transport codes such as MCNP. MCNP performs detailed statistical analysis of the criticality calculation and provides feedback to the user with warning messages, tables, and graphs. The purpose of the analysis is to provide the user with sufficient information to assess spatial convergence of the eigenfunction and thus the validity of the criticality calculation. As a test of this statistical analysis package in MCNP, analytic criticality verification benchmark problems have been used for the first time to assess the performance of the criticality convergence tests in MCNP. The MCNP statistical analysis capability has been recently assessed using the 75 multigroup criticality verification analytic problem test set. MCNP was verified with these problems at the 10-4 to 10-5 statistical error level using 40 000 histories per cycle and 2000 active cycles. In all cases, the final boxed combined keff answer was given with the standard deviation and three confidence intervals that contained the analytic keff. To test the effectiveness of the statistical analysis checks in identifying poor eigenfunction convergence, ten problems from the test set were deliberately run incorrectly using 1000 histories per cycle, 200 active cycles, and 10 inactive cycles. Six problems with large dominance ratios were chosen from the test set because they do not achieve the normal spatial mode in the beginning of the calculation. To further stress the convergence tests, these problems were also started with an initial fission source point 1 cm from the boundary thus increasing the likelihood of a poorly converged initial fission source distribution. The final combined keff confidence intervals for these deliberately ill-posed problems did not include the analytic keff value. In no case did a bad confidence interval go undetected. Warning messages were given signaling that the
Nonparametric likelihood based estimation of linear filters for point processes
DEFF Research Database (Denmark)
Hansen, Niels Richard
2015-01-01
result is a representation of the gradient of the log-likelihood, which we use to derive computable approximations of the log-likelihood and the gradient by time discretization. These approximations are then used to minimize the approximate penalized log-likelihood. For time and memory efficiency...
DEFF Research Database (Denmark)
Rasmussen, Morten
Fast and Accurate Identification of Cross-Linked Peptides for the structural analysis of large protein complexes and to elucidate interaction networks. Salman Tahir Jimi-Carlo Bukowski-Wills; Morten Rasmussen; Juri RappsilberWellcome Trust Centre for Cell Biology, Edinburgh , United Kingdom Novel...
Vibrato Monte Carlo and the calculation of greeks
Keegan, Sinead
2008-01-01
In computational ¯nance Monte Carlo simulation can be used to calculate the correct prices of ¯nancial options, and to compute the values of the as- sociated Greeks (the derivatives of the option price with respect to certain input parameters). The main methods used for the calculation of Greeks are finite difference, likelihood ratio, and pathwise sensitivity. Each of these has its limitations and in particular the pathwise sensitivity approach may not be used for an option...
Monte Carlo Radiative Transfer
Whitney, Barbara A
2011-01-01
I outline methods for calculating the solution of Monte Carlo Radiative Transfer (MCRT) in scattering, absorption and emission processes of dust and gas, including polarization. I provide a bibliography of relevant papers on methods with astrophysical applications.
Monte Carlo transition probabilities
Lucy, L. B.
2001-01-01
Transition probabilities governing the interaction of energy packets and matter are derived that allow Monte Carlo NLTE transfer codes to be constructed without simplifying the treatment of line formation. These probabilities are such that the Monte Carlo calculation asymptotically recovers the local emissivity of a gas in statistical equilibrium. Numerical experiments with one-point statistical equilibrium problems for Fe II and Hydrogen confirm this asymptotic behaviour. In addition, the re...
Dishonestly increasing the likelihood of winning
Directory of Open Access Journals (Sweden)
Shaul Shalvi
2012-05-01
Full Text Available People not only seek to avoid losses or secure gains; they also attempt to create opportunities for obtaining positive outcomes. When distributing money between gambles with equal probabilities, people often invest in turning negative gambles into positive ones, even at a cost of reduced expected value. Results of an experiment revealed that (1 the preference to turn a negative outcome into a positive outcome exists when people's ability to do so depends on their performance levels (rather than merely on their choice, (2 this preference is amplified when the likelihood to turn negative into positive is high rather than low, and (3 this preference is attenuated when people can lie about their performance levels, allowing them to turn negative into positive not by performing better but rather by lying about how well they performed.
Maximum likelihood estimation of fractionally cointegrated systems
DEFF Research Database (Denmark)
Lasak, Katarzyna
In this paper we consider a fractionally cointegrated error correction model and investigate asymptotic properties of the maximum likelihood (ML) estimators of the matrix of the cointe- gration relations, the degree of fractional cointegration, the matrix of the speed of adjustment to the...... equilibrium parameters and the variance-covariance matrix of the error term. We show that using ML principles to estimate jointly all parameters of the fractionally cointegrated system we obtain consistent estimates and provide their asymptotic distributions. The cointegration matrix is asymptotically mixed...... any influence on the long-run relationship. The rate of convergence of the estimators of the long-run relationships depends on the coin- tegration degree but it is optimal for the strong cointegration case considered. We also prove that misspecification of the degree of fractional cointegation does...
Cosmological Parameters from CMB Maps without Likelihood Approximation
Racine, B.; Jewell, J. B.; Eriksen, H. K.; Wehus, I. K.
2016-03-01
We propose an efficient Bayesian Markov chain Monte Carlo (MCMC) algorithm for estimating cosmological parameters from cosmic microwave background (CMB) data without the use of likelihood approximations. It builds on a previously developed Gibbs sampling framework that allows for exploration of the joint CMB sky signal and power spectrum posterior, P({\\boldsymbol{s}},{C}{\\ell }| {\\boldsymbol{d}}), and addresses a long-standing problem of efficient parameter estimation simultaneously in regimes of high and low signal-to-noise ratio. To achieve this, our new algorithm introduces a joint Markov chain move in which both the signal map and power spectrum are synchronously modified, by rescaling the map according to the proposed power spectrum before evaluating the Metropolis-Hastings accept probability. Such a move was already introduced by Jewell et al., who used it to explore low signal-to-noise posteriors. However, they also found that the same algorithm is inefficient in the high signal-to-noise regime, since a brute-force rescaling operation does not account for phase information. This problem is mitigated in the new algorithm by subtracting the Wiener filter mean field from the proposed map prior to rescaling, leaving high signal-to-noise information invariant in the joint step, and effectively only rescaling the low signal-to-noise component. To explore the full posterior, the new joint move is then interleaved with a standard conditional Gibbs move for the sky map. We apply our new algorithm to simplified simulations for which we can evaluate the exact posterior to study both its accuracy and its performance, and find good agreement with the exact posterior; marginal means agree to ≲0.006σ and standard deviations to better than ˜3%. The Markov chain correlation length is of the same order of magnitude as those obtained by other standard samplers in the field.
Shedlock, Daniel; Haghighat, Alireza
2005-01-01
In the United States, the Nuclear Waste Policy Act of 1982 mandated centralised storage of spent nuclear fuel by 1988. However, the Yucca Mountain project is currently scheduled to start accepting spent nuclear fuel in 2010. Since many nuclear power plants were only designed for -10 y of spent fuel pool storage, > 35 plants have been forced into alternate means of spent fuel storage. In order to continue operation and make room in spent fuel pools, nuclear generators are turning towards independent spent fuel storage installations (ISFSIs). Typical vertical concrete ISFSIs are -6.1 m high and 3.3 m in diameter. The inherently large system, and the presence of thick concrete shields result in difficulties for both Monte Carlo (MC) and discrete ordinates (SN) calculations. MC calculations require significant variance reduction and multiple runs to obtain a detailed dose distribution. SN models need a large number of spatial meshes to accurately model the geometry and high quadrature orders to reduce ray effects, therefore, requiring significant amounts of computer memory and time. The use of various differencing schemes is needed to account for radial heterogeneity in material cross sections and densities. Two P3, S12, discrete ordinate, PENTRAN (parallel environment neutral-particle TRANsport) models were analysed and different MC models compared. A multigroup MCNP model was developed for direct comparison to the SN models. The biased A3MCNP (automated adjoint accelerated MCNP) and unbiased (MCNP) continuous energy MC models were developed to assess the adequacy of the CASK multigroup (22 neutron, 18 gamma) cross sections. The PENTRAN SN results are in close agreement (5%) with the multigroup MC results; however, they differ by -20-30% from the continuous-energy MC predictions. This large difference can be attributed to the expected difference between multigroup and continuous energy cross sections, and the fact that the CASK library is based on the old ENDF
The Multi-Mission Maximum Likelihood framework (3ML)
Vianello, Giacomo; Younk, Patrick; Tibaldo, Luigi; Burgess, James M; Ayala, Hugo; Harding, Patrick; Hui, Michelle; Omodei, Nicola; Zhou, Hao
2015-01-01
Astrophysical sources are now observed by many different instruments at different wavelengths, from radio to high-energy gamma-rays, with an unprecedented quality. Putting all these data together to form a coherent view, however, is a very difficult task. Each instrument has its own data format, software and analysis procedure, which are difficult to combine. It is for example very challenging to perform a broadband fit of the energy spectrum of the source. The Multi-Mission Maximum Likelihood framework (3ML) aims to solve this issue, providing a common framework which allows for a coherent modeling of sources using all the available data, independent of their origin. At the same time, thanks to its architecture based on plug-ins, 3ML uses the existing official software of each instrument for the corresponding data in a way which is transparent to the user. 3ML is based on the likelihood formalism, in which a model summarizing our knowledge about a particular region of the sky is convolved with the instrument...
LikeDM: likelihood calculator of dark matter detection
Huang, Xiaoyuan; Yuan, Qiang
2016-01-01
With the large progresses of searching for dark matter (DM) particles from indirect and direct methods, we develop a numerical tool which enables fast calculation of the likelihood of specified DM particle models given a number of observational data, such as charged cosmic rays from space-borne experiments (e.g., PAMELA, AMS-02), $\\gamma$-rays from Fermi space telescope, and the underground direct detection experiments. The purpose of this tool, \\likedm\\ --- likelihood calculator of dark matter detection, is to bridge the particle model of DM and the observational data. The intermediate steps between these two, including the astrophysical backgrounds, the propagation of charged particles, the analysis of Fermi $\\gamma$-ray data, as well as the DM velocity distribution and the nuclear form factor, have been dealt with in the code. We release the first version (v1.0) focusing on the constraints of charged cosmic and gamma rays and the direct detection part will be implemented in the next version. This manual de...
tmle : An R Package for Targeted Maximum Likelihood Estimation
Directory of Open Access Journals (Sweden)
Susan Gruber
2012-11-01
Full Text Available Targeted maximum likelihood estimation (TMLE is a general approach for constructing an efficient double-robust semi-parametric substitution estimator of a causal effect parameter or statistical association measure. tmle is a recently developed R package that implements TMLE of the effect of a binary treatment at a single point in time on an outcome of interest, controlling for user supplied covariates, including an additive treatment effect, relative risk, odds ratio, and the controlled direct effect of a binary treatment controlling for a binary intermediate variable on the pathway from treatment to the out- come. Estimation of the parameters of a marginal structural model is also available. The package allows outcome data with missingness, and experimental units that contribute repeated records of the point-treatment data structure, thereby allowing the analysis of longitudinal data structures. Relevant factors of the likelihood may be modeled or fit data-adaptively according to user specifications, or passed in from an external estimation procedure. Effect estimates, variances, p values, and 95% confidence intervals are provided by the software.
Kusoglu Sarikaya, C.; Rafatov, I.; Kudryavtsev, A. A.
2016-06-01
The work deals with the Particle in Cell/Monte Carlo Collision (PIC/MCC) analysis of the problem of detection and identification of impurities in the nonlocal plasma of gas discharge using the Plasma Electron Spectroscopy (PLES) method. For this purpose, 1d3v PIC/MCC code for numerical simulation of glow discharge with nonlocal electron energy distribution function is developed. The elastic, excitation, and ionization collisions between electron-neutral pairs and isotropic scattering and charge exchange collisions between ion-neutral pairs and Penning ionizations are taken into account. Applicability of the numerical code is verified under the Radio-Frequency capacitively coupled discharge conditions. The efficiency of the code is increased by its parallelization using Open Message Passing Interface. As a demonstration of the PLES method, parallel PIC/MCC code is applied to the direct current glow discharge in helium doped with a small amount of argon. Numerical results are consistent with the theoretical analysis of formation of nonlocal EEDF and existing experimental data.
Van Der Perk, Marcel; Burema, Jiske; Vandenhove, Hildegarde; Goor, François; Timofeyev, Sergei
2004-09-01
A Monte Carlo analysis of two sequential GIS-embedded submodels, which evaluate the economic feasibility of short rotation coppice (SRC) production and energy conversion in areas contaminated by Chernobyl-derived (137)Cs, was performed to allow for variability of environmental conditions that was not contained in the spatial model inputs. The results from this analysis were compared to the results from the deterministic model presented in part I of this paper. It was concluded that, although the variability in the model results due to within-gridcell variability of the model inputs was considerable, the prediction of the areas where SRC and energy conversion is potentially profitable was robust. If the additional variability in the model input that is not contained in the input maps is also taken into account, the SRC production and energy conversion appears to be potentially profitable at more locations for both the small scale and large scale production scenarios than the model predicted using the deterministic model.
Bonavita, M; Desidera, S; Gratton, R; Janson, M; Beuzit, J L; Kasper, M; Mordasini, C
2011-01-01
The high number of planet discoveries made in the last years provides a good sample for statistical analysis, leading to some clues on the distributions of planet parameters, like masses and periods, at least in close proximity to the host star. We likely need to wait for the extremely large telescopes (ELTs) to have an overall view of the extrasolar planetary systems. In this context it would be useful to have a tool that can be used for the interpretation of the present results,and also to predict what the outcomes would be of the future instruments. For this reason we built MESS: a Monte Carlo simulation code which uses either the results of the statistical analysis of the properties of discovered planets, or the results of the planet formation theories, to build synthetic planet populations fully described in terms of frequency, orbital elements and physical properties. They can then be used to either test the consistency of their properties with the observed population of planets given different detectio...
Pineda Rojas, Andrea L.; Venegas, Laura E.; Mazzeo, Nicolás A.
2016-09-01
A simple urban air quality model [MODelo de Dispersión Atmosférica Ubana - Generic Reaction Set (DAUMOD-GRS)] was recently developed. One-hour peak O3 concentrations in the Metropolitan Area of Buenos Aires (MABA) during the summer estimated with the DAUMOD-GRS model have shown values lower than 20 ppb (the regional background concentration) in the urban area and levels greater than 40 ppb in its surroundings. Due to the lack of measurements outside the MABA, these relatively high ozone modelled concentrations constitute the only estimate for the area. In this work, a methodology based on the Monte Carlo analysis is implemented to evaluate the uncertainty in these modelled concentrations associated to possible errors of the model input data. Results show that the larger 1-h peak O3 levels in the MABA during the summer present larger uncertainties (up to 47 ppb). On the other hand, multiple linear regression analysis is applied at selected receptors in order to identify the variables explaining most of the obtained variance. Although their relative contributions vary spatially, the uncertainty of the regional background O3 concentration dominates at all the analysed receptors (34.4-97.6%), indicating that their estimations could be improved to enhance the ability of the model to simulate peak O3 concentrations in the MABA.
Communication Network Reliability Analysis Monte Carlo method --Status and Prospect%蒙特·卡罗方法的现状和展望
Institute of Scientific and Technical Information of China (English)
王建秋
2011-01-01
Communication network, transmission network, integrated circuit network, transport network has now been throughout all aspects of social life, their reliability is related to the beneficial to the people＇s livelihood, their reliability research are very important. Due to the complexity of communication network, the communication network reliability analysis system has the quite great difficulty, so this paper Monte Carlo method, the communication network system reliability analysis research.%通信网络、输电网络、集成电路网络、交通网络等网络现今已遍布社会生活的各个方面，它们的可靠性关系到国计民生，对它们可靠性研究有十分重要的意义。由于通信网络等的复杂度。对通信网络的系统可靠性分析具有相当大的难度，所以本文蒙特·卡罗方法对通信网络系统可靠性分析进行了深入的研究。
2009-01-01
Carlo Rubbia turned 75 on March 31, and CERN held a symposium to mark his birthday and pay tribute to his impressive contribution to both CERN and science. Carlo Rubbia, 4th from right, together with the speakers at the symposium.On 7 April CERN hosted a celebration marking Carlo Rubbia’s 75th birthday and 25 years since he was awarded the Nobel Prize for Physics. "Today we will celebrate 100 years of Carlo Rubbia" joked CERN’s Director-General, Rolf Heuer in his opening speech, "75 years of his age and 25 years of the Nobel Prize." Rubbia received the Nobel Prize along with Simon van der Meer for contributions to the discovery of the W and Z bosons, carriers of the weak interaction. During the symposium, which was held in the Main Auditorium, several eminent speakers gave lectures on areas of science to which Carlo Rubbia made decisive contributions. Among those who spoke were Michel Spiro, Director of the French National Insti...
Robust maximum likelihood estimation for stochastic state space model with observation outliers
AlMutawa, J.
2016-08-01
The objective of this paper is to develop a robust maximum likelihood estimation (MLE) for the stochastic state space model via the expectation maximisation algorithm to cope with observation outliers. Two types of outliers and their influence are studied in this paper: namely,the additive outlier (AO) and innovative outlier (IO). Due to the sensitivity of the MLE to AO and IO, we propose two techniques for robustifying the MLE: the weighted maximum likelihood estimation (WMLE) and the trimmed maximum likelihood estimation (TMLE). The WMLE is easy to implement with weights estimated from the data; however, it is still sensitive to IO and a patch of AO outliers. On the other hand, the TMLE is reduced to a combinatorial optimisation problem and hard to implement but it is efficient to both types of outliers presented here. To overcome the difficulty, we apply the parallel randomised algorithm that has a low computational cost. A Monte Carlo simulation result shows the efficiency of the proposed algorithms. An earlier version of this paper was presented at the 8th Asian Control Conference, Kaohsiung, Taiwan, 2011.
Maximum likelihood versus likelihood-free quantum system identification in the atom maser
Catana, Catalin; Kypraios, Theodore; Guţă, Mădălin
2014-10-01
We consider the problem of estimating a dynamical parameter of a Markovian quantum open system (the atom maser), by performing continuous time measurements in the system's output (outgoing atoms). Two estimation methods are investigated and compared. Firstly, the maximum likelihood estimator (MLE) takes into account the full measurement data and is asymptotically optimal in terms of its mean square error. Secondly, the ‘likelihood-free’ method of approximate Bayesian computation (ABC) produces an approximation of the posterior distribution for a given set of summary statistics, by sampling trajectories at different parameter values and comparing them with the measurement data via chosen statistics. Building on previous results which showed that atom counts are poor statistics for certain values of the Rabi angle, we apply MLE to the full measurement data and estimate its Fisher information. We then select several correlation statistics such as waiting times, distribution of successive identical detections, and use them as input of the ABC algorithm. The resulting posterior distribution follows closely the data likelihood, showing that the selected statistics capture ‘most’ statistical information about the Rabi angle.
Energy Technology Data Exchange (ETDEWEB)
Kim, Chang Hyo; Hong, In Seob; Han, Beom Seok; Jeong, Jong Seong [Seoul National University, Seoul (Korea)
2002-03-01
The objective of this project is to verify neutronics characteristics of the SMART core design as to compare computational results of the MCNAP code with those of the MASTER code. To achieve this goal, we will analyze neutronics characteristics of the SMART core using the MCNAP code and compare these results with results of the MASTER code. We improved parallel computing module and developed error analysis module of the MCNAP code. We analyzed mechanism of the error propagation through depletion computation and developed a calculation module for quantifying these errors. We performed depletion analysis for fuel pins and assemblies of the SMART core. We modeled a 3-D structure of the SMART core and considered a variation of material compositions by control rods operation and performed depletion analysis for the SMART core. We computed control-rod worths of assemblies and a reactor core for operation of individual control-rod groups. We computed core reactivity coefficients-MTC, FTC and compared these results with computational results of the MASTER code. To verify error analysis module of the MCNAP code, we analyzed error propagation through depletion of the SMART B-type assembly. 18 refs., 102 figs., 36 tabs. (Author)
Hrivnacova, I; Berejnov, V V; Brun, R; Carminati, F; Fassò, A; Futo, E; Gheata, A; Caballero, I G; Morsch, Andreas
2003-01-01
The concept of Virtual Monte Carlo (VMC) has been developed by the ALICE Software Project to allow different Monte Carlo simulation programs to run without changing the user code, such as the geometry definition, the detector response simulation or input and output formats. Recently, the VMC classes have been integrated into the ROOT framework, and the other relevant packages have been separated from the AliRoot framework and can be used individually by any other HEP project. The general concept of the VMC and its set of base classes provided in ROOT will be presented. Existing implementations for Geant3, Geant4 and FLUKA and simple examples of usage will be described.
Lin, Chin; Chu, Chi-Ming; Su, Sui-Lung
2016-01-01
Conventional genome-wide association studies (GWAS) have been proven to be a successful strategy for identifying genetic variants associated with complex human traits. However, there is still a large heritability gap between GWAS and transitional family studies. The “missing heritability” has been suggested to be due to lack of studies focused on epistasis, also called gene–gene interactions, because individual trials have often had insufficient sample size. Meta-analysis is a common method f...
Behera, Rakesh K.; Watanabe, Taku; Andersson, David A.; Uberuaga, Blas P.; Deo, Chaitanya S.
2016-04-01
Oxygen interstitials in UO2+x significantly affect the thermophysical properties and microstructural evolution of the oxide nuclear fuel. In hyperstoichiometric Urania (UO2+x), these oxygen interstitials form different types of defect clusters, which have different migration behavior. In this study we have used kinetic Monte Carlo (kMC) to evaluate diffusivities of oxygen interstitials accounting for mono- and di-interstitial clusters. Our results indicate that the predicted diffusivities increase significantly at higher non-stoichiometry (x > 0.01) for di-interstitial clusters compared to a mono-interstitial only model. The diffusivities calculated at higher temperatures compare better with experimental values than at lower temperatures (analysis to estimate the effect of input di-interstitial binding energies on the predicted diffusivities and activation energies. While this article only discusses mono- and di-interstitials in evaluating oxygen diffusion response in UO2+x, future improvements to the model will primarily focus on including energetic definitions of larger stable interstitial clusters reported in the literature. The addition of larger clusters to the kMC model is expected to improve the comparison of oxygen transport in UO2+x with experiment.
Andersson, K E; Madejski, G M
2006-01-01
We propose a new Monte Carlo method to study extended X-ray sources with the European Photon Imaging Camera (EPIC) aboard XMM Newton. The Smoothed Particle Inference (SPI) technique, described in a companion paper, is applied here to the EPIC data for the clusters of galaxies Abell 1689, Centaurus and RXJ 0658-55 (the ``bullet cluster''). We aim to show the advantages of this method of simultaneous spectral-spatial modeling over traditional X-ray spectral analysis. In Abell 1689 we confirm our earlier findings about structure in temperature distribution and produce a high resolution temperature map. We also confirm our findings about velocity structure within the gas. In the bullet cluster, RXJ 0658-55, we produce the highest resolution temperature map yet to be published of this cluster allowing us to trace what looks like the motion of the bullet in the cluster. We even detect a south to north temperature gradient within the bullet itself. In the Centaurus cluster we detect, by dividing up the luminosity of...
Analysis of Investment Risk Based on Monte Carlo Method%蒙特卡洛法在投资项目风险分析中的应用
Institute of Scientific and Technical Information of China (English)
王霞; 张本涛; 马庆
2011-01-01
本文以经济净现值为评价指标来度量项目的投资风险,确定各影响因素的概率分布,建立了基于三角分布的风险评价的随机模型,采用蒙特卡罗方法进行模拟,利用MATLAB编程实现,得到投资项目的净现值频数分布的直方图和累计频率曲线图,并对模拟结果进行统计和分析,可得到净现值的平均预测值以及风险率,为评价投资项目的风险提供了理论依据.%In this paper, based on the important economic evaluation index NPV, the paper measures the risk of investment projects, determines the probability distribution of various factors, establishes the risk evaluation of the stochastic model based on the triangular distribution, which is simulated using Monte Carlo method, and realises by MATLAB programming, then can get the frequency distribution histograms and cumulative frequency curve of the net present value of investment projects, predictive average value and the rate risk are obtained by statistic analysis,providing a theoretical basis for risk evaluation of investment projects.
Energy Technology Data Exchange (ETDEWEB)
Abanades, A., E-mail: abanades@etsii.upm.es [Grupo de Modelizacion de Sistemas Termoenergeticos, ETSII, Universidad Politecnica de Madrid, c/Ramiro de Maeztu, 7, 28040 Madrid (Spain); Alvarez-Velarde, F.; Gonzalez-Romero, E.M. [Centro de Investigaciones Medioambientales y Tecnologicas (CIEMAT), Avda. Complutense, 40, Ed. 17, 28040 Madrid (Spain); Ismailov, K. [Tokyo Institute of Technology, 2-12-1, O-okayama, Meguro-ku, Tokyo 152-8550 (Japan); Lafuente, A. [Grupo de Modelizacion de Sistemas Termoenergeticos, ETSII, Universidad Politecnica de Madrid, c/Ramiro de Maeztu, 7, 28040 Madrid (Spain); Nishihara, K. [Transmutation Section, J-PARC Center, JAEA, Tokai-mura, Ibaraki-ken 319-1195 (Japan); Saito, M. [Tokyo Institute of Technology, 2-12-1, O-okayama, Meguro-ku, Tokyo 152-8550 (Japan); Stanculescu, A. [International Atomic Energy Agency (IAEA), Vienna (Austria); Sugawara, T. [Transmutation Section, J-PARC Center, JAEA, Tokai-mura, Ibaraki-ken 319-1195 (Japan)
2013-01-15
Highlights: Black-Right-Pointing-Pointer TARC experiment benchmark capture rates results. Black-Right-Pointing-Pointer Utilization of updated databases, included ADSLib. Black-Right-Pointing-Pointer Self-shielding effect in reactor design for transmutation. Black-Right-Pointing-Pointer Effect of Lead nuclear data. - Abstract: The design of Accelerator Driven Systems (ADS) requires the development of simulation tools that are able to describe in a realistic way their nuclear performance and transmutation rate capability. In this publication, we present an evaluation of state of the art Monte Carlo design tools to assess their performance concerning transmutation of long-lived fission products. This work, performed under the umbrella of the International Atomic Energy Agency, analyses two important aspects for transmutation systems: moderation on Lead and neutron captures of {sup 99}Tc, {sup 127}I and {sup 129}I. The analysis of the results shows how shielding effects due to the resonances at epithermal energies of these nuclides affects strongly their transmutation rate. The results suggest that some research effort should be undertaken to improve the quality of Iodine nuclear data at epithermal and fast neutron energy to obtain a reliable transmutation estimation.
Directory of Open Access Journals (Sweden)
Bin Zhou
Full Text Available It is difficult to evaluate and compare interventions for reducing exposure to air pollutants, including polycyclic aromatic hydrocarbons (PAHs, a widely found air pollutant in both indoor and outdoor air. This study presents the first application of the Monte Carlo population exposure assessment model to quantify the effects of different intervention strategies on inhalation exposure to PAHs and the associated lung cancer risk. The method was applied to the population in Beijing, China, in the year 2006. Several intervention strategies were designed and studied, including atmospheric cleaning, smoking prohibition indoors, use of clean fuel for cooking, enhancing ventilation while cooking and use of indoor cleaners. Their performances were quantified by population attributable fraction (PAF and potential impact fraction (PIF of lung cancer risk, and the changes in indoor PAH concentrations and annual inhalation doses were also calculated and compared. The results showed that atmospheric cleaning and use of indoor cleaners were the two most effective interventions. The sensitivity analysis showed that several input parameters had major influence on the modeled PAH inhalation exposure and the rankings of different interventions. The ranking was reasonably robust for the remaining majority of parameters. The method itself can be extended to other pollutants and in different places. It enables the quantitative comparison of different intervention strategies and would benefit intervention design and relevant policy making.
King, Martin D; Crowder, Martin J; Hand, David J; Harris, Neil G; Williams, Stephen R; Obrenovitch, Tihomir P; Gadian, David G
2003-06-01
Markov chain Monte Carlo simulation was used in a reanalysis of the longitudinal data obtained by Harris et al. (J Cereb Blood Flow Metab 20:28-36) in a study of the direct current (DC) potential and apparent diffusion coefficient (ADC) responses to focal ischemia. The main purpose was to provide a formal analysis of the temporal relationship between the ADC and DC responses, to explore the possible involvement of a common latent (driving) process. A Bayesian nonlinear hierarchical random coefficients model was adopted. DC and ADC transition parameter posterior probability distributions were generated using three parallel Markov chains created using the Metropolis algorithm. Particular attention was paid to the within-subject differences between the DC and ADC time course characteristics. The results show that the DC response is biphasic, whereas the ADC exhibits monophasic behavior, and that the two DC components are each distinguishable from the ADC response in their time dependencies. The DC and ADC changes are not, therefore, driven by a common latent process. This work demonstrates a general analytical approach to the multivariate, longitudinal data-processing problem that commonly arises in stroke and other biomedical research. PMID:12796716
Mudra, Regina M.; Nadler, Andreas; Keller, Emanuela; Niederer, Peter F.
2006-07-01
Near-infrared spectroscopy (NIRS) combined with indocyanine green (ICG) dilution is applied externally on the head to determine the cerebral hemodynamics of neurointensive care patients. We applied Monte Carlo simulation for the analysis of a number of problems associated with this method. First, the contamination of the optical density (OD) signal due to the extracerebral tissue was assessed. Second, the measured OD signal depends essentially on the relative blood content (with respect to its absorption) in the various transilluminated tissues. To take this into account, we weighted the calculated densities of the photon distribution under baseline conditions within the different tissues with the changes and aberration of the relative blood volumes that are typically observed under healthy and pathologic conditions. Third, in case of NIRS ICG dye dilution, an ICG bolus replaces part of the blood such that a transient change of absorption in the brain tissues occurs that can be recorded in the OD signal. Our results indicate that for an exchange fraction of Δ=30% of the relative blood volume within the intracerebral tissue, the OD signal is determined from 64 to 74% by the gray matter and between 8 to 16% by the white matter maximally for a distance of d=4.5 cm.
Transfer Entropy as a Log-likelihood Ratio
Barnett, Lionel
2012-01-01
Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the neurosciences, econometrics and the analysis of complex system dynamics in diverse fields. We show that for a class of parametrised partial Markov models for jointly stochastic processes in discrete time, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. The result generalises the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression. In the general case, an asymptotic $\\chi^2$ distribution for the model transfer entropy estimator is established.
Transfer Entropy as a Log-Likelihood Ratio
Barnett, Lionel; Bossomaier, Terry
2012-09-01
Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.
Educational differences in likelihood of attributing breast symptoms to cancer
DEFF Research Database (Denmark)
Marcu, Afrodita; Lyratzopoulos, Georgios; Black, Georgia;
2016-01-01
asked to indicate their level of agreement with a cancer avoidance statement ('I would not want to know if I have breast cancer'). RESULTS: Women were more likely to mention cancer as a possible cause of an axillary lump (64%) compared with nipple rash (30%). In multivariable analysis, low and mid......BACKGROUND: Stage at diagnosis of breast cancer varies by socio-economic status (SES), with lower SES associated with poorer survival. We investigated associations between SES (indexed by education), and the likelihood of attributing breast symptoms to breast cancer. METHOD: We conducted an online...... survey with 961 women (47-92 years) with variable educational levels. Two vignettes depicted familiar and unfamiliar breast changes (axillary lump and nipple rash). Without making breast cancer explicit, women were asked 'What do you think this […..] could be?' After the attribution question, women were...
Likelihood ratios: Clinical application in day-to-day practice
Directory of Open Access Journals (Sweden)
Parikh Rajul
2009-01-01
Full Text Available In this article we provide an introduction to the use of likelihood ratios in clinical ophthalmology. Likelihood ratios permit the best use of clinical test results to establish diagnoses for the individual patient. Examples and step-by-step calculations demonstrate the estimation of pretest probability, pretest odds, and calculation of posttest odds and posttest probability using likelihood ratios. The benefits and limitations of this approach are discussed.
Maximum likelihood molecular clock comb: analytic solutions.
Chor, Benny; Khetan, Amit; Snir, Sagi
2006-04-01
Maximum likelihood (ML) is increasingly used as an optimality criterion for selecting evolutionary trees, but finding the global optimum is a hard computational task. Because no general analytic solution is known, numeric techniques such as hill climbing or expectation maximization (EM), are used in order to find optimal parameters for a given tree. So far, analytic solutions were derived only for the simplest model--three taxa, two state characters, under a molecular clock. Four taxa rooted trees have two topologies--the fork (two subtrees with two leaves each) and the comb (one subtree with three leaves, the other with a single leaf). In a previous work, we devised a closed form analytic solution for the ML molecular clock fork. In this work, we extend the state of the art in the area of analytic solutions ML trees to the family of all four taxa trees under the molecular clock assumption. The change from the fork topology to the comb incurs a major increase in the complexity of the underlying algebraic system and requires novel techniques and approaches. We combine the ultrametric properties of molecular clock trees with the Hadamard conjugation to derive a number of topology dependent identities. Employing these identities, we substantially simplify the system of polynomial equations. We finally use tools from algebraic geometry (e.g., Gröbner bases, ideal saturation, resultants) and employ symbolic algebra software to obtain analytic solutions for the comb. We show that in contrast to the fork, the comb has no closed form solutions (expressed by radicals in the input data). In general, four taxa trees can have multiple ML points. In contrast, we can now prove that under the molecular clock assumption, the comb has a unique (local and global) ML point. (Such uniqueness was previously shown for the fork.).
Likelihood-based inference with singular information matrix
Rotnitzky, Andrea; David R Cox; Bottai, Matteo; Robins, James
2000-01-01
We consider likelihood-based asymptotic inference for a p-dimensional parameter θ of an identifiable parametric model with singular information matrix of rank p-1 at θ=θ* and likelihood differentiable up to a specific order. We derive the asymptotic distribution of the likelihood ratio test statistics for the simple null hypothesis that θ=θ* and of the maximum likelihood estimator (MLE) of θ when θ=θ*. We show that there exists a reparametrization such that the MLE of the last p-1 components ...
Leonardo Rossi
Carlo Caso (1940 - 2007) Our friend and colleague Carlo Caso passed away on July 7th, after several months of courageous fight against cancer. Carlo spent most of his scientific career at CERN, taking an active part in the experimental programme of the laboratory. His long and fruitful involvement in particle physics started in the sixties, in the Genoa group led by G. Tomasini. He then made several experiments using the CERN liquid hydrogen bubble chambers -first the 2000HBC and later BEBC- to study various facets of the production and decay of meson and baryon resonances. He later made his own group and joined the NA27 Collaboration to exploit the EHS Spectrometer with a rapid cycling bubble chamber as vertex detector. Amongst their many achievements, they were the first to measure, with excellent precision, the lifetime of the charmed D mesons. At the start of the LEP era, Carlo and his group moved to the DELPHI experiment, participating in the construction and running of the HPC electromagnetic c...
Energy Technology Data Exchange (ETDEWEB)
Wollaber, Allan Benton [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-06-16
This is a powerpoint which serves as lecture material for the Parallel Computing summer school. It goes over the fundamentals of Monte Carlo. Welcome to Los Alamos, the birthplace of “Monte Carlo” for computational physics. Stanislaw Ulam, John von Neumann, and Nicholas Metropolis are credited as the founders of modern Monte Carlo methods. The name “Monte Carlo” was chosen in reference to the Monte Carlo Casino in Monaco (purportedly a place where Ulam’s uncle went to gamble). The central idea (for us) – to use computer-generated “random” numbers to determine expected values or estimate equation solutions – has since spread to many fields. "The first thoughts and attempts I made to practice [the Monte Carlo Method] were suggested by a question which occurred to me in 1946 as I was convalescing from an illness and playing solitaires. The question was what are the chances that a Canfield solitaire laid out with 52 cards will come out successfully? After spending a lot of time trying to estimate them by pure combinatorial calculations, I wondered whether a more practical method than “abstract thinking” might not be to lay it out say one hundred times and simply observe and count the number of successful plays... Later [in 1946], I described the idea to John von Neumann, and we began to plan actual calculations." - Stanislaw Ulam.
International Nuclear Information System (INIS)
This paper describes the problems encountered in the analysis of the Encapsulated Nuclear Heat Source (ENHS) core benchmark and the new cross section libraries developed to overcome these problems. The ENHS is a new lead-bismuth or lead cooled novel reactor concept that is fuelled with metallic alloy of Pu, U and Zr, and is designed to operate for 20 effective full power years without re-fuelling and with very small burn-up reactivity swing. There are numerous uncertainties in the prediction of core parameters of this and other innovative reactor designs, arising from approximations used in the solution of the transport equation, in nuclear data processing and cross section libraries generation. In this paper we analyzed the effects of uncertainties in lead cross sections data from several versions of ENDF, JENDL and JEFF for lead-cooled and reflected computational benchmarks. (author)
Analysis of dpa Rates in the HFIR Reactor Vessel using a Hybrid Monte Carlo/Deterministic Method*
Directory of Open Access Journals (Sweden)
Risner J.M.
2016-01-01
Full Text Available The Oak Ridge High Flux Isotope Reactor (HFIR, which began full-power operation in 1966, provides one of the highest steady-state neutron flux levels of any research reactor in the world. An ongoing vessel integrity analysis program to assess radiation-induced embrittlement of the HFIR reactor vessel requires the calculation of neutron and gamma displacements per atom (dpa, particularly at locations near the beam tube nozzles, where radiation streaming effects are most pronounced. In this study we apply the Forward-Weighted Consistent Adjoint Driven Importance Sampling (FW-CADIS technique in the ADVANTG code to develop variance reduction parameters for use in the MCNP radiation transport code. We initially evaluated dpa rates for dosimetry capsule locations, regions in the vicinity of the HB-2 beamline, and the vessel beltline region. We then extended the study to provide dpa rate maps using three-dimensional cylindrical mesh tallies that extend from approximately 12 in. below to approximately 12 in. above the height of the core. The mesh tally structures contain over 15,000 mesh cells, providing a detailed spatial map of neutron and photon dpa rates at all locations of interest. Relative errors in the mesh tally cells are typically less than 1%.
Analysis of dpa rates in the HFIR reactor vessel using a hybrid Monte Carlo/deterministic method
Energy Technology Data Exchange (ETDEWEB)
Blakeman, Edward [Retired
2016-01-01
The Oak Ridge High Flux Isotope Reactor (HFIR), which began full-power operation in 1966, provides one of the highest steady-state neutron flux levels of any research reactor in the world. An ongoing vessel integrity analysis program to assess radiation-induced embrittlement of the HFIR reactor vessel requires the calculation of neutron and gamma displacements per atom (dpa), particularly at locations near the beam tube nozzles, where radiation streaming effects are most pronounced. In this study we apply the Forward-Weighted Consistent Adjoint Driven Importance Sampling (FW-CADIS) technique in the ADVANTG code to develop variance reduction parameters for use in the MCNP radiation transport code. We initially evaluated dpa rates for dosimetry capsule locations, regions in the vicinity of the HB-2 beamline, and the vessel beltline region. We then extended the study to provide dpa rate maps using three-dimensional cylindrical mesh tallies that extend from approximately 12 below to approximately 12 above the axial extent of the core. The mesh tally structures contain over 15,000 mesh cells, providing a detailed spatial map of neutron and photon dpa rates at all locations of interest. Relative errors in the mesh tally cells are typically less than 1%.
Analysis of dpa Rates in the HFIR Reactor Vessel using a Hybrid Monte Carlo/Deterministic Method
Risner, J. M.; Blakeman, E. D.
2016-02-01
The Oak Ridge High Flux Isotope Reactor (HFIR), which began full-power operation in 1966, provides one of the highest steady-state neutron flux levels of any research reactor in the world. An ongoing vessel integrity analysis program to assess radiation-induced embrittlement of the HFIR reactor vessel requires the calculation of neutron and gamma displacements per atom (dpa), particularly at locations near the beam tube nozzles, where radiation streaming effects are most pronounced. In this study we apply the Forward-Weighted Consistent Adjoint Driven Importance Sampling (FW-CADIS) technique in the ADVANTG code to develop variance reduction parameters for use in the MCNP radiation transport code. We initially evaluated dpa rates for dosimetry capsule locations, regions in the vicinity of the HB-2 beamline, and the vessel beltline region. We then extended the study to provide dpa rate maps using three-dimensional cylindrical mesh tallies that extend from approximately 12 in. below to approximately 12 in. above the height of the core. The mesh tally structures contain over 15,000 mesh cells, providing a detailed spatial map of neutron and photon dpa rates at all locations of interest. Relative errors in the mesh tally cells are typically less than 1%. Notice: This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC0500OR22725 with the US Department of Energy. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the US Government purposes.
Calibration of two complex ecosystem models with different likelihood functions
Hidy, Dóra; Haszpra, László; Pintér, Krisztina; Nagy, Zoltán; Barcza, Zoltán
2014-05-01
goodness metric on calibration. The different likelihoods are different functions of RMSE (root mean squared error) weighted by measurement uncertainty: exponential / linear / quadratic / linear normalized by correlation. As a first calibration step sensitivity analysis was performed in order to select the influential parameters which have strong effect on the output data. In the second calibration step only the sensitive parameters were calibrated (optimal values and confidence intervals were calculated). In case of PaSim more parameters were found responsible for the 95% of the output data variance than is case of BBGC MuSo. Analysis of the results of the optimized models revealed that the exponential likelihood estimation proved to be the most robust (best model simulation with optimized parameter, highest confidence interval increase). The cross-validation of the model simulations can help in constraining the highly uncertain greenhouse gas budget of grasslands.
DEFF Research Database (Denmark)
Baker, R.; Candresse, T.; Dormannsné Simon, E.;
2010-01-01
The EFSA Plant Health Unit asked the Assessment Methodology Unit to recalculate and update the Quantitative Pathway Analysis of the exposure of the EU wheat production area with Tilletia indica M. teliospores provided by USDA APHIS. Simulations were computed, for importations of US wheat into EU....... This technical report contains a full description of the calculations and results. The description and discussion of the scenario and model can be found in the Scientific Opinion on a quantitative pathway analysis of the likelihood of Tilletia indica M. introduction into EU with importation of US wheat of EFSA...
Analysis of Gumbel Model for Software Reliability Using Bayesian Paradigm
Directory of Open Access Journals (Sweden)
Raj Kumar
2012-12-01
Full Text Available In this paper, we have illustrated the suitability of Gumbel Model for software reliability data. The model parameters are estimated using likelihood based inferential procedure: classical as well as Bayesian. The quasi Newton-Raphson algorithm is applied to obtain the maximum likelihood estimates and associated probability intervals. The Bayesian estimates of the parameters of Gumbel model are obtained using Markov Chain Monte Carlo(MCMC simulation method in OpenBUGS(established software for Bayesian analysis using Markov Chain Monte Carlo methods. The R functions are developed to study the statistical properties, model validation and comparison tools of the model and the output analysis of MCMC samples generated from OpenBUGS. Details of applying MCMC to parameter estimation for the Gumbel model are elaborated and a real software reliability data set is considered to illustrate the methods of inference discussed in this paper.
Energy Technology Data Exchange (ETDEWEB)
Hernandez A, P. L.; Medina C, D.; Rodriguez I, J. L.; Salas L, M. A.; Vega C, H. R., E-mail: pabloyae_2@hotmail.com [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Cipres No. 10, Fracc. La Penuela, 98068 Zacatecas, Zac. (Mexico)
2015-10-15
The problems associated with insecurity and terrorism have forced to designing systems for detecting nuclear materials, drugs and explosives that are installed on roads, ports and airports. Organic materials are composed of C, H, O and N; similarly the explosive materials are manufactured which can be distinguished by the concentration of these elements. Its elemental composition, particularly the concentration of hydrogen and oxygen, allow distinguish them from other organic substances. When these materials are irradiated with neutrons nuclear reactions (n, γ) are produced, where the emitted photons are ready gamma rays whose energy is characteristic of each element and its abundance allows estimating their concentration. The aim of this study was designed using Monte Carlo methods a system with neutron source, gamma rays detector and moderator able to distinguish the presence of Rdx and urea. In design were used as moderators: paraffin, light water, polyethylene and graphite; as detectors were used HPGe and the NaI(Tl). The design that showed the best performance was the moderator of light water and HPGe, with a source of {sup 241}AmBe. For this design, the values of ambient dose equivalent around the system were calculated. (Author)
EMPIRICAL LIKELIHOOD FOR LINEAR MODELS UNDER m-DEPENDENT ERRORS
Institute of Scientific and Technical Information of China (English)
QinYongsong; JiangBo; LiYufang
2005-01-01
In this paper，the empirical likelihood confidence regions for the regression coefficient in a linear model are constructed under m-dependent errors. It is shown that the blockwise empirical likelihood is a good way to deal with dependent samples.
Penalized maximum likelihood estimation for generalized linear point processes
DEFF Research Database (Denmark)
Hansen, Niels Richard
2010-01-01
-likelihood. Of particular interest is when the intensity is expressed in terms of a linear filter parametrized by a Sobolev space. Using that the Sobolev spaces are reproducing kernel Hilbert spaces we derive results on the representation of the penalized maximum likelihood estimator in a special case and the gradient...
Planck 2013 results. XV. CMB power spectra and likelihood
DEFF Research Database (Denmark)
Tauber, Jan; Bartlett, J.G.; Bucher, M.;
2014-01-01
This paper presents the Planck 2013 likelihood, a complete statistical description of the two-point correlation function of the CMB temperature fluctuations that accounts for all known relevant uncertainties, both instrumental and astrophysical in nature. We use this likelihood to derive our best...
Empirical likelihood inference for diffusion processes with jumps
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
In this paper, we consider the empirical likelihood inference for the jump-diffusion model. We construct the confidence intervals based on the empirical likelihood for the infinitesimal moments in the jump-diffusion models. They are better than the confidence intervals which are based on the asymptotic normality of point estimates.
The modified signed likelihood statistic and saddlepoint approximations
DEFF Research Database (Denmark)
Jensen, Jens Ledet
1992-01-01
SUMMARY: For a number of tests in exponential families we show that the use of a normal approximation to the modified signed likelihood ratio statistic r * is equivalent to the use of a saddlepoint approximation. This is also true in a large deviation region where the signed likelihood ratio...... statistic r is of order √ n. © 1992 Biometrika Trust....
INTERACTING MULTIPLE MODEL ALGORITHM BASED ON JOINT LIKELIHOOD ESTIMATION
Institute of Scientific and Technical Information of China (English)
Sun Jie; Jiang Chaoshu; Chen Zhuming; Zhang Wei
2011-01-01
A novel approach is proposed for the estimation of likelihood on Interacting Multiple-Model (IMM) filter.In this approach,the actual innovation,based on a mismatched model,can be formulated as sum of the theoretical innovation based on a matched model and the distance between matched and mismatched models,whose probability distributions are known.The joint likelihood of innovation sequence can be estimated by convolution of the two known probability density functions.The likelihood of tracking models can be calculated by conditional probability formula.Compared with the conventional likelihood estimation method,the proposed method improves the estimation accuracy of likelihood and robustness of IMM,especially when maneuver occurs.
Likelihood transform: making optimization and parameter estimation easier
Wang, Yan
2014-01-01
Parameterized optimization and parameter estimation is of great importance in almost every branch of modern science, technology and engineering. A practical issue in the problem is that when the parameter space is large and the available data is noisy, the geometry of the likelihood surface in the parameter space will be complicated. This makes searching and optimization algorithms computationally expensive, sometimes even beyond reach. In this paper, we define a likelihood transform which can make the structure of the likelihood surface much simpler, hence reducing the intrinsic complexity and easing optimization significantly. We demonstrate the properties of likelihood transform by apply it to a simplified gravitational wave chirp signal search. For the signal with an signal-to-noise ratio 20, likelihood transform has made a deterministic template-based search possible for the first time, which turns out to be 1000 times more efficient than an exhaustive grid- based search. The method in principle can be a...
Dai, Mi; Wang, Yun
2015-01-01
In order to obtain robust cosmological constraints from Type Ia supernova (SN Ia) data, we have applied Markov Chain Monte Carlo (MCMC) to SN Ia lightcurve fitting. We develop a method for sampling the resultant probability density distributions (pdf) of the SN Ia lightcuve parameters in the MCMC likelihood analysis to constrain cosmological parameters, and validate it using simulated data sets. Applying this method to the Joint Lightcurve Analysis (JLA) data set of SNe Ia, we find that sampl...
International Nuclear Information System (INIS)
The possibility of determining physical quantities (such as the number of particles behind shields of given thickness, energy spectra, detector responses, etc.) with a satisfactory statistical uncertainty, in a relatively short computing time, can be used as a measure of the efficiency of a Monte Carlo method. The numerical simulation of rare events with a straightforward Monte Carlo method is inefficient due to the great number of histories, without scores. In this paper, for the specific geometry of a gamma backscattered thickness gauge, with 60Co and 137Cs as gamma sources, the back-scattered gamma spectrum, probabilities for back-scattering and the spectral characteristics of the detector response were determined using a nonanalog Monte Carlo game with statistical weights applied. (author)
Energy Technology Data Exchange (ETDEWEB)
Burgio, N., E-mail: nunzio.burgio@enea.it [ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development), C.R. Casaccia Via Anguillarese 301, 00123 Rome (Italy); Cretara, L., E-mail: luca.cretara@uniroma1.it [DIAEE – Sapienza University of Rome, Corso Vittorio Emanuele II 244, 00186 Rome (Italy); Frullini, M., E-mail: massimo.frullini@uniroma1.it [DIAEE – Sapienza University of Rome, Corso Vittorio Emanuele II 244, 00186 Rome (Italy); Gandini, A., E-mail: augusto.gandini@uniroma1.it [DIAEE – Sapienza University of Rome, Corso Vittorio Emanuele II 244, 00186 Rome (Italy); Peluso, V., E-mail: vincenzogiuseppe.peluso@enea.it [ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development), Via Martiri di monte Sole 4, 40129 Bologna (Italy); Santagata, A., E-mail: alfonso.santagata@enea.it [ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development), C.R. Casaccia, Via Anguillarese 301, 00123 Rome (Italy)
2014-07-01
Highlights: • We develop a MCNPX model of the TAPIRO fast research reactor. • The model has been tested against the result of a late experimental champaign finding on overall agreement. • The source of uncertainties in the nuclear data and in the model assumptions has been discussed. • The model is sufficiently accurate to design irradiation experiment in support to R and D activities on LFR and ADS systems. - Abstract: After Fukushima events, the Italian nuclear program has been redefined leaving space only to activities related to Generation IV nuclear systems. Accordingly with this renewed national scenario, TAPIRO fast reactor facility is gaining a relatively major strategic role. A program is in fact being proposed to host in TAPIRO benchmark experimental activities related to the development of Lead fast reactor and Accelerator Driven Systems. A first step of this program would consist on the validation of neutronic codes, cross section data and reactor models to be adopted for its analysis. Along this line in this work the results of a simulation study has been made relevant to the measurements performed in the SCK-CEN/ENEA experimental campaign carried out in the 1980–1986 period. The calculations have been made using the Monte Carlo MCNPX 2.7.0 Code. In this article the main results are presented and discussed, with particular emphasis on the uncertainties, relevant both to nuclear data and the model layout. The results of this simulation study indicate in particular that TAPIRO's MCNPX model is adequate for the optimization of set-ups of perspective neutron irradiation experiments, this allowing cuts in costs and development time.
Ku, B.; Nam, M.
2012-12-01
Neutron logging has been widely used to estimate neutron porosity to evaluate formation properties in oil industry. More recently, neutron logging has been highlighted for monitoring the behavior of CO2 injected into reservoir for geological CO2 sequestration. For a better understanding of neutron log interpretation, Monte Carlo N-Particle (MCNP) algorithm is used to illustrate the response of a neutron tool. In order to obtain calibration curves for the neutron tool, neutron responses are simulated in water-filled limestone, sandstone and dolomite formations of various porosities. Since the salinities (concentration of NaCl) of borehole fluid and formation water are important factors for estimating formation porosity, we first compute and analyze neutron responses for brine-filled formations with different porosities. Further, we consider changes in brine saturation of a reservoir due to hydrocarbon production or geological CO2 sequestration to simulate corresponding neutron logging data. As gas saturation decreases, measured neutron porosity confirms gas effects on neutron logging, which is attributed to the fact that gas has slightly smaller number of hydrogen than brine water. In the meantime, increase in CO2 saturation due to CO2 injection reduces measured neutron porosity giving a clue to estimation the CO2 saturation, since the injected CO2 substitute for the brine water. A further analysis on the reduction gives a strategy for estimating CO2 saturation based on time-lapse neutron logging. This strategy can help monitoring not only geological CO2 sequestration but also CO2 flood for enhanced-oil-recovery. Acknowledgements: This work was supported by the Energy Efficiency & Resources of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Knowledge Economy (No. 2012T100201588). Myung Jin Nam was partially supported by the National Research Foundation of Korea(NRF) grant funded by the Korea
Inferring fixed effects in a mixed linear model from an integrated likelihood
DEFF Research Database (Denmark)
Gianola, Daniel; Sorensen, Daniel
2008-01-01
A new method for likelihood-based inference of fixed effects in mixed linear models, with variance components treated as nuisance parameters, is presented. The method uses uniform-integration of the likelihood; the implementation employs the expectation-maximization (EM) algorithm for elimination...... of all nuisances, viewing random effects and variance components as missing data. In a simulation of a grazing trial, the procedure was compared with four widely used estimators of fixed effects in mixed models, and found to be competitive. An analysis of body weight in freshwater crayfish was conducted...
Monte Carlo and nonlinearities
Dauchet, Jérémi; Blanco, Stéphane; Caliot, Cyril; Charon, Julien; Coustet, Christophe; Hafi, Mouna El; Eymet, Vincent; Farges, Olivier; Forest, Vincent; Fournier, Richard; Galtier, Mathieu; Gautrais, Jacques; Khuong, Anaïs; Pelissier, Lionel; Piaud, Benjamin; Roger, Maxime; Terrée, Guillaume; Weitz, Sebastian
2016-01-01
The Monte Carlo method is widely used to numerically predict systems behaviour. However, its powerful incremental design assumes a strong premise which has severely limited application so far: the estimation process must combine linearly over dimensions. Here we show that this premise can be alleviated by projecting nonlinearities on a polynomial basis and increasing the configuration-space dimension. Considering phytoplankton growth in light-limited environments, radiative transfer in planetary atmospheres, electromagnetic scattering by particles and concentrated-solar-power-plant productions, we prove the real world usability of this advance on four test-cases that were so far regarded as impracticable by Monte Carlo approaches. We also illustrate an outstanding feature of our method when applied to sharp problems with interacting particles: handling rare events is now straightforward. Overall, our extension preserves the features that made the method popular: addressing nonlinearities does not compromise o...
Yours in revolution : retrofitting Carlos the Jackal.
Samuel Thomas
2013-01-01
This paper explores the representation of ‘Carlos the Jackal’, the one-time ‘World’s Most Wanted Man’ and ‘International Face of Terror’ – primarily in cin-ema but also encompassing other forms of popular culture and aspects of Cold War policy-making. At the centre of the analysis is Olivier Assayas’s Carlos (2010), a transnational, five and a half hour film (first screened as a TV mini-series) about the life and times of the infamous militant. Concentrating on the var-ious ways in which Assa...
Monte Carlo simulation of neutron scattering instruments
International Nuclear Information System (INIS)
A library of Monte Carlo subroutines has been developed for the purpose of design of neutron scattering instruments. Using small-angle scattering as an example, the philosophy and structure of the library are described and the programs are used to compare instruments at continuous wave (CW) and long-pulse spallation source (LPSS) neutron facilities. The Monte Carlo results give a count-rate gain of a factor between 2 and 4 using time-of-flight analysis. This is comparable to scaling arguments based on the ratio of wavelength bandwidth to resolution width
Maximum Likelihood Blind Channel Estimation for Space-Time Coding Systems
Directory of Open Access Journals (Sweden)
Hakan A. Çırpan
2002-05-01
Full Text Available Sophisticated signal processing techniques have to be developed for capacity enhancement of future wireless communication systems. In recent years, space-time coding is proposed to provide significant capacity gains over the traditional communication systems in fading wireless channels. Space-time codes are obtained by combining channel coding, modulation, transmit diversity, and optional receive diversity in order to provide diversity at the receiver and coding gain without sacrificing the bandwidth. In this paper, we consider the problem of blind estimation of space-time coded signals along with the channel parameters. Both conditional and unconditional maximum likelihood approaches are developed and iterative solutions are proposed. The conditional maximum likelihood algorithm is based on iterative least squares with projection whereas the unconditional maximum likelihood approach is developed by means of finite state Markov process modelling. The performance analysis issues of the proposed methods are studied. Finally, some simulation results are presented.
Energy Technology Data Exchange (ETDEWEB)
Wollaber, Allan Benton [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-06-16
This is a powerpoint presentation which serves as lecture material for the Parallel Computing summer school. It goes over the fundamentals of the Monte Carlo calculation method. The material is presented according to the following outline: Introduction (background, a simple example: estimating π), Why does this even work? (The Law of Large Numbers, The Central Limit Theorem), How to sample (inverse transform sampling, rejection), and An example from particle transport.
Development of Monte Carlo depletion code MCDEP
Energy Technology Data Exchange (ETDEWEB)
Kim, K. S.; Kim, K. Y.; Lee, J. C.; Ji, S. K. [KAERI, Taejon (Korea, Republic of)
2003-07-01
Monte Carlo neutron transport calculation has been used to obtain a reference solution in reactor physics analysis. The typical and widely-used Monte Carlo transport code is MCNP (Monte Carlo N-Particle Transport Code) developed in Los Alamos National Laboratory. The drawbacks of Monte-Carlo transport codes are the lacks of the capacities for the depletion and temperature dependent calculations. In this research we developed MCDEP (Monte Carlo Depletion Code Package) using MCNP with the capacity of the depletion calculation. This code package is the integration of MCNP and depletion module of ORIGEN-2 using the matrix exponential method. This code package enables the automatic MCNP and depletion calculations only with the initial MCNP and MCDEP inputs prepared by users. Depletion chains were simplified for the efficiency of computing time and the treatment of short-lived nuclides without cross section data. The results of MCDEP showed that the reactivity and pin power distributions for the PWR fuel pins and assemblies are consistent with those of CASMO-3 and HELIOS.
2009-01-01
On 7 April CERN will be holding a symposium to mark the 75th birthday of Carlo Rubbia, who shared the 1984 Nobel Prize for Physics with Simon van der Meer for contributions to the discovery of the W and Z bosons, carriers of the weak interaction. Following a presentation by Rolf Heuer, lectures will be given by eminent speakers on areas of science to which Carlo Rubbia has made decisive contributions. Michel Spiro, Director of the French National Institute of Nuclear and Particle Physics (IN2P3) of the CNRS, Lyn Evans, sLHC Project Leader, and Alan Astbury of the TRIUMF Laboratory will talk about the physics of the weak interaction and the discovery of the W and Z bosons. Former CERN Director-General Herwig Schopper will lecture on CERN’s accelerators from LEP to the LHC. Giovanni Bignami, former President of the Italian Space Agency and Professor at the IUSS School for Advanced Studies in Pavia will speak about his work with Carlo Rubbia. Finally, Hans Joachim Sch...
2009-01-01
On 7 April CERN will be holding a symposium to mark the 75th birthday of Carlo Rubbia, who shared the 1984 Nobel Prize for Physics with Simon van der Meer for contributions to the discovery of the W and Z bosons, carriers of the weak interaction. Following a presentation by Rolf Heuer, lectures will be given by eminent speakers on areas of science to which Carlo Rubbia has made decisive contributions. Michel Spiro, Director of the French National Institute of Nuclear and Particle Physics (IN2P3) of the CNRS, Lyn Evans, sLHC Project Leader, and Alan Astbury of the TRIUMF Laboratory will talk about the physics of the weak interaction and the discovery of the W and Z bosons. Former CERN Director-General Herwig Schopper will lecture on CERN’s accelerators from LEP to the LHC. Giovanni Bignami, former President of the Italian Space Agency, will speak about his work with Carlo Rubbia. Finally, Hans Joachim Schellnhuber of the Potsdam Institute for Climate Research and Sven Kul...
Directory of Open Access Journals (Sweden)
Charlie Samuya Veric
2001-12-01
Full Text Available The importance of Carlos Bulosan in Filipino and Filipino-American radical history and literature is indisputable. His eminence spans the pacific, and he is known, diversely, as a radical poet, fictionist, novelist, and labor organizer. Author of the canonical America Iis the Hearts, Bulosan is celebrated for chronicling the conditions in America in his time, such as racism and unemployment. In the history of criticism on Bulosan's life and work, however, there is an undeclared general consensus that views Bulosan and his work as coherent permanent texts of radicalism and anti-imperialism. Central to the existence of such a tradition of critical reception are the generations of critics who, in more ways than one, control the discourse on and of Carlos Bulosan. This essay inquires into the sphere of the critical reception that orders, for our time and for the time ahead, the reading and interpretation of Bulosan. What eye and seeing, the essay asks, determine the perception of Bulosan as the angel of radicalism? What is obscured in constructing Bulosan as an immutable figure of the political? What light does the reader conceive when the personal is brought into the open and situated against the political? the essay explores the answers to these questions in Bulosan's loving letters to various friends, strangers, and white American women. The presence of these interrogations, the essay believes, will secure ultimately the continuing importance of Carlos Bulosan to radical literature and history.
P.C.M. Molenaar; J.R. Nesselroade
1998-01-01
The study of intraindividual variability pervades empirical inquiry in virtually all subdisciplines of psychology. The statistical analysis of multivariate time-series data - a central product of intraindividual investigations - requires special modeling techniques. The dynamic factor model (DFM), w
Yours in Revolution: Retrofitting Carlos the Jackal
Directory of Open Access Journals (Sweden)
Samuel Thomas
2013-09-01
Full Text Available This paper explores the representation of ‘Carlos the Jackal’, the one-time ‘World’s Most Wanted Man’ and ‘International Face of Terror’ – primarily in cin-ema but also encompassing other forms of popular culture and aspects of Cold War policy-making. At the centre of the analysis is Olivier Assayas’s Carlos (2010, a transnational, five and a half hour film (first screened as a TV mini-series about the life and times of the infamous militant. Concentrating on the var-ious ways in which Assayas expresses a critical preoccupation with names and faces through complex formal composition, the project examines the play of ab-straction and embodiment that emerges from the narrativisation of terrorist vio-lence. Lastly, it seeks to engage with the hidden implications of Carlos in terms of the intertwined trajectories of formal experimentation and revolutionary politics.
International Nuclear Information System (INIS)
Analysis of scattering data based on a Monte Carlo integration method was used to obtain a low resolution model of the 4Ca2+.troponin c.troponin I complex. This modeling method allows rapid testing of plausible structures where the best fit model can be ascertained by a comparison between model structure scattering profiles and measured scattering data. In the best fit model, troponin I appears as a spiral structure that wraps about 4CA2+.trophonin C which adopts an extended dumbell conformation similar to that observed in the crystal structures of troponin C. The Monte Carlo modeling method can be applied to other biological systems in which detailed structural information is lacking
Targeted search for continuous gravitational waves: Bayesian versus maximum-likelihood statistics
International Nuclear Information System (INIS)
We investigate the Bayesian framework for detection of continuous gravitational waves (GWs) in the context of targeted searches, where the phase evolution of the GW signal is assumed to be known, while the four amplitude parameters are unknown. We show that the orthodox maximum-likelihood statistic (known as F-statistic) can be rediscovered as a Bayes factor with an unphysical prior in amplitude parameter space. We introduce an alternative detection statistic ('B-statistic') using the Bayes factor with a more natural amplitude prior, namely an isotropic probability distribution for the orientation of GW sources. Monte Carlo simulations of targeted searches show that the resulting Bayesian B-statistic is more powerful in the Neyman-Pearson sense (i.e., has a higher expected detection probability at equal false-alarm probability) than the frequentist F-statistic.
PSMIX: an R package for population structure inference via maximum likelihood method
Directory of Open Access Journals (Sweden)
Zhao Hongyu
2006-06-01
Full Text Available Abstract Background Inference of population stratification and individual admixture from genetic markers is an integrative part of a study in diverse situations, such as association mapping and evolutionary studies. Bayesian methods have been proposed for population stratification and admixture inference using multilocus genotypes and widely used in practice. However, these Bayesian methods demand intensive computation resources and may run into convergence problem in Markov Chain Monte Carlo based posterior samplings. Results We have developed PSMIX, an R package based on maximum likelihood method using expectation-maximization algorithm, for inference of population stratification and individual admixture. Conclusion Compared with software based on Bayesian methods (e.g., STRUCTURE, PSMIX has similar accuracy, but more efficient computations. PSMIX and its supplemental documents are freely available at http://bioinformatics.med.yale.edu/PSMIX.
Directory of Open Access Journals (Sweden)
Jensen Just
2004-01-01
Full Text Available Abstract A Gaussian mixture model with a finite number of components and correlated random effects is described. The ultimate objective is to model somatic cell count information in dairy cattle and to develop criteria for genetic selection against mastitis, an important udder disease. Parameter estimation is by maximum likelihood or by an extension of restricted maximum likelihood. A Monte Carlo expectation-maximization algorithm is used for this purpose. The expectation step is carried out using Gibbs sampling, whereas the maximization step is deterministic. Ranking rules based on the conditional probability of membership in a putative group of uninfected animals, given the somatic cell information, are discussed. Several extensions of the model are suggested.
Knupp, L S; Veloso, C M; Marcondes, M I; Silveira, T S; Silva, A L; Souza, N O; Knupp, S N R; Cannas, A
2016-03-01
The aim of this study was to analyze the economic viability of producing dairy goat kids fed liquid diets in alternative of goat milk and slaughtered at two different ages. Forty-eight male newborn Saanen and Alpine kids were selected and allocated to four groups using a completely randomized factorial design: goat milk (GM), cow milk (CM), commercial milk replacer (CMR) and fermented cow colostrum (FC). Each group was then divided into two groups: slaughter at 60 and 90 days of age. The animals received Tifton hay and concentrate ad libitum. The values of total costs of liquid and solid feed plus labor, income and average gross margin were calculated. The data were then analyzed using the Monte Carlo techniques with the @Risk 5.5 software, with 1000 iterations of the variables being studied through the model. The kids fed GM and CMR generated negative profitability values when slaughtered at 60 days (US$ -16.4 and US$ -2.17, respectively) and also at 90 days (US$ -30.8 and US$ -0.18, respectively). The risk analysis showed that there is a 98% probability that profitability would be negative when GM is used. In this regard, CM and FC presented low risk when the kids were slaughtered at 60 days (8.5% and 21.2%, respectively) and an even lower risk when animals were slaughtered at 90 days (5.2% and 3.8%, respectively). The kids fed CM and slaughtered at 90 days presented the highest average gross income (US$ 67.88) and also average gross margin (US$ 18.43/animal). For the 60-day rearing regime to be economically viable, the CMR cost should not exceed 11.47% of the animal-selling price. This implies that the replacer cannot cost more than US$ 0.39 and 0.43/kg for the 60- and 90-day feeding regimes, respectively. The sensitivity analysis showed that the variables with the greatest impact on the final model's results were animal selling price, liquid diet cost, final weight at slaughter and labor. In conclusion, the production of male dairy goat kids can be economically
MAXIMUM LIKELIHOOD ESTIMATION IN GENERALIZED GAMMA TYPE MODEL
Directory of Open Access Journals (Sweden)
Vinod Kumar
2010-01-01
Full Text Available In the present paper, the maximum likelihood estimates of the two parameters of ageneralized gamma type model have been obtained directly by solving the likelihood equationsas well as by reparametrizing the model first and then solving the likelihood equations (as doneby Prentice, 1974 for fixed values of the third parameter. It is found that reparametrization doesneither reduce the bulk nor the complexity of calculations. as claimed by Prentice (1974. Theprocedure has been illustrated with the help of an example. The distribution of MLE of q alongwith its properties has also been obtained.
Maximum Likelihood Estimation of the Identification Parameters and Its Correction
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
By taking the subsequence out of the input-output sequence of a system polluted by white noise, anindependent observation sequence and its probability density are obtained and then a maximum likelihood estimation of theidentification parameters is given. In order to decrease the asymptotic error, a corrector of maximum likelihood (CML)estimation with its recursive algorithm is given. It has been proved that the corrector has smaller asymptotic error thanthe least square methods. A simulation example shows that the corrector of maximum likelihood estimation is of higherapproximating precision to the true parameters than the least square methods.
Likelihood ratio model for classification of forensic evidence
International Nuclear Information System (INIS)
One of the problems of analysis of forensic evidence such as glass fragments, is the determination of their use-type category, e.g. does a glass fragment originate from an unknown window or container? Very small glass fragments arise during various accidents and criminal offences, and could be carried on the clothes, shoes and hair of participants. It is therefore necessary to obtain information on their physicochemical composition in order to solve the classification problem. Scanning Electron Microscopy coupled with an Energy Dispersive X-ray Spectrometer and the Glass Refractive Index Measurement method are routinely used in many forensic institutes for the investigation of glass. A natural form of glass evidence evaluation for forensic purposes is the likelihood ratio-LR = p(E|H1)/p(E|H2). The main aim of this paper was to study the performance of LR models for glass object classification which considered one or two sources of data variability, i.e. between-glass-object variability and(or) within-glass-object variability. Within the proposed model a multivariate kernel density approach was adopted for modelling the between-object distribution and a multivariate normal distribution was adopted for modelling within-object distributions. Moreover, a graphical method of estimating the dependence structure was employed to reduce the highly multivariate problem to several lower-dimensional problems. The performed analysis showed that the best likelihood model was the one which allows to include information about between and within-object variability, and with variables derived from elemental compositions measured by SEM-EDX, and refractive values determined before (RIb) and after (RIa) the annealing process, in the form of dRI = log10|RIa - RIb|. This model gave better results than the model with only between-object variability considered. In addition, when dRI and variables derived from elemental compositions were used, this model outperformed two other
Empirical Likelihood Ratio Confidence Interval for Positively Associated Series
Institute of Scientific and Technical Information of China (English)
Jun-jian Zhang
2007-01-01
Empirical likelihood is discussed by using the blockwise technique for strongly stationary,positively associated random variables.Our results show that the statistics is asymptotically chi-square distributed and the corresponding confidence interval can be constructed.
Maximum likelihood estimation of finite mixture model for economic data
Phoong, Seuk-Yen; Ismail, Mohd Tahir
2014-06-01
Finite mixture model is a mixture model with finite-dimension. This models are provides a natural representation of heterogeneity in a finite number of latent classes. In addition, finite mixture models also known as latent class models or unsupervised learning models. Recently, maximum likelihood estimation fitted finite mixture models has greatly drawn statistician's attention. The main reason is because maximum likelihood estimation is a powerful statistical method which provides consistent findings as the sample sizes increases to infinity. Thus, the application of maximum likelihood estimation is used to fit finite mixture model in the present paper in order to explore the relationship between nonlinear economic data. In this paper, a two-component normal mixture model is fitted by maximum likelihood estimation in order to investigate the relationship among stock market price and rubber price for sampled countries. Results described that there is a negative effect among rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia.
International Nuclear Information System (INIS)
Novae are astrophysical events (violent explosion) occurring in close binary systems consisting of a white dwarf and a main-sequence star or a star in a more advanced stage of evolution. They are called 'narrow systems' because the two components interact with each other: there is a process of mass exchange with resulting in the transfer of matter from the companion star to the white dwarf, leading to the formation of this last of the so-called accretion disk, rich mainly of hydrogen. Over time, more and more material accumulates until the pressure and the temperature reached are sufficient to trigger nuclear fusion reactions, rapidly converting a large part of the hydrogen into heavier elements. The products of 'hot hydrogen burning' are then placed in the interstellar medium as a result of violent explosions. Studies on the element abundances observed in these events can provide important information about the stages of evolution stellar. During the outbursts of novae some radioactive isotopes are synthesized: in particular, the decay of short-lived nuclei such as 13N and 18F with subsequent emission of gamma radiation energy below 511 keV. The gamma rays from products electron-positron annihilation of positrons emitted in the decay of 18F are the most abundant and the first observable as soon as the atmosphere of the nova starts to become transparent to gamma radiation. Hence the importance of the study of nuclear reactions that lead both to the formation and to the destruction of 18F. Among these, the 18F(p,α)15O reaction is one of the main channels of destruction. This reaction was then studied at energies of astrophysical interest. The experiment done at Riken, Japan, has as its objective the study of the 18F(p,α)15O reaction, using a beam of 18F produced at CRIB, to derive important information about the phenomenon of novae. In this paper we present the experimental technique and the Monte Carlo code developed to be used in the data
Improved maximum likelihood reconstruction of complex multi-generational pedigrees.
Sheehan, Nuala A; Bartlett, Mark; Cussens, James
2014-11-01
The reconstruction of pedigrees from genetic marker data is relevant to a wide range of applications. Likelihood-based approaches aim to find the pedigree structure that gives the highest probability to the observed data. Existing methods either entail an exhaustive search and are hence restricted to small numbers of individuals, or they take a more heuristic approach and deliver a solution that will probably have high likelihood but is not guaranteed to be optimal. By encoding the pedigree learning problem as an integer linear program we can exploit efficient optimisation algorithms to construct pedigrees guaranteed to have maximal likelihood for the standard situation where we have complete marker data at unlinked loci and segregation of genes from parents to offspring is Mendelian. Previous work demonstrated efficient reconstruction of pedigrees of up to about 100 individuals. The modified method that we present here is not so restricted: we demonstrate its applicability with simulated data on a real human pedigree structure of over 1600 individuals. It also compares well with a very competitive approximate approach in terms of solving time and accuracy. In addition to identifying a maximum likelihood pedigree, we can obtain any number of pedigrees in decreasing order of likelihood. This is useful for assessing the uncertainty of a maximum likelihood solution and permits model averaging over high likelihood pedigrees when this would be appropriate. More importantly, when the solution is not unique, as will often be the case for large pedigrees, it enables investigation into the properties of maximum likelihood pedigree estimates which has not been possible up to now. Crucially, we also have a means of assessing the behaviour of other approximate approaches which all aim to find a maximum likelihood solution. Our approach hence allows us to properly address the question of whether a reasonably high likelihood solution that is easy to obtain is practically as
Sieve likelihood ratio inference on general parameter space
Institute of Scientific and Technical Information of China (English)
SHI; Jian; SHEN; Xiaotong
2005-01-01
In this paper,a theory on sieve likelihood ratio inference on general parameter spaces(including infinite dimensional) is studied.Under fairly general regularity conditions,the sieve log-likelihood ratio statistic is proved to be asymptotically x2 distributed,which can be viewed as a generalization of the well-known Wilks' theorem.As an example,a emiparametric partial linear model is investigated.
Gaussian Process Pseudo-Likelihood Models for Sequence Labeling
Srijith, P. K.; Balamurugan, P.; Shevade, Shirish
2014-01-01
Several machine learning problems arising in natural language processing can be modeled as a sequence labeling problem. We provide Gaussian process models based on pseudo-likelihood approximation to perform sequence labeling. Gaussian processes (GPs) provide a Bayesian approach to learning in a kernel based framework. The pseudo-likelihood model enables one to capture long range dependencies among the output components of the sequence without becoming computationally intractable. We use an ef...
Multi-Level Monte Carlo Simulations with Importance Sampling
Przemyslaw S. Stilger and Ser-Huang Poon
2013-01-01
We present an application of importance sampling to multi-asset options under the Heston and the Bates models as well as to the Heston-Hull-White and the Heston-Cox-Ingersoll-Ross models. Moreover, we provide an efficient importance sampling scheme in a Multi-Level Monte Carlo simulation. In all cases, we explain how the Greeks can be computed in the different simulation schemes using the Likelihood Ratio Method, and how combining it with importance sampling leads to a significant variance re...
Optimization of Monte Carlo simulations
Bryskhe, Henrik
2009-01-01
This thesis considers several different techniques for optimizing Monte Carlo simulations. The Monte Carlo system used is Penelope but most of the techniques are applicable to other systems. The two mayor techniques are the usage of the graphics card to do geometry calculations, and raytracing. Using graphics card provides a very efficient way to do fast ray and triangle intersections. Raytracing provides an approximation of Monte Carlo simulation but is much faster to perform. A program was ...
The Likelihood of Experiencing Relative Poverty over the Life Course.
Directory of Open Access Journals (Sweden)
Mark R Rank
Full Text Available Research on poverty in the United States has largely consisted of examining cross-sectional levels of absolute poverty. In this analysis, we focus on understanding relative poverty within a life course context. Specifically, we analyze the likelihood of individuals falling below the 20th percentile and the 10th percentile of the income distribution between the ages of 25 and 60. A series of life tables are constructed using the nationally representative Panel Study of Income Dynamics data set. This includes panel data from 1968 through 2011. Results indicate that the prevalence of relative poverty is quite high. Consequently, between the ages of 25 to 60, 61.8 percent of the population will experience a year below the 20th percentile, and 42.1 percent will experience a year below the 10th percentile. Characteristics associated with experiencing these levels of poverty include those who are younger, nonwhite, female, not married, with 12 years or less of education, or who have a work disability.
Empirical Likelihood-Based ANOVA for Trimmed Means
Velina, Mara; Valeinis, Janis; Greco, Luca; Luta, George
2016-01-01
In this paper, we introduce an alternative to Yuen’s test for the comparison of several population trimmed means. This nonparametric ANOVA type test is based on the empirical likelihood (EL) approach and extends the results for one population trimmed mean from Qin and Tsao (2002). The results of our simulation study indicate that for skewed distributions, with and without variance heterogeneity, Yuen’s test performs better than the new EL ANOVA test for trimmed means with respect to control over the probability of a type I error. This finding is in contrast with our simulation results for the comparison of means, where the EL ANOVA test for means performs better than Welch’s heteroscedastic F test. The analysis of a real data example illustrates the use of Yuen’s test and the new EL ANOVA test for trimmed means for different trimming levels. Based on the results of our study, we recommend the use of Yuen’s test for situations involving the comparison of population trimmed means between groups of interest. PMID:27690063
Maximum likelihood estimation for cytogenetic dose-response curves
International Nuclear Information System (INIS)
In vitro dose-response curves are used to describe the relation between the yield of dicentric chromosome aberrations and radiation dose for human lymphocytes. The dicentric yields follow the Poisson distribution, and the expected yield depends on both the magnitude and the temporal distribution of the dose for low LET radiation. A general dose-response model that describes this relation has been obtained by Kellerer and Rossi using the theory of dual radiation action. The yield of elementary lesions is kappa[γd + g(t, tau)d2], where t is the time and d is dose. The coefficient of the d2 term is determined by the recovery function and the temporal mode of irradiation. Two special cases of practical interest are split-dose and continuous exposure experiments, and the resulting models are intrinsically nonlinear in the parameters. A general purpose maximum likelihood estimation procedure is described and illustrated with numerical examples from both experimental designs. Poisson regression analysis is used for estimation, hypothesis testing, and regression diagnostics. Results are discussed in the context of exposure assessment procedures for both acute and chronic human radiation exposure
Energy Technology Data Exchange (ETDEWEB)
Rodenas, Jose [Departamento de Ingenieria Quimica y Nuclear, Universidad Politecnica de Valencia, Apartado 22012, E-46071 Valencia (Spain)], E-mail: jrodenas@iqn.upv.es; Gallardo, Sergio; Ballester, Silvia; Primault, Virginie [Departamento de Ingenieria Quimica y Nuclear, Universidad Politecnica de Valencia, Apartado 22012, E-46071 Valencia (Spain); Ortiz, Josefina [Laboratorio de Radiactividad Ambiental, Universidad Politecnica de Valencia, Apartado 22012, E-46071 Valencia (Spain)
2007-10-15
A gamma spectrometer including an HP Ge detector is commonly used for environmental radioactivity measurements. The efficiency of the detector should be calibrated for each geometry considered. Simulation of the calibration procedure with a validated computer program is an important auxiliary tool for environmental radioactivity laboratories. The MCNP code based on the Monte Carlo method has been applied to simulate the detection process in order to obtain spectrum peaks and determine the efficiency curve for each modelled geometry. The source used for measurements was a calibration mixed radionuclide gamma reference solution, covering a wide energy range (50-2000 keV). Two measurement geometries - Marinelli beaker and Petri boxes - as well as different materials - water, charcoal, sand - containing the source have been considered. Results obtained from the Monte Carlo model have been compared with experimental measurements in the laboratory in order to validate the model.
Rundel, R. D.; Butler, D. M.; Stolarski, R. S.
1978-01-01
The paper discusses the development of a concise stratospheric model which uses iteration to obtain coupling between interacting species. The one-dimensional, steady-state, diurnally-averaged model generates diffusion equations with appropriate sources and sinks for species odd oxygen, H2O, H2, CO, N2O, odd nitrogen, CH4, CH3Cl, CCl4, CF2Cl2, CFCl3, and odd chlorine. The model evaluates steady-state perturbations caused by injections of chlorine and NO(x) and may be used to predict ozone depletion. The model is used in a Monte Carlo study of the propagation of reaction-rate imprecisions by calculating an ozone perturbation caused by the addition of chlorine. Since the model is sensitive to only 10 of the more than 50 reaction rates considered, only about 1000 Monte Carlo cases are required to span the space of possible results.
Approximate Whittle Analysis of Fractional Cointegration and the Stock Market Synchronization Issue
De Truchis, Gilles
2012-01-01
I consider a bivariate stationary fractional cointegration system and I propose a quasi-maximum likelihood estimator based on the Whittle analysis of the joint spectral density of the regressor and errors to estimate jointly all parameters of interest of the model: the long run coefficient and the long memory parameters of the regressor and errors. I lead a Monte Carlo experiment which reveals the good finite sample properties of this estimator, even when the parameter space is extended to th...
P300 amplitude variations, prior probabilities, and likelihoods: A Bayesian ERP study.
Kopp, Bruno; Seer, Caroline; Lange, Florian; Kluytmans, Anouck; Kolossa, Antonio; Fingscheidt, Tim; Hoijtink, Herbert
2016-10-01
The capability of the human brain for Bayesian inference was assessed by manipulating probabilistic contingencies in an urn-ball task. Event-related potentials (ERPs) were recorded in response to stimuli that differed in their relative frequency of occurrence (.18 to .82). A veraged ERPs with sufficient signal-to-noise ratio (relative frequency of occurrence > .5) were used for further analysis. Research hypotheses about relationships between probabilistic contingencies and ERP amplitude variations were formalized as (in-)equality constrained hypotheses. Conducting Bayesian model comparisons, we found that manipulations of prior probabilities and likelihoods were associated with separately modifiable and distinct ERP responses. P3a amplitudes were sensitive to the degree of prior certainty such that higher prior probabilities were related to larger frontally distributed P3a waves. P3b amplitudes were sensitive to the degree of likelihood certainty such that lower likelihoods were associated with larger parietally distributed P3b waves. These ERP data suggest that these antecedents of Bayesian inference (prior probabilities and likelihoods) are coded by the human brain.
PERBANDINGAN ESTIMASI KEMAMPUAN LATEN ANTARA METODE MAKSIMUM LIKELIHOOD DAN METODE BAYES
Directory of Open Access Journals (Sweden)
Heri Retnawati
2015-10-01
Full Text Available Studi ini bertujuan untuk membandingkan ketepatan estimasi kemampuan laten (latent trait pada model logistik dengan metode maksimum likelihood (ML gabungan dan bayes. Studi ini menggunakan metode simulasi Monte Carlo, dengan model data ujian nasional matematika SMP. Variabel simulasi adalah panjang tes dan banyaknya peserta. Data dibangkitkan dengan menggunakan SAS/IML dengan replikasi 40 kali, dan tiap data diestimasi dengan ML dan Bayes. Hasil estimasi kemudian dibandingkan dengan kemampuan yang sebenarnya, dengan menghitung mean square of error (MSE dan korelasi antara kemampuan laten yang sebenarnya dan hasil estimasi. Metode yang memiliki MSE lebih kecil dikatakan sebagai metode estimasi yang lebih baik. Hasil studi menunjukkan bahwa pada estimasi kemampuan laten dengan 15, 20, 25, dan 30 butir dengan 500 dan 1.000 peserta, hasil MSE belum stabil, namun ketika peserta menjadi 1.500 orang, diperoleh akurasi estimasi kemampuan yang hampir sama baik estimasi antara metode ML dan metode Bayes. Pada estimasi dengan 15 dan 20 butir dan peserta 500, 1.000, dan 1.500, hasil MSE belum stabil, dan ketika estimasi melibatkan 25 dan 30 butir, baik dengan peserta 500, 1.000, maupun 1.500 akan diperoleh hasil yang lebih akurat dengan metode ML. Kata kunci: estimasi kemampuan, metode maksimum likelihood, metode Bayes THE COMPARISON OF ESTIMATION OF LATENT TRAITS USING MAXIMUM LIKELIHOOD AND BAYES METHODS Abstract This study aimed to compare the accuracy of the estimation of latent ability (latent trait in the logistic model using maximum likelihood (ML and Bayes methods. This study uses a quantitative approach that is the Monte Carlo simulation method using students responses to national examination as data model, and variables are the length of the test and the number of participants. The data were generated using SAS/IML with replication 40 times, and each datum is then estimated by ML and Bayes. The estimation results are then compared with the
International Nuclear Information System (INIS)
The course of ''Monte Carlo Techniques'' will try to give a general overview of how to build up a method based on a given theory, allowing you to compare the outcome of an experiment with that theory. Concepts related with the construction of the method, such as, random variables, distributions of random variables, generation of random variables, random-based numerical methods, will be introduced in this course. Examples of some of the current theories in High Energy Physics describing the e+e- annihilation processes (QED, Electro-Weak, QCD) will also be briefly introduced. A second step in the employment of this method is related to the detector. The interactions that a particle could have along its way, through the detector as well as the response of the different materials which compound the detector will be quoted in this course. An example of detector at LEP era, in which these techniques are being applied, will close the course. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Marcus, Ryan C. [Los Alamos National Laboratory
2012-07-25
MCMini is a proof of concept that demonstrates the possibility for Monte Carlo neutron transport using OpenCL with a focus on performance. This implementation, written in C, shows that tracing particles and calculating reactions on a 3D mesh can be done in a highly scalable fashion. These results demonstrate a potential path forward for MCNP or other Monte Carlo codes.
Exclusion probabilities and likelihood ratios with applications to kinship problems.
Slooten, Klaas-Jan; Egeland, Thore
2014-05-01
In forensic genetics, DNA profiles are compared in order to make inferences, paternity cases being a standard example. The statistical evidence can be summarized and reported in several ways. For example, in a paternity case, the likelihood ratio (LR) and the probability of not excluding a random man as father (RMNE) are two common summary statistics. There has been a long debate on the merits of the two statistics, also in the context of DNA mixture interpretation, and no general consensus has been reached. In this paper, we show that the RMNE is a certain weighted average of inverse likelihood ratios. This is true in any forensic context. We show that the likelihood ratio in favor of the correct hypothesis is, in expectation, bigger than the reciprocal of the RMNE probability. However, with the exception of pathological cases, it is also possible to obtain smaller likelihood ratios. We illustrate this result for paternity cases. Moreover, some theoretical properties of the likelihood ratio for a large class of general pairwise kinship cases, including expected value and variance, are derived. The practical implications of the findings are discussed and exemplified.
Monte Carlo methods for electromagnetics
Sadiku, Matthew NO
2009-01-01
Until now, novices had to painstakingly dig through the literature to discover how to use Monte Carlo techniques for solving electromagnetic problems. Written by one of the foremost researchers in the field, Monte Carlo Methods for Electromagnetics provides a solid understanding of these methods and their applications in electromagnetic computation. Including much of his own work, the author brings together essential information from several different publications.Using a simple, clear writing style, the author begins with a historical background and review of electromagnetic theory. After addressing probability and statistics, he introduces the finite difference method as well as the fixed and floating random walk Monte Carlo methods. The text then applies the Exodus method to Laplace's and Poisson's equations and presents Monte Carlo techniques for handing Neumann problems. It also deals with whole field computation using the Markov chain, applies Monte Carlo methods to time-varying diffusion problems, and ...
Predicting missing links and identifying spurious links via likelihood analysis
Pan, Liming; Zhou, Tao; Lü, Linyuan; Hu, Chin-Kun
2016-03-01
Real network data is often incomplete and noisy, where link prediction algorithms and spurious link identification algorithms can be applied. Thus far, it lacks a general method to transform network organizing mechanisms to link prediction algorithms. Here we use an algorithmic framework where a network’s probability is calculated according to a predefined structural Hamiltonian that takes into account the network organizing principles, and a non-observed link is scored by the conditional probability of adding the link to the observed network. Extensive numerical simulations show that the proposed algorithm has remarkably higher accuracy than the state-of-the-art methods in uncovering missing links and identifying spurious links in many complex biological and social networks. Such method also finds applications in exploring the underlying network evolutionary mechanisms.
Hybrid pairwise likelihood analysis of animal behavior experiments
Cattelan, Manuela; Varin, Cristiano
2012-01-01
The study of the determinants of contests between animals is an important issue in understanding animal behavior. Tournament experiments among a set of animals are used by zoologists for this purpose. From a statistical point of view, the results of these tournament experiments are naturally analyzed by paired comparison models such as the Bradley-Terry and the Thurstone models. A major complication is the presence of dependence between the outcomes of couples of contests with an animal in co...
Autocorrelations in hybrid Monte Carlo simulations
International Nuclear Information System (INIS)
Simulations of QCD suffer from severe critical slowing down towards the continuum limit. This problem is known to be prominent in the topological charge, however, all observables are affected to various degree by these slow modes in the Monte Carlo evolution. We investigate the slowing down in high statistics simulations and propose a new error analysis method, which gives a realistic estimate of the contribution of the slow modes to the errors. (orig.)
Simulated Annealing using Hybrid Monte Carlo
Salazar, Rafael; Toral, Raúl
1997-01-01
We propose a variant of the simulated annealing method for optimization in the multivariate analysis of differentiable functions. The method uses global actualizations via the hybrid Monte Carlo algorithm in their generalized version for the proposal of new configurations. We show how this choice can improve upon the performance of simulated annealing methods (mainly when the number of variables is large) by allowing a more effective searching scheme and a faster annealing schedule.
A scalable maximum likelihood method for quantum state tomography
International Nuclear Information System (INIS)
The principle of maximum likelihood reconstruction has proven to yield satisfactory results in the context of quantum state tomography for many-body systems of moderate system sizes. Until recently, however, quantum state tomography has been considered to be infeasible for systems consisting of a large number of subsystems due to the exponential growth of the Hilbert space dimension with the number of constituents. Several reconstruction schemes have been proposed since then to overcome the two main obstacles in quantum many-body tomography: experiment time and post-processing resources. Here we discuss one strategy to address these limitations for the maximum likelihood principle by adopting a particular state representation to merge a well established reconstruction algorithm maximizing the likelihood with techniques known from quantum many-body theory. (paper)
IMPROVING VOICE ACTIVITY DETECTION VIA WEIGHTING LIKELIHOOD AND DIMENSION REDUCTION
Institute of Scientific and Technical Information of China (English)
Wang Huanliang; Han Jiqing; Li Haifeng; Zheng Tieran
2008-01-01
The performance of the traditional Voice Activity Detection (VAD) algorithms declines sharply in lower Signal-to-Noise Ratio (SNR) environments. In this paper, a feature weighting likelihood method is proposed for noise-robust VAD. The contribution of dynamic features to likelihood score can be increased via the method, which improves consequently the noise robustness of VAD.Divergence based dimension reduction method is proposed for saving computation, which reduces these feature dimensions with smaller divergence value at the cost of degrading the performance a little.Experimental results on Aurora Ⅱ database show that the detection performance in noise environments can remarkably be improved by the proposed method when the model trained in clean data is used to detect speech endpoints. Using weighting likelihood on the dimension-reduced features obtains comparable, even better, performance compared to original full-dimensional feature.
Generalized empirical likelihood methods for analyzing longitudinal data
Wang, S.
2010-02-16
Efficient estimation of parameters is a major objective in analyzing longitudinal data. We propose two generalized empirical likelihood based methods that take into consideration within-subject correlations. A nonparametric version of the Wilks theorem for the limiting distributions of the empirical likelihood ratios is derived. It is shown that one of the proposed methods is locally efficient among a class of within-subject variance-covariance matrices. A simulation study is conducted to investigate the finite sample properties of the proposed methods and compare them with the block empirical likelihood method by You et al. (2006) and the normal approximation with a correctly estimated variance-covariance. The results suggest that the proposed methods are generally more efficient than existing methods which ignore the correlation structure, and better in coverage compared to the normal approximation with correctly specified within-subject correlation. An application illustrating our methods and supporting the simulation study results is also presented.
Evolutionary Sequential Monte Carlo Samplers for Change-Point Models
Directory of Open Access Journals (Sweden)
Arnaud Dufays
2016-03-01
Full Text Available Sequential Monte Carlo (SMC methods are widely used for non-linear filtering purposes. However, the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC methods. Not only do SMC algorithms draw posterior distributions of static or dynamic parameters but additionally they provide an estimate of the marginal likelihood. The tempered and time (TNT algorithm, developed in this paper, combines (off-line tempered SMC inference with on-line SMC inference for drawing realizations from many sequential posterior distributions without experiencing a particle degeneracy problem. Furthermore, it introduces a new MCMC rejuvenation step that is generic, automated and well-suited for multi-modal distributions. As this update relies on the wide heuristic optimization literature, numerous extensions are readily available. The algorithm is notably appropriate for estimating change-point models. As an example, we compare several change-point GARCH models through their marginal log-likelihoods over time.
Verallgemeinerte Maximum-Likelihood-Methoden und der selbstinformative Grenzwert
Johannes, Jan
2002-01-01
Es sei X eine Zufallsvariable mit unbekannter Verteilung P. Zu den Hauptaufgaben der Mathematischen Statistik zählt die Konstruktion von Schätzungen für einen abgeleiteten Parameter theta(P) mit Hilfe einer Beobachtung X=x. Im Fall einer dominierten Verteilungsfamilie ist es möglich, das Maximum-Likelihood-Prinzip (MLP) anzuwenden. Eine Alternative dazu liefert der Bayessche Zugang. Insbesondere erweist sich unter Regularitätsbedingungen, dass die Maximum-Likelihood-Schätzung (MLS) dem Gren...
Nearly Efficient Likelihood Ratio Tests of the Unit Root Hypothesis
DEFF Research Database (Denmark)
Jansson, Michael; Nielsen, Morten Ørregaard
Seemingly absent from the arsenal of currently available "nearly efficient" testing procedures for the unit root hypothesis, i.e. tests whose local asymptotic power functions are indistinguishable from the Gaussian power envelope, is a test admitting a (quasi-)likelihood ratio interpretation. We...... show that the likelihood ratio unit root test derived in a Gaussian AR(1) model with standard normal innovations is nearly efficient in that model. Moreover, these desirable properties carry over to more complicated models allowing for serially correlated and/or non-Gaussian innovations....
Australian food life style segments and elaboration likelihood differences
DEFF Research Database (Denmark)
Brunsø, Karen; Reid, Mike
As the global food marketing environment becomes more competitive, the international and comparative perspective of consumers' attitudes and behaviours becomes more important for both practitioners and academics. This research employs the Food-Related Life Style (FRL) instrument in Australia...... in order to 1) determine Australian Life Style Segments and compare these with their European counterparts, and to 2) explore differences in elaboration likelihood among the Australian segments, e.g. consumers' interest and motivation to perceive product related communication. The results provide new...... insights into cross-cultural similarities and differences, into elaboration likelihood differences among consumer segments, and show how the involvement construct may be used as basis for communication development....
Empirical likelihood-based evaluations of Value at Risk models
Institute of Scientific and Technical Information of China (English)
WEI ZhengHong; WEN SongQiao; ZHU LiXing
2009-01-01
Value at Risk (VaR) is a basic and very useful tool in measuring market risks. Numerous VaR models have been proposed in literature. Therefore, it is of great interest to evaluate the efficiency of these models, and to select the most appropriate one. In this paper, we shall propose to use the empirical likelihood approach to evaluate these models. Simulation results and real life examples show that the empirical likelihood method is more powerful and more robust than some of the asymptotic method available in literature.
Parallelizing Monte Carlo with PMC
Energy Technology Data Exchange (ETDEWEB)
Rathkopf, J.A.; Jones, T.R.; Nessett, D.M.; Stanberry, L.C.
1994-11-01
PMC (Parallel Monte Carlo) is a system of generic interface routines that allows easy porting of Monte Carlo packages of large-scale physics simulation codes to Massively Parallel Processor (MPP) computers. By loading various versions of PMC, simulation code developers can configure their codes to run in several modes: serial, Monte Carlo runs on the same processor as the rest of the code; parallel, Monte Carlo runs in parallel across many processors of the MPP with the rest of the code running on other MPP processor(s); distributed, Monte Carlo runs in parallel across many processors of the MPP with the rest of the code running on a different machine. This multi-mode approach allows maintenance of a single simulation code source regardless of the target machine. PMC handles passing of messages between nodes on the MPP, passing of messages between a different machine and the MPP, distributing work between nodes, and providing independent, reproducible sequences of random numbers. Several production codes have been parallelized under the PMC system. Excellent parallel efficiency in both the distributed and parallel modes results if sufficient workload is available per processor. Experiences with a Monte Carlo photonics demonstration code and a Monte Carlo neutronics package are described.
Institute of Scientific and Technical Information of China (English)
范学明; 陶辛未
2011-01-01
Monte-Carlo Method is the representative statistical method in stochastic analysis. This method has no restriction on the variation of random variables and the dimension of problems. And its solutions often be regarded as relative accurate solutions. Thus this method has been paid widely attention. Sampling is the base of Monte-Carlo method. The major sampling method are the traditional sampling method and the Latin-Hypercube sampling method in the current. The basic principle and sampling effectiveness for two sampling methods have been introduced. On this basis, an attempt algorithm on improve sampling effectiveness has been put forward. This algorithm is simple and effective, and finally the calculation efficiency of Monte-Carlo method be improved. The algorithm's effectiveness is approved by theoretically derived and numerical examples.%Monte-Carlo法是随机分析中统计型方法的典型方法,由于该法具有对随机量的变异性没有限制、对问题的维数不敏感以及其解答为相对精确解等优点得到广泛的关注.而样本生成是Monte-Carlo法随机分析的基础.目前Monte-Carlo法的主要抽样方法有传统抽样法和Latin-Hypercube抽样法.介绍了以上两种抽样方法的基本原理并分析了方法生成样本的有效性,并在此基础上做了进一步提高样本有效性的尝试.该做法简单高效,从而最终提高Monte-Carlo法随机分析的计算效率.通过理论分析和数值算例验证了该做法的有效性.
Adluru, Nagesh; Hanlon, Bret M; Lutz, Antoine; Lainhart, Janet E; Alexander, Andrew L; Davidson, Richard J
2013-04-01
Neuroimage phenotyping for psychiatric and neurological disorders is performed using voxelwise analyses also known as voxel based analyses or morphometry (VBM). A typical voxelwise analysis treats measurements at each voxel (e.g., fractional anisotropy, gray matter probability) as outcome measures to study the effects of possible explanatory variables (e.g., age, group) in a linear regression setting. Furthermore, each voxel is treated independently until the stage of correction for multiple comparisons. Recently, multi-voxel pattern analyses (MVPA), such as classification, have arisen as an alternative to VBM. The main advantage of MVPA over VBM is that the former employ multivariate methods which can account for interactions among voxels in identifying significant patterns. They also provide ways for computer-aided diagnosis and prognosis at individual subject level. However, compared to VBM, the results of MVPA are often more difficult to interpret and prone to arbitrary conclusions. In this paper, first we use penalized likelihood modeling to provide a unified framework for understanding both VBM and MVPA. We then utilize statistical learning theory to provide practical methods for interpreting the results of MVPA beyond commonly used performance metrics, such as leave-one-out-cross validation accuracy and area under the receiver operating characteristic (ROC) curve. Additionally, we demonstrate that there are challenges in MVPA when trying to obtain image phenotyping information in the form of statistical parametric maps (SPMs), which are commonly obtained from VBM, and provide a bootstrap strategy as a potential solution for generating SPMs using MVPA. This technique also allows us to maximize the use of available training data. We illustrate the empirical performance of the proposed framework using two different neuroimaging studies that pose different levels of challenge for classification using MVPA. PMID:23397550
Likelihood-based inference for clustered line transect data
DEFF Research Database (Denmark)
Waagepetersen, Rasmus Plenge; Schweder, Tore
The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...
LIKELIHOOD ESTIMATION OF PARAMETERS USING SIMULTANEOUSLY MONITORED PROCESSES
DEFF Research Database (Denmark)
Friis-Hansen, Peter; Ditlevsen, Ove Dalager
2004-01-01
The topic is maximum likelihood inference from several simultaneously monitored response processes of a structure to obtain knowledge about the parameters of other not monitored but important response processes when the structure is subject to some Gaussian load field in space and time...
Planck 2013 results. XV. CMB power spectra and likelihood
Ade, P.A.R.; Armitage-Caplan, C.; Arnaud, M.; Ashdown, M.; Atrio-Barandela, F.; Aumont, J.; Baccigalupi, C.; Banday, A.J.; Barreiro, R.B.; Bartlett, J.G.; Battaner, E.; Benabed, K.; Benoit, A.; Benoit-Levy, A.; Bernard, J.P.; Bersanelli, M.; Bielewicz, P.; Bobin, J.; Bock, J.J.; Bonaldi, A.; Bonavera, L.; Bond, J.R.; Borrill, J.; Bouchet, F.R.; Boulanger, F.; Bridges, M.; Bucher, M.; Burigana, C.; Butler, R.C.; Calabrese, E.; Cardoso, J.F.; Catalano, A.; Challinor, A.; Chamballu, A.; Chiang, L.Y.; Chiang, H.C.; Christensen, P.R.; Church, S.; Clements, D.L.; Colombi, S.; Colombo, L.P.L.; Combet, C.; Couchot, F.; Coulais, A.; Crill, B.P.; Curto, A.; Cuttaia, F.; Danese, L.; Davies, R.D.; Davis, R.J.; de Bernardis, P.; de Rosa, A.; de Zotti, G.; Delabrouille, J.; Delouis, J.M.; Desert, F.X.; Dickinson, C.; Diego, J.M.; Dole, H.; Donzelli, S.; Dore, O.; Douspis, M.; Dunkley, J.; Dupac, X.; Efstathiou, G.; Elsner, F.; Ensslin, T.A.; Eriksen, H.K.; Finelli, F.; Forni, O.; Frailis, M.; Fraisse, A.A.; Franceschi, E.; Gaier, T.C.; Galeotta, S.; Galli, S.; Ganga, K.; Giard, M.; Giardino, G.; Giraud-Heraud, Y.; Gjerlow, E.; Gonzalez-Nuevo, J.; Gorski, K.M.; Gratton, S.; Gregorio, A.; Gruppuso, A.; Gudmundsson, J.E.; Hansen, F.K.; Hanson, D.; Harrison, D.; Helou, G.; Henrot-Versille, S.; Hernandez-Monteagudo, C.; Herranz, D.; Hildebrandt, S.R.; Hivon, E.; Hobson, M.; Holmes, W.A.; Hornstrup, A.; Hovest, W.; Huffenberger, K.M.; Hurier, G.; Jaffe, T.R.; Jaffe, A.H.; Jewell, J.; Jones, W.C.; Juvela, M.; Keihanen, E.; Keskitalo, R.; Kiiveri, K.; Kisner, T.S.; Kneissl, R.; Knoche, J.; Knox, L.; Kunz, M.; Kurki-Suonio, H.; Lagache, G.; Lahteenmaki, A.; Lamarre, J.M.; Lasenby, A.; Lattanzi, M.; Laureijs, R.J.; Lawrence, C.R.; Le Jeune, M.; Leach, S.; Leahy, J.P.; Leonardi, R.; Leon-Tavares, J.; Lesgourgues, J.; Liguori, M.; Lilje, P.B.; Lindholm, V.; Linden-Vornle, M.; Lopez-Caniego, M.; Lubin, P.M.; Macias-Perez, J.F.; Maffei, B.; Maino, D.; Mandolesi, N.; Marinucci, D.; Maris, M.; Marshall, D.J.; Martin, P.G.; Martinez-Gonzalez, E.; Masi, S.; Matarrese, S.; Matthai, F.; Mazzotta, P.; Meinhold, P.R.; Melchiorri, A.; Mendes, L.; Menegoni, E.; Mennella, A.; Migliaccio, M.; Millea, M.; Mitra, S.; Miville-Deschenes, M.A.; Molinari, D.; Moneti, A.; Montier, L.; Morgante, G.; Mortlock, D.; Moss, A.; Munshi, D.; Naselsky, P.; Nati, F.; Natoli, P.; Netterfield, C.B.; Norgaard-Nielsen, H.U.; Noviello, F.; Novikov, D.; Novikov, I.; O'Dwyer, I.J.; Orieux, F.; Osborne, S.; Oxborrow, C.A.; Paci, F.; Pagano, L.; Pajot, F.; Paladini, R.; Paoletti, D.; Partridge, B.; Pasian, F.; Patanchon, G.; Paykari, P.; Perdereau, O.; Perotto, L.; Perrotta, F.; Piacentini, F.; Piat, M.; Pierpaoli, E.; Pietrobon, D.; Plaszczynski, S.; Pointecouteau, E.; Polenta, G.; Ponthieu, N.; Popa, L.; Poutanen, T.; Pratt, G.W.; Prezeau, G.; Prunet, S.; Puget, J.L.; Rachen, J.P.; Rahlin, A.; Rebolo, R.; Reinecke, M.; Remazeilles, M.; Renault, C.; Ricciardi, S.; Riller, T.; Ringeval, C.; Ristorcelli, I.; Rocha, G.; Rosset, C.; Roudier, G.; Rowan-Robinson, M.; Rubino-Martin, J.A.; Rusholme, B.; Sandri, M.; Sanselme, L.; Santos, D.; Savini, G.; Scott, D.; Seiffert, M.D.; Shellard, E.P.S.; Spencer, L.D.; Starck, J.L.; Stolyarov, V.; Stompor, R.; Sudiwala, R.; Sureau, F.; Sutton, D.; Suur-Uski, A.S.; Sygnet, J.F.; Tauber, J.A.; Tavagnacco, D.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Tucci, M.; Tuovinen, J.; Turler, M.; Valenziano, L.; Valiviita, J.; Van Tent, B.; Varis, J.; Vielva, P.; Villa, F.; Vittorio, N.; Wade, L.A.; Wandelt, B.D.; Wehus, I.K.; White, M.; White, S.D.M.; Yvon, D.; Zacchei, A.; Zonca, A.
2014-01-01
We present the Planck likelihood, a complete statistical description of the two-point correlation function of the CMB temperature fluctuations. We use this likelihood to derive the Planck CMB power spectrum over three decades in l, covering 2 = 50, we employ a correlated Gaussian likelihood approximation based on angular cross-spectra derived from the 100, 143 and 217 GHz channels. We validate our likelihood through an extensive suite of consistency tests, and assess the impact of residual foreground and instrumental uncertainties on cosmological parameters. We find good internal agreement among the high-l cross-spectra with residuals of a few uK^2 at l <= 1000. We compare our results with foreground-cleaned CMB maps, and with cross-spectra derived from the 70 GHz Planck map, and find broad agreement in terms of spectrum residuals and cosmological parameters. The best-fit LCDM cosmology is in excellent agreement with preliminary Planck polarisation spectra. The standard LCDM cosmology is well constrained b...
Profile likelihood maps of a 15-dimensional MSSM
C. Strege; G. Bertone; G.J. Besjes; S. Caron; R. Ruiz de Austri; A. Strubig; R. Trotta
2014-01-01
We present statistically convergent profile likelihood maps obtained via global fits of a phenomenological Minimal Supersymmetric Standard Model with 15 free parameters (the MSSM-15), based on over 250M points. We derive constraints on the model parameters from direct detection limits on dark matter
Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model
DEFF Research Database (Denmark)
Johansen, Søren; Nielsen, Morten Ørregaard
We consider model based inference in a fractionally cointegrated (or cofractional) vector autoregressive model based on the conditional Gaussian likelihood. The model allows the process X(t) to be fractional of order d and cofractional of order d-b; that is, there exist vectors ß for which ß...
Bias Correction for Alternating Iterative Maximum Likelihood Estimators
Institute of Scientific and Technical Information of China (English)
Gang YU; Wei GAO; Ningzhong SHI
2013-01-01
In this paper,we give a definition of the alternating iterative maximum likelihood estimator (AIMLE) which is a biased estimator.Furthermore we adjust the AIMLE to result in asymptotically unbiased and consistent estimators by using a bootstrap iterative bias correction method as in Kuk (1995).Two examples and simulation results reported illustrate the performance of the bias correction for AIMLE.
A KULLBACK—LEIBLER EMPIRICAL LIKELIHOOD INFERENCE FOR CENSORED DATA
Institute of Scientific and Technical Information of China (English)
SHIJian; Tai－ShingLan
2002-01-01
In this paper,two kinds of Kullback-Leibler criteria with appropriate constraints are proposed to construct empirical likelihood confidence intervals for the mean of right censored data.It is shown that one of the criteria is equivalent to Adimari's(1997) procedure,and the other shares the same asymptotic behavior.
How to Maximize the Likelihood Function for a DSGE Model
DEFF Research Database (Denmark)
Andreasen, Martin Møller
). Following these extensions, we examine the ability of the two routines to maximize the likelihood function for a sequence of test economies. Our results show that the CMA- ES routine clearly outperforms Simulated Annealing in its ability to find the global optimum and in efficiency. With 10 unknown...
Likelihood inference for a fractionally cointegrated vector autoregressive model
DEFF Research Database (Denmark)
Johansen, Søren; Nielsen, Morten Ørregaard
We consider model based inference in a fractionally cointegrated (or cofractional) vector autoregressive model based on the conditional Gaussian likelihood. The model allows the process X_{t} to be fractional of order d and cofractional of order d-b; that is, there exist vectors β for which β...
Likelihood inference for a nonstationary fractional autoregressive model
DEFF Research Database (Denmark)
Johansen, Søren; Nielsen, Morten Ørregaard
This paper discusses model based inference in an autoregressive model for fractional processes based on the Gaussian likelihood. The model allows for the process to be fractional of order d or d-b; where d ≥ b > 1/2 are parameters to be estimated. We model the data X1,...,XT given the initial...