Pemodelan Markov Switching Dengan Time-varying Transition Probability
Savitri, Anggita Puri; Warsito, Budi; Rahmawati, Rita
2016-01-01
Exchange rate or currency is an economic variable which reflects country's state of economy. It fluctuates over time because of its ability to switch the condition or regime caused by economic and political factors. The changes in the exchange rate are depreciation and appreciation. Therefore, it could be modeled using Markov Switching with Time-Varying Transition Probability which observe the conditional changes and use information variable. From this model, time-varying transition probabili...
Maximum Entropy Estimation of Transition Probabilities of Reversible Markov Chains
Directory of Open Access Journals (Sweden)
Erik Van der Straeten
2009-11-01
Full Text Available In this paper, we develop a general theory for the estimation of the transition probabilities of reversible Markov chains using the maximum entropy principle. A broad range of physical models can be studied within this approach. We use one-dimensional classical spin systems to illustrate the theoretical ideas. The examples studied in this paper are: the Ising model, the Potts model and the Blume-Emery-Griffiths model.
Saliency Detection via Absorbing Markov Chain With Learnt Transition Probability.
Lihe Zhang; Jianwu Ai; Bowen Jiang; Huchuan Lu; Xiukui Li
2018-02-01
In this paper, we propose a bottom-up saliency model based on absorbing Markov chain (AMC). First, a sparsely connected graph is constructed to capture the local context information of each node. All image boundary nodes and other nodes are, respectively, treated as the absorbing nodes and transient nodes in the absorbing Markov chain. Then, the expected number of times from each transient node to all other transient nodes can be used to represent the saliency value of this node. The absorbed time depends on the weights on the path and their spatial coordinates, which are completely encoded in the transition probability matrix. Considering the importance of this matrix, we adopt different hierarchies of deep features extracted from fully convolutional networks and learn a transition probability matrix, which is called learnt transition probability matrix. Although the performance is significantly promoted, salient objects are not uniformly highlighted very well. To solve this problem, an angular embedding technique is investigated to refine the saliency results. Based on pairwise local orderings, which are produced by the saliency maps of AMC and boundary maps, we rearrange the global orderings (saliency value) of all nodes. Extensive experiments demonstrate that the proposed algorithm outperforms the state-of-the-art methods on six publicly available benchmark data sets.
Recursive recovery of Markov transition probabilities from boundary value data
Energy Technology Data Exchange (ETDEWEB)
Patch, Sarah Kathyrn [Univ. of California, Berkeley, CA (United States)
1994-04-01
In an effort to mathematically describe the anisotropic diffusion of infrared radiation in biological tissue Gruenbaum posed an anisotropic diffusion boundary value problem in 1989. In order to accommodate anisotropy, he discretized the temporal as well as the spatial domain. The probabilistic interpretation of the diffusion equation is retained; radiation is assumed to travel according to a random walk (of sorts). In this random walk the probabilities with which photons change direction depend upon their previous as well as present location. The forward problem gives boundary value data as a function of the Markov transition probabilities. The inverse problem requires finding the transition probabilities from boundary value data. Problems in the plane are studied carefully in this thesis. Consistency conditions amongst the data are derived. These conditions have two effects: they prohibit inversion of the forward map but permit smoothing of noisy data. Next, a recursive algorithm which yields a family of solutions to the inverse problem is detailed. This algorithm takes advantage of all independent data and generates a system of highly nonlinear algebraic equations. Pluecker-Grassmann relations are instrumental in simplifying the equations. The algorithm is used to solve the 4 x 4 problem. Finally, the smallest nontrivial problem in three dimensions, the 2 x 2 x 2 problem, is solved.
Estimation and asymptotic theory for transition probabilities in Markov Renewal Multi–state models
Spitoni, C.; Verduijn, M.; Putter, H.
2012-01-01
In this paper we discuss estimation of transition probabilities for semi–Markov multi–state models. Non–parametric and semi–parametric estimators of the transition probabilities for a large class of models (forward going models) are proposed. Large sample theory is derived using the functional
Directory of Open Access Journals (Sweden)
Shi Zhiyan
2009-01-01
Full Text Available We study some limit properties of the harmonic mean of random transition probability for a second-order nonhomogeneous Markov chain and a nonhomogeneous Markov chain indexed by a tree. As corollary, we obtain the property of the harmonic mean of random transition probability for a nonhomogeneous Markov chain.
Transition probabilities of health states for workers in Malaysia using a Markov chain model
Samsuddin, Shamshimah; Ismail, Noriszura
2017-04-01
The aim of our study is to estimate the transition probabilities of health states for workers in Malaysia who contribute to the Employment Injury Scheme under the Social Security Organization Malaysia using the Markov chain model. Our study uses four states of health (active, temporary disability, permanent disability and death) based on the data collected from the longitudinal studies of workers in Malaysia for 5 years. The transition probabilities vary by health state, age and gender. The results show that men employees are more likely to have higher transition probabilities to any health state compared to women employees. The transition probabilities can be used to predict the future health of workers in terms of a function of current age, gender and health state.
International Nuclear Information System (INIS)
Gao Zhong-Ke; Hu Li-Dan; Jin Ning-De
2013-01-01
We generate a directed weighted complex network by a method based on Markov transition probability to represent an experimental two-phase flow. We first systematically carry out gas—liquid two-phase flow experiments for measuring the time series of flow signals. Then we construct directed weighted complex networks from various time series in terms of a network generation method based on Markov transition probability. We find that the generated network inherits the main features of the time series in the network structure. In particular, the networks from time series with different dynamics exhibit distinct topological properties. Finally, we construct two-phase flow directed weighted networks from experimental signals and associate the dynamic behavior of gas-liquid two-phase flow with the topological statistics of the generated networks. The results suggest that the topological statistics of two-phase flow networks allow quantitative characterization of the dynamic flow behavior in the transitions among different gas—liquid flow patterns. (general)
de Uña-Álvarez, Jacobo; Meira-Machado, Luís
2015-06-01
Multi-state models are often used for modeling complex event history data. In these models the estimation of the transition probabilities is of particular interest, since they allow for long-term predictions of the process. These quantities have been traditionally estimated by the Aalen-Johansen estimator, which is consistent if the process is Markov. Several non-Markov estimators have been proposed in the recent literature, and their superiority with respect to the Aalen-Johansen estimator has been proved in situations in which the Markov condition is strongly violated. However, the existing estimators have the drawback of requiring that the support of the censoring distribution contains the support of the lifetime distribution, which is not often the case. In this article, we propose two new methods for estimating the transition probabilities in the progressive illness-death model. Some asymptotic results are derived. The proposed estimators are consistent regardless the Markov condition and the referred assumption about the censoring support. We explore the finite sample behavior of the estimators through simulations. The main conclusion of this piece of research is that the proposed estimators are much more efficient than the existing non-Markov estimators in most cases. An application to a clinical trial on colon cancer is included. Extensions to progressive processes beyond the three-state illness-death model are discussed. © 2015, The International Biometric Society.
International Nuclear Information System (INIS)
Olivieri, E.; Scoppola, E.
1996-01-01
In this paper we consider aperiodic ergodic Markov chains with transition probabilities exponentially small in a large parameter β. We extend to the general, not necessarily reversible case the analysis, started in part I of this work, of the first exit problem from a general domain Q containing many stable equilibria (attracting equilibrium points for the β = ∞ dynamics). In particular we describe the tube of typical trajectories during the first excursion outside Q
Zhang, Yuanhui; Wu, Haipeng; Denton, Brian T; Wilson, James R; Lobo, Jennifer M
2017-10-27
Markov models are commonly used for decision-making studies in many application domains; however, there are no widely adopted methods for performing sensitivity analysis on such models with uncertain transition probability matrices (TPMs). This article describes two simulation-based approaches for conducting probabilistic sensitivity analysis on a given discrete-time, finite-horizon, finite-state Markov model using TPMs that are sampled over a specified uncertainty set according to a relevant probability distribution. The first approach assumes no prior knowledge of the probability distribution, and each row of a TPM is independently sampled from the uniform distribution on the row's uncertainty set. The second approach involves random sampling from the (truncated) multivariate normal distribution of the TPM's maximum likelihood estimators for its rows subject to the condition that each row has nonnegative elements and sums to one. The two sampling methods are easily implemented and have reasonable computation times. A case study illustrates the application of these methods to a medical decision-making problem involving the evaluation of treatment guidelines for glycemic control of patients with type 2 diabetes, where natural variation in a patient's glycated hemoglobin (HbA1c) is modeled as a Markov chain, and the associated TPMs are subject to uncertainty.
Analysis and design of Markov jump systems with complex transition probabilities
Zhang, Lixian; Shi, Peng; Zhu, Yanzheng
2016-01-01
The book addresses the control issues such as stability analysis, control synthesis and filter design of Markov jump systems with the above three types of TPs, and thus is mainly divided into three parts. Part I studies the Markov jump systems with partially unknown TPs. Different methodologies with different conservatism for the basic stability and stabilization problems are developed and compared. Then the problems of state estimation, the control of systems with time-varying delays, the case involved with both partially unknown TPs and uncertain TPs in a composite way are also tackled. Part II deals with the Markov jump systems with piecewise homogeneous TPs. Methodologies that can effectively handle control problems in the scenario are developed, including the one coping with the asynchronous switching phenomenon between the currently activated system mode and the controller/filter to be designed. Part III focuses on the Markov jump systems with memory TPs. The concept of σ-mean square stability is propo...
Directory of Open Access Journals (Sweden)
Olariu E
2017-09-01
Full Text Available Elena Olariu,1 Kevin K Cadwell,1 Elizabeth Hancock,1 David Trueman,1 Helene Chevrou-Severac2 1PHMR Ltd, London, UK; 2Takeda Pharmaceuticals International AG, Zurich, Switzerland Objective: Although Markov cohort models represent one of the most common forms of decision-analytic models used in health care decision-making, correct implementation of such models requires reliable estimation of transition probabilities. This study sought to identify consensus statements or guidelines that detail how such transition probability matrices should be estimated. Methods: A literature review was performed to identify relevant publications in the following databases: Medline, Embase, the Cochrane Library, and PubMed. Electronic searches were supplemented by manual-searches of health technology assessment (HTA websites in Australia, Belgium, Canada, France, Germany, Ireland, Norway, Portugal, Sweden, and the UK. One reviewer assessed studies for eligibility. Results: Of the 1,931 citations identified in the electronic searches, no studies met the inclusion criteria for full-text review, and no guidelines on transition probabilities in Markov models were identified. Manual-searching of the websites of HTA agencies identified ten guidelines on economic evaluations (Australia, Belgium, Canada, France, Germany, Ireland, Norway, Portugal, Sweden, and UK. All identified guidelines provided general guidance on how to develop economic models, but none provided guidance on the calculation of transition probabilities. One relevant publication was identified following review of the reference lists of HTA agency guidelines: the International Society for Pharmacoeconomics and Outcomes Research taskforce guidance. This provided limited guidance on the use of rates and probabilities. Conclusions: There is limited formal guidance available on the estimation of transition probabilities for use in decision-analytic models. Given the increasing importance of cost
Li, Yushuang; Song, Tian; Yang, Jiasheng; Zhang, Yi; Yang, Jialiang
2016-01-01
In this paper, we have proposed a novel alignment-free method for comparing the similarity of protein sequences. We first encode a protein sequence into a 440 dimensional feature vector consisting of a 400 dimensional Pseudo-Markov transition probability vector among the 20 amino acids, a 20 dimensional content ratio vector, and a 20 dimensional position ratio vector of the amino acids in the sequence. By evaluating the Euclidean distances among the representing vectors, we compare the similarity of protein sequences. We then apply this method into the ND5 dataset consisting of the ND5 protein sequences of 9 species, and the F10 and G11 datasets representing two of the xylanases containing glycoside hydrolase families, i.e., families 10 and 11. As a result, our method achieves a correlation coefficient of 0.962 with the canonical protein sequence aligner ClustalW in the ND5 dataset, much higher than those of other 5 popular alignment-free methods. In addition, we successfully separate the xylanases sequences in the F10 family and the G11 family and illustrate that the F10 family is more heat stable than the G11 family, consistent with a few previous studies. Moreover, we prove mathematically an identity equation involving the Pseudo-Markov transition probability vector and the amino acids content ratio vector.
Probability distributions for Markov chain based quantum walks
Balu, Radhakrishnan; Liu, Chaobin; Venegas-Andraca, Salvador E.
2018-01-01
We analyze the probability distributions of the quantum walks induced from Markov chains by Szegedy (2004). The first part of this paper is devoted to the quantum walks induced from finite state Markov chains. It is shown that the probability distribution on the states of the underlying Markov chain is always convergent in the Cesaro sense. In particular, we deduce that the limiting distribution is uniform if the transition matrix is symmetric. In the case of a non-symmetric Markov chain, we exemplify that the limiting distribution of the quantum walk is not necessarily identical with the stationary distribution of the underlying irreducible Markov chain. The Szegedy scheme can be extended to infinite state Markov chains (random walks). In the second part, we formulate the quantum walk induced from a lazy random walk on the line. We then obtain the weak limit of the quantum walk. It is noted that the current quantum walk appears to spread faster than its counterpart-quantum walk on the line driven by the Grover coin discussed in literature. The paper closes with an outlook on possible future directions.
The transition probabilities of the reciprocity model
Snijders, T.A.B.
1999-01-01
The reciprocity model is a continuous-time Markov chain model used for modeling longitudinal network data. A new explicit expression is derived for its transition probability matrix. This expression can be checked relatively easily. Some properties of the transition probabilities are given, as well
First hitting probabilities for semi markov chains and estimation
DEFF Research Database (Denmark)
Georgiadis, Stylianos
2017-01-01
We first consider a stochastic system described by an absorbing semi-Markov chain with finite state space and we introduce the absorption probability to a class of recurrent states. Afterwards, we study the first hitting probability to a subset of states for an irreducible semi-Markov chain...
Transition probabilities for atoms
International Nuclear Information System (INIS)
Kim, Y.K.
1980-01-01
Current status of advanced theoretical methods for transition probabilities for atoms and ions is discussed. An experiment on the f values of the resonance transitions of the Kr and Xe isoelectronic sequences is suggested as a test for the theoretical methods
Markov chains with quasitoeplitz transition matrix: first zero hitting
Directory of Open Access Journals (Sweden)
Alexander M. Dukhovny
1989-01-01
Full Text Available This paper continues the investigation of Markov Chains with a quasitoeplitz transition matrix. Generating functions of first zero hitting probabilities and mean times are found by the solution of special Riemann boundary value problems on the unit circle. Duality is discussed.
Transition Effect Matrices and Quantum Markov Chains
Gudder, Stan
2009-06-01
A transition effect matrix (TEM) is a quantum generalization of a classical stochastic matrix. By employing a TEM we obtain a quantum generalization of a classical Markov chain. We first discuss state and operator dynamics for a quantum Markov chain. We then consider various types of TEMs and vector states. In particular, we study invariant, equilibrium and singular vector states and investigate projective, bistochastic, invertible and unitary TEMs.
Markov chains with quasitoeplitz transition matrix
Directory of Open Access Journals (Sweden)
Alexander M. Dukhovny
1989-01-01
Full Text Available This paper investigates a class of Markov chains which are frequently encountered in various applications (e.g. queueing systems, dams and inventories with feedback. Generating functions of transient and steady state probabilities are found by solving a special Riemann boundary value problem on the unit circle. A criterion of ergodicity is established.
Invariant probabilities of transition functions
Zaharopol, Radu
2014-01-01
The structure of the set of all the invariant probabilities and the structure of various types of individual invariant probabilities of a transition function are two topics of significant interest in the theory of transition functions, and are studied in this book. The results obtained are useful in ergodic theory and the theory of dynamical systems, which, in turn, can be applied in various other areas (like number theory). They are illustrated using transition functions defined by flows, semiflows, and one-parameter convolution semigroups of probability measures. In this book, all results on transition probabilities that have been published by the author between 2004 and 2008 are extended to transition functions. The proofs of the results obtained are new. For transition functions that satisfy very general conditions the book describes an ergodic decomposition that provides relevant information on the structure of the corresponding set of invariant probabilities. Ergodic decomposition means a splitting of t...
Markov transitions and the propagation of chaos
International Nuclear Information System (INIS)
Gottlieb, A.
1998-01-01
The propagation of chaos is a central concept of kinetic theory that serves to relate the equations of Boltzmann and Vlasov to the dynamics of many-particle systems. Propagation of chaos means that molecular chaos, i.e., the stochastic independence of two random particles in a many-particle system, persists in time, as the number of particles tends to infinity. We establish a necessary and sufficient condition for a family of general n-particle Markov processes to propagate chaos. This condition is expressed in terms of the Markov transition functions associated to the n-particle processes, and it amounts to saying that chaos of random initial states propagates if it propagates for pure initial states. Our proof of this result relies on the weak convergence approach to the study of chaos due to Sztitman and Tanaka. We assume that the space in which the particles live is homomorphic to a complete and separable metric space so that we may invoke Prohorov's theorem in our proof. We also show that, if the particles can be in only finitely many states, then molecular chaos implies that the specific entropies in the n-particle distributions converge to the entropy of the limiting single-particle distribution
Compositionality for Markov reward chains with fast and silent transitions
Markovski, J.; Sokolova, A.; Trcka, N.; Vink, de E.P.
2009-01-01
A parallel composition is defined for Markov reward chains with stochastic discontinuity, and with fast and silent transitions. In this setting, compositionality with respect to the relevant aggregation preorders is established. For Markov reward chains with fast transitions the preorders are
Convergence of Transition Probability Matrix in CLVMarkov Models
Permana, D.; Pasaribu, U. S.; Indratno, S. W.; Suprayogi, S.
2018-04-01
A transition probability matrix is an arrangement of transition probability from one states to another in a Markov chain model (MCM). One of interesting study on the MCM is its behavior for a long time in the future. The behavior is derived from one property of transition probabilty matrix for n steps. This term is called the convergence of the n-step transition matrix for n move to infinity. Mathematically, the convergence of the transition probability matrix is finding the limit of the transition matrix which is powered by n where n moves to infinity. The convergence form of the transition probability matrix is very interesting as it will bring the matrix to its stationary form. This form is useful for predicting the probability of transitions between states in the future. The method usually used to find the convergence of transition probability matrix is through the process of limiting the distribution. In this paper, the convergence of the transition probability matrix is searched using a simple concept of linear algebra that is by diagonalizing the matrix.This method has a higher level of complexity because it has to perform the process of diagonalization in its matrix. But this way has the advantage of obtaining a common form of power n of the transition probability matrix. This form is useful to see transition matrix before stationary. For example cases are taken from CLV model using MCM called Model of CLV-Markov. There are several models taken by its transition probability matrix to find its convergence form. The result is that the convergence of the matrix of transition probability through diagonalization has similarity with convergence with commonly used distribution of probability limiting method.
Estimating Model Probabilities using Thermodynamic Markov Chain Monte Carlo Methods
Ye, M.; Liu, P.; Beerli, P.; Lu, D.; Hill, M. C.
2014-12-01
Markov chain Monte Carlo (MCMC) methods are widely used to evaluate model probability for quantifying model uncertainty. In a general procedure, MCMC simulations are first conducted for each individual model, and MCMC parameter samples are then used to approximate marginal likelihood of the model by calculating the geometric mean of the joint likelihood of the model and its parameters. It has been found the method of evaluating geometric mean suffers from the numerical problem of low convergence rate. A simple test case shows that even millions of MCMC samples are insufficient to yield accurate estimation of the marginal likelihood. To resolve this problem, a thermodynamic method is used to have multiple MCMC runs with different values of a heating coefficient between zero and one. When the heating coefficient is zero, the MCMC run is equivalent to a random walk MC in the prior parameter space; when the heating coefficient is one, the MCMC run is the conventional one. For a simple case with analytical form of the marginal likelihood, the thermodynamic method yields more accurate estimate than the method of using geometric mean. This is also demonstrated for a case of groundwater modeling with consideration of four alternative models postulated based on different conceptualization of a confining layer. This groundwater example shows that model probabilities estimated using the thermodynamic method are more reasonable than those obtained using the geometric method. The thermodynamic method is general, and can be used for a wide range of environmental problem for model uncertainty quantification.
K-forbidden transition probabilities
International Nuclear Information System (INIS)
Saitoh, T.R.; Sletten, G.; Bark, R.A.; Hagemann, G.B.; Herskind, B.; Saitoh-Hashimoto, N.; Tsukuba Univ., Ibaraki
2000-01-01
Reduced hindrance factors of K-forbidden transitions are compiled for nuclei with A∝180 where γ-vibrational states are observed. Correlations between these reduced hindrance factors and Coriolis forces, statistical level mixing and γ-softness have been studied. It is demonstrated that the K-forbidden transition probabilities are related to γ-softness. The decay of the high-K bandheads has been studied by means of the two-state mixing, which would be induced by the γ-softness, with the use of a number of K-forbidden transitions compiled in the present work, where high-K bandheads are depopulated by both E2 and ΔI=1 transitions. The validity of the two-state mixing scheme has been examined by using the proposed identity of the B(M1)/B(E2) ratios of transitions depopulating high-K bandheads and levels of low-K bands. A break down of the identity might indicate that other levels would mediate transitions between high- and low-K states. (orig.)
Markov Chain Monte Carlo Methods
Indian Academy of Sciences (India)
Keywords. Markov chain; state space; stationary transition probability; stationary distribution; irreducibility; aperiodicity; stationarity; M-H algorithm; proposal distribution; acceptance probability; image processing; Gibbs sampler.
Kirkwood, James R
2015-01-01
Review of ProbabilityShort HistoryReview of Basic Probability DefinitionsSome Common Probability DistributionsProperties of a Probability DistributionProperties of the Expected ValueExpected Value of a Random Variable with Common DistributionsGenerating FunctionsMoment Generating FunctionsExercisesDiscrete-Time, Finite-State Markov ChainsIntroductionNotationTransition MatricesDirected Graphs: Examples of Markov ChainsRandom Walk with Reflecting BoundariesGamblerâ€™s RuinEhrenfest ModelCentral Problem of Markov ChainsCondition to Ensure a Unique Equilibrium StateFinding the Equilibrium StateTransient and Recurrent StatesIndicator FunctionsPerron-Frobenius TheoremAbsorbing Markov ChainsMean First Passage TimeMean Recurrence Time and the Equilibrium StateFundamental Matrix for Regular Markov ChainsDividing a Markov Chain into Equivalence ClassesPeriodic Markov ChainsReducible Markov ChainsSummaryExercisesDiscrete-Time, Infinite-State Markov ChainsRenewal ProcessesDelayed Renewal ProcessesEquilibrium State f...
Transition probability spaces in loop quantum gravity
Guo, Xiao-Kan
2018-03-01
We study the (generalized) transition probability spaces, in the sense of Mielnik and Cantoni, for spacetime quantum states in loop quantum gravity. First, we show that loop quantum gravity admits the structures of transition probability spaces. This is exemplified by first checking such structures in covariant quantum mechanics and then identifying the transition probability spaces in spin foam models via a simplified version of general boundary formulation. The transition probability space thus defined gives a simple way to reconstruct the discrete analog of the Hilbert space of the canonical theory and the relevant quantum logical structures. Second, we show that the transition probability space and in particular the spin foam model are 2-categories. Then we discuss how to realize in spin foam models two proposals by Crane about the mathematical structures of quantum gravity, namely, the quantum topos and causal sites. We conclude that transition probability spaces provide us with an alternative framework to understand various foundational questions of loop quantum gravity.
Decomposition of conditional probability for high-order symbolic Markov chains
Melnik, S. S.; Usatenko, O. V.
2017-07-01
The main goal of this paper is to develop an estimate for the conditional probability function of random stationary ergodic symbolic sequences with elements belonging to a finite alphabet. We elaborate on a decomposition procedure for the conditional probability function of sequences considered to be high-order Markov chains. We represent the conditional probability function as the sum of multilinear memory function monomials of different orders (from zero up to the chain order). This allows us to introduce a family of Markov chain models and to construct artificial sequences via a method of successive iterations, taking into account at each step increasingly high correlations among random elements. At weak correlations, the memory functions are uniquely expressed in terms of the high-order symbolic correlation functions. The proposed method fills the gap between two approaches, namely the likelihood estimation and the additive Markov chains. The obtained results may have applications for sequential approximation of artificial neural network training.
Directory of Open Access Journals (Sweden)
Pablo J. Villacorta
2016-07-01
Full Text Available Markov chains are well-established probabilistic models of a wide variety of real systems that evolve along time. Countless examples of applications of Markov chains that successfully capture the probabilistic nature of real problems include areas as diverse as biology, medicine, social science, and engineering. One interesting feature which characterizes certain kinds of Markov chains is their stationary distribution, which stands for the global fraction of time the system spends in each state. The computation of the stationary distribution requires precise knowledge of the transition probabilities. When the only information available is a sequence of observations drawn from the system, such probabilities have to be estimated. Here we review an existing method to estimate fuzzy transition probabilities from observations and, with them, obtain the fuzzy stationary distribution of the resulting fuzzy Markov chain. The method also works when the user directly provides fuzzy transition probabilities. We provide an implementation in the R environment that is the first available to the community and serves as a proof of concept. We demonstrate the usefulness of our proposal with computational experiments on a toy problem, namely a time-homogeneous Markov chain that guides the randomized movement of an autonomous robot that patrols a small area.
Camera-Model Identification Using Markovian Transition Probability Matrix
Xu, Guanshuo; Gao, Shang; Shi, Yun Qing; Hu, Ruimin; Su, Wei
Detecting the (brands and) models of digital cameras from given digital images has become a popular research topic in the field of digital forensics. As most of images are JPEG compressed before they are output from cameras, we propose to use an effective image statistical model to characterize the difference JPEG 2-D arrays of Y and Cb components from the JPEG images taken by various camera models. Specifically, the transition probability matrices derived from four different directional Markov processes applied to the image difference JPEG 2-D arrays are used to identify statistical difference caused by image formation pipelines inside different camera models. All elements of the transition probability matrices, after a thresholding technique, are directly used as features for classification purpose. Multi-class support vector machines (SVM) are used as the classification tool. The effectiveness of our proposed statistical model is demonstrated by large-scale experimental results.
Evolution of an array of elements with logistic transition probability
International Nuclear Information System (INIS)
Majernik, Vladimir; Surda, Anton
1996-01-01
The paper addresses the problem how the state of an array of elements changes if the transition probabilities of its elements is chosen in the form of a logistic map. This problem leads to a special type of a discrete-time Markov which we simulated numerically for the different transition probabilities and the number of elements in the array. We show that the time evolution of the array exhibits a wide scale of behavior depending on the value of the total number of its elements and on the logistic constant a. We point out that this problem can be applied for description of a spin system with a certain type of mean field and of the multispecies ecosystems with an internal noise. (authors)
Evolution of probability measures by cellular automata on algebraic topological Markov chains
Directory of Open Access Journals (Sweden)
ALEJANDRO MAASS
2003-01-01
Full Text Available In this paper we review some recent results on the evolution of probability measures under cellular automata acting on a fullshift. In particular we discuss the crucial role of the attractiveness of maximal measures. We enlarge the context of the results of a previous study of topological Markov chains that are Abelian groups; the shift map is an automorphism of this group. This is carried out by studying the dynamics of Markov measures by a particular additive cellular automata. Many of these topics were within the focus of Francisco Varela's mathematical interests.
A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector
Directory of Open Access Journals (Sweden)
H. Othman
2007-02-01
Full Text Available We introduce an efficient hidden Markov model-based voice activity detection (VAD algorithm with time-variant state-transition probabilities in the underlying Markov chain. The transition probabilities vary in an exponential charge/discharge scheme and are softly merged with state conditional likelihood into a final VAD decision. Working in the domain of ITU-T G.729 parameters, with no additional cost for feature extraction, the proposed algorithm significantly outperforms G.729 Annex B VAD while providing a balanced tradeoff between clipping and false detection errors. The performance compares very favorably with the adaptive multirate VAD, option 2 (AMR2.
A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector
Directory of Open Access Journals (Sweden)
Othman H
2007-01-01
Full Text Available We introduce an efficient hidden Markov model-based voice activity detection (VAD algorithm with time-variant state-transition probabilities in the underlying Markov chain. The transition probabilities vary in an exponential charge/discharge scheme and are softly merged with state conditional likelihood into a final VAD decision. Working in the domain of ITU-T G.729 parameters, with no additional cost for feature extraction, the proposed algorithm significantly outperforms G.729 Annex B VAD while providing a balanced tradeoff between clipping and false detection errors. The performance compares very favorably with the adaptive multirate VAD, option 2 (AMR2.
Yamada, Yuhei; Yamazaki, Yoshihiro
2018-04-01
This study considered a stochastic model for cluster growth in a Markov process with a cluster size dependent additive noise. According to this model, the probability distribution of the cluster size transiently becomes an exponential or a log-normal distribution depending on the initial condition of the growth. In this letter, a master equation is obtained for this model, and derivation of the distributions is discussed.
DEFF Research Database (Denmark)
Azarang, Leyla; Scheike, Thomas; de Uña-Álvarez, Jacobo
2017-01-01
In this work, we present direct regression analysis for the transition probabilities in the possibly non-Markov progressive illness–death model. The method is based on binomial regression, where the response is the indicator of the occupancy for the given state along time. Randomly weighted score...
Nonlinear Markov processes: Deterministic case
International Nuclear Information System (INIS)
Frank, T.D.
2008-01-01
Deterministic Markov processes that exhibit nonlinear transition mechanisms for probability densities are studied. In this context, the following issues are addressed: Markov property, conditional probability densities, propagation of probability densities, multistability in terms of multiple stationary distributions, stability analysis of stationary distributions, and basin of attraction of stationary distribution
A nonstationary Markov transition model for computing the relative risk of dementia before death
Yu, Lei; Griffith, William S.; Tyas, Suzanne L.; Snowdon, David A.; Kryscio, Richard J.
2010-01-01
This paper investigates the long-term behavior of the k-step transition probability matrix for a nonstationary discrete time Markov chain in the context of modeling transitions from intact cognition to dementia with mild cognitive impairment (MCI) and global impairment (GI) as intervening cognitive states. The authors derive formulas for the following absorption statistics: (1) the relative risk of absorption between competing absorbing states, and (2) the mean and variance of the number of visits among the transient states before absorption. Since absorption is not guaranteed, sufficient conditions are discussed to ensure that the substochastic matrix associated with transitions among transient states converges to zero in limit. Results are illustrated with an application to the Nun Study, a cohort of 678 participants, 75 to 107 years of age, followed longitudinally with up to ten cognitive assessments over a fifteen-year period. PMID:20087848
Atomic Transition Probabilities Scandium through Manganese
International Nuclear Information System (INIS)
Martin, G.A.; Fuhr, J.R.; Wiese, W.L.
1988-01-01
Atomic transition probabilities for about 8,800 spectral lines of five iron-group elements, Sc(Z = 21) to Mn(Z = 25), are critically compiled, based on all available literature sources. The data are presented in separate tables for each element and stage of ionization and are further subdivided into allowed (i.e., electric dipole-E1) and forbidden (magnetic dipole-M1, electric quadrupole-E2, and magnetic quadrupole-M2) transitions. Within each data table the spectral lines are grouped into multiplets, which are in turn arranged according to parent configurations, transition arrays, and ascending quantum numbers. For each line the transition probability for spontaneous emission and the line strength are given, along with the spectroscopic designation, the wavelength, the statistical weights, and the energy levels of the upper and lower states. For allowed lines the absorption oscillator strength is listed, while for forbidden transitions the type of transition is identified (M1, E2, etc.). In addition, the estimated accuracy and the source are indicated. In short introductions, which precede the tables for each ion, the main justifications for the choice of the adopted data and for the accuracy rating are discussed. A general introduction contains a discussion of our method of evaluation and the principal criteria for our judgements
System Geometries and Transit/Eclipse Probabilities
Directory of Open Access Journals (Sweden)
Howard A.
2011-02-01
Full Text Available Transiting exoplanets provide access to data to study the mass-radius relation and internal structure of extrasolar planets. Long-period transiting planets allow insight into planetary environments similar to the Solar System where, in contrast to hot Jupiters, planets are not constantly exposed to the intense radiation of their parent stars. Observations of secondary eclipses additionally permit studies of exoplanet temperatures and large-scale exo-atmospheric properties. We show how transit and eclipse probabilities are related to planet-star system geometries, particularly for long-period, eccentric orbits. The resulting target selection and observational strategies represent the principal ingredients of our photometric survey of known radial-velocity planets with the aim of detecting transit signatures (TERMS.
First Passage Probability Estimation of Wind Turbines by Markov Chain Monte Carlo
DEFF Research Database (Denmark)
Sichani, Mahdi Teimouri; Nielsen, Søren R.K.
2013-01-01
Markov Chain Monte Carlo simulation has received considerable attention within the past decade as reportedly one of the most powerful techniques for the first passage probability estimation of dynamic systems. A very popular method in this direction capable of estimating probability of rare events...... of the method by modifying the conditional sampler. In this paper, applicability of the original SS is compared to the recently introduced modifications of the method on a wind turbine model. The model incorporates a PID pitch controller which aims at keeping the rotational speed of the wind turbine rotor equal...... to its nominal value. Finally Monte Carlo simulations are performed which allow assessment of the accuracy of the first passage probability estimation by the SS methods....
Brémaud, Pierre
2017-01-01
The emphasis in this book is placed on general models (Markov chains, random fields, random graphs), universal methods (the probabilistic method, the coupling method, the Stein-Chen method, martingale methods, the method of types) and versatile tools (Chernoff's bound, Hoeffding's inequality, Holley's inequality) whose domain of application extends far beyond the present text. Although the examples treated in the book relate to the possible applications, in the communication and computing sciences, in operations research and in physics, this book is in the first instance concerned with theory. The level of the book is that of a beginning graduate course. It is self-contained, the prerequisites consisting merely of basic calculus (series) and basic linear algebra (matrices). The reader is not assumed to be trained in probability since the first chapters give in considerable detail the background necessary to understand the rest of the book. .
a Probability Model for Drought Prediction Using Fusion of Markov Chain and SAX Methods
Jouybari-Moghaddam, Y.; Saradjian, M. R.; Forati, A. M.
2017-09-01
Drought is one of the most powerful natural disasters which are affected on different aspects of the environment. Most of the time this phenomenon is immense in the arid and semi-arid area. Monitoring and prediction the severity of the drought can be useful in the management of the natural disaster caused by drought. Many indices were used in predicting droughts such as SPI, VCI, and TVX. In this paper, based on three data sets (rainfall, NDVI, and land surface temperature) which are acquired from MODIS satellite imagery, time series of SPI, VCI, and TVX in time limited between winters 2000 to summer 2015 for the east region of Isfahan province were created. Using these indices and fusion of symbolic aggregation approximation and hidden Markov chain drought was predicted for fall 2015. For this purpose, at first, each time series was transformed into the set of quality data based on the state of drought (5 group) by using SAX algorithm then the probability matrix for the future state was created by using Markov hidden chain. The fall drought severity was predicted by fusion the probability matrix and state of drought severity in summer 2015. The prediction based on the likelihood for each state of drought includes severe drought, middle drought, normal drought, severe wet and middle wet. The analysis and experimental result from proposed algorithm show that the product of this algorithm is acceptable and the proposed algorithm is appropriate and efficient for predicting drought using remote sensor data.
Transition Probabilities in {sup 189}Os
Energy Technology Data Exchange (ETDEWEB)
Malmskog, S G; Berg, V; Baecklin, A
1970-02-15
The level structure of {sup 189}Os has been studied from the decay of {sup 189}Ir (13,3 days) produced in proton spallation at CERN and mass separated in the ISOLDE on-line facility. The gamma-ray spectrum has been recorded both with a high resolution Si(Li) - detector and Ge(Li) - detectors. Three previously not reported transitions were observed defining a new level at 348.5 keV. Special attention was given to the low energy level band structure. Several multipolarity mixing ratios were deduced from measured L-subshell ratios which, together with measured level half-lives, gave absolute transition probabilities. The low level decay properties are discussed in terms of the Nilsson model with the inclusion of Coriolis coupling.
Transition Dipole Moments and Transition Probabilities of the CN Radical
Yin, Yuan; Shi, Deheng; Sun, Jinfeng; Zhu, Zunlue
2018-04-01
This paper studies the transition probabilities of electric dipole transitions between 10 low-lying states of the CN radical. These states are X2Σ+, A2Π, B2Σ+, a4Σ+, b4Π, 14Σ‑, 24Π, 14Δ, 16Σ+, and 16Π. The potential energy curves are calculated using the CASSCF method, which is followed by the icMRCI approach with the Davidson correction. The transition dipole moments between different states are calculated. To improve the accuracy of potential energy curves, core–valence correlation and scalar relativistic corrections, as well as the extrapolation of potential energies to the complete basis set limit are included. The Franck–Condon factors and Einstein coefficients of emissions are calculated. The radiative lifetimes are determined for the vibrational levels of the A2Π, B2Σ+, b4Π, 14Σ‑, 24Π, 14Δ, and 16Π states. According to the transition probabilities and radiative lifetimes, some guidelines for detecting these states spectroscopically are proposed. The spin–orbit coupling effect on the spectroscopic and vibrational properties is evaluated. The splitting energy in the A2Π state is determined to be 50.99 cm‑1, which compares well with the experimental ones. The potential energy curves, transition dipole moments, spectroscopic parameters, and transition probabilities reported in this paper can be considered to be very reliable. The results obtained here can be used as guidelines for detecting these transitions, in particular those that have not been measured in previous experiments or have not been observed in the Sun, comets, stellar atmospheres, dark interstellar clouds, and diffuse interstellar clouds.
International Nuclear Information System (INIS)
Esik, Olga; Tusnady, Gabor; Daubner, Kornel; Nemeth, Gyoergy; Fuezy, Marton; Szentirmay, Zoltan
1997-01-01
Purpose: The typically benign, but occasionally rapidly fatal clinical course of papillary thyroid cancer has raised the need for individual survival probability estimation, to tailor the treatment strategy exclusively to a given patient. Materials and methods: A retrospective study was performed on 400 papillary thyroid cancer patients with a median follow-up time of 7.1 years to establish a clinical database for uni- and multivariate analysis of the prognostic factors related to survival (Kaplan-Meier product limit method and Cox regression). For a more precise prognosis estimation, the effect of the most important clinical events were then investigated on the basis of a Markov renewal model. The basic concept of this approach is that each patient has an individual disease course which (besides the initial clinical categories) is affected by special events, e.g. internal covariates (local/regional/distant relapses). On the supposition that these events and the cause-specific death are influenced by the same biological processes, the parameters of transient survival probability characterizing the speed of the course of the disease for each clinical event and their sequence were determined. The individual survival curves for each patient were calculated by using these parameters and the independent significant clinical variables selected from multivariate studies, summation of which resulted in a mean cause-specific survival function valid for the entire group. On the basis of this Markov model, prediction of the cause-specific survival probability is possible for extrastudy cases, if it is supposed that the clinical events occur within new patients in the same manner and with the similar probability as within the study population. Results: The patient's age, a distant metastasis at presentation, the extent of the surgical intervention, the primary tumor size and extent (pT), the external irradiation dosage and the degree of TSH suppression proved to be
Consistency and refinement for Interval Markov Chains
DEFF Research Database (Denmark)
Delahaye, Benoit; Larsen, Kim Guldstrand; Legay, Axel
2012-01-01
Interval Markov Chains (IMC), or Markov Chains with probability intervals in the transition matrix, are the base of a classic specification theory for probabilistic systems [18]. The standard semantics of IMCs assigns to a specification the set of all Markov Chains that satisfy its interval...
Time-Varying Transition Probability Matrix Estimation and Its Application to Brand Share Analysis.
Chiba, Tomoaki; Hino, Hideitsu; Akaho, Shotaro; Murata, Noboru
2017-01-01
In a product market or stock market, different products or stocks compete for the same consumers or purchasers. We propose a method to estimate the time-varying transition matrix of the product share using a multivariate time series of the product share. The method is based on the assumption that each of the observed time series of shares is a stationary distribution of the underlying Markov processes characterized by transition probability matrices. We estimate transition probability matrices for every observation under natural assumptions. We demonstrate, on a real-world dataset of the share of automobiles, that the proposed method can find intrinsic transition of shares. The resulting transition matrices reveal interesting phenomena, for example, the change in flows between TOYOTA group and GM group for the fiscal year where TOYOTA group's sales beat GM's sales, which is a reasonable scenario.
Time-Varying Transition Probability Matrix Estimation and Its Application to Brand Share Analysis.
Directory of Open Access Journals (Sweden)
Tomoaki Chiba
Full Text Available In a product market or stock market, different products or stocks compete for the same consumers or purchasers. We propose a method to estimate the time-varying transition matrix of the product share using a multivariate time series of the product share. The method is based on the assumption that each of the observed time series of shares is a stationary distribution of the underlying Markov processes characterized by transition probability matrices. We estimate transition probability matrices for every observation under natural assumptions. We demonstrate, on a real-world dataset of the share of automobiles, that the proposed method can find intrinsic transition of shares. The resulting transition matrices reveal interesting phenomena, for example, the change in flows between TOYOTA group and GM group for the fiscal year where TOYOTA group's sales beat GM's sales, which is a reasonable scenario.
Chen, Pei; Liu, Rui; Li, Yongjun; Chen, Luonan
2016-07-15
Identifying the critical state or pre-transition state just before the occurrence of a phase transition is a challenging task, because the state of the system may show little apparent change before this critical transition during the gradual parameter variations. Such dynamics of phase transition is generally composed of three stages, i.e. before-transition state, pre-transition state and after-transition state, which can be considered as three different Markov processes. By exploring the rich dynamical information provided by high-throughput data, we present a novel computational method, i.e. hidden Markov model (HMM) based approach, to detect the switching point of the two Markov processes from the before-transition state (a stationary Markov process) to the pre-transition state (a time-varying Markov process), thereby identifying the pre-transition state or early-warning signals of the phase transition. To validate the effectiveness, we apply this method to detect the signals of the imminent phase transitions of complex systems based on the simulated datasets, and further identify the pre-transition states as well as their critical modules for three real datasets, i.e. the acute lung injury triggered by phosgene inhalation, MCF-7 human breast cancer caused by heregulin and HCV-induced dysplasia and hepatocellular carcinoma. Both functional and pathway enrichment analyses validate the computational results. The source code and some supporting files are available at https://github.com/rabbitpei/HMM_based-method lnchen@sibs.ac.cn or liyj@scut.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Exact asymptotics of probabilities of large deviations for Markov chains: the Laplace method
Energy Technology Data Exchange (ETDEWEB)
Fatalov, Vadim R [M. V. Lomonosov Moscow State University, Faculty of Mechanics and Mathematics, Moscow (Russian Federation)
2011-08-31
We prove results on exact asymptotics as n{yields}{infinity} for the expectations E{sub a} exp{l_brace}-{theta}{Sigma}{sub k=0}{sup n-1}g(X{sub k}){r_brace} and probabilities P{sub a}{l_brace}(1/n {Sigma}{sub k=0}{sup n-1}g(X{sub k})
Markov chain Monte Carlo linkage analysis: effect of bin width on the probability of linkage.
Slager, S L; Juo, S H; Durner, M; Hodge, S E
2001-01-01
We analyzed part of the Genetic Analysis Workshop (GAW) 12 simulated data using Monte Carlo Markov chain (MCMC) methods that are implemented in the computer program Loki. The MCMC method reports the "probability of linkage" (PL) across the chromosomal regions of interest. The point of maximum PL can then be taken as a "location estimate" for the location of the quantitative trait locus (QTL). However, Loki does not provide a formal statistical test of linkage. In this paper, we explore how the bin width used in the calculations affects the max PL and the location estimate. We analyzed age at onset (AO) and quantitative trait number 5, Q5, from 26 replicates of the general simulated data in one region where we knew a major gene, MG5, is located. For each trait, we found the max PL and the corresponding location estimate, using four different bin widths. We found that bin width, as expected, does affect the max PL and the location estimate, and we recommend that users of Loki explore how their results vary with different bin widths.
Partially Hidden Markov Models
DEFF Research Database (Denmark)
Forchhammer, Søren Otto; Rissanen, Jorma
1996-01-01
Partially Hidden Markov Models (PHMM) are introduced. They differ from the ordinary HMM's in that both the transition probabilities of the hidden states and the output probabilities are conditioned on past observations. As an illustration they are applied to black and white image compression where...
Investigation of nuclear structures using transition probabilities
International Nuclear Information System (INIS)
Dewald, A.; Moeller, O.; Peusquens, R.
2002-01-01
Magnetic rotation which appears as regular M1 bands in the spectra, is a well established phenomenon in several Pb isotopes. In the A = 130 region where similar M1 bands are known, e.g. in 124 Xe and 128 Ba, it is still not clear whether it does exists. Crucial experimental observables are the B (M1) values which -are expected to decrease with in creasing spin. At Strasbourg a recoil distance measurement (RDM) with the EUROBALL spectrometer at Strasbourg and the Koeln plunger using the reaction 110 Pd( 18 O, 4n) 124 Xe at a beam energy of 86 MeV yielded preliminary lifetimes of ground band states and states of the M1 band. The deduced B(M1) values show the expected behaviour for magnetic rotation. It is also shown that the experimental B(M1) values can be described as well on the basis of a rotational band. The measured B(E2) values are used to investigate the nuclear deformation of 124 Xe as well as the interaction of the ground state band with two s-bands. Spherical deformed shape coexistence is investigated by means of electromagnetic transition probabilities in the case of 188 Pb. Lifetimes were measured in 188 Pb using a novel combination of the Koeln plunger device with the GSFMA set-up at ATLAS. The reaction 40 Ca ( 152 Sm, 4n) 188 Pb at a beam energy of 725 MeV in inverse kinematics is used. It is found that the lowest 2 + state is predominantly of prolate structure
Rare gases transition probabilities for plasma diagnostics
International Nuclear Information System (INIS)
Katsonis, K.; Siskos, A.; Ndiaye, A.; Clark, R.E.H.; Cornille, M.; Abdallah, J. Jr
2005-01-01
Emission spectroscopy is a powerful optical diagnostics tool which has been largely used in studying and monitoring various industrial, laboratory and natural plasmas. As these plasmas are rarely in Local Thermodynamic Equilibrium (LTE) a prerequisite of satisfactory evaluation of the plasma electron density n e and temperature T e is the existence of a detailed Collisional-Radiative (C-R) model taking into account the main physical processes influencing the plasma state and dynamics of its main constituents. The theoretical spectra which such a model generates match the experimental ones whenever the experimental values of ne and T e are introduced. In practice, in validating such models, discrepancies are observed which often are due to the atomic data included in the C-R model. In generating theoretical spectra pertaining to each atom(ion) multiplet, the most sensible atomic data are the relevant transition probabilities A j→i and electron collision excitation cross sections σ i→j . We note that the latter are actually poorly known, especially for low ionization stages and near the excitation threshold. We address here the evaluation of the former, especially of the A j→i of the Ar 2+ ion responsible for the Ar III spectra and of those of the Xe 2+ ion which are evaluated in an analogous way. Extensive studies of the Ar III and Xe III spectra exist, but the present status of Aj i cannot be considered sufficient for the generation of the theoretical spectra even of the most prominent visible lines coming from the Ar III multiplets 4s - 4p, 5p (corresponding to the well known '' red '' and 'blue' lines of Ar I) 4p - 4d, 5d and 3p - 4s, 5s (resonant) and the analogous Xe III multiplets (which have principal quantum numbers increased by two). Due to the gap observed in the Grotrian diagrams, the resonant lines which, together with the important metastable ones, belong to the 3p - 4s, 5s multiplets, (5p - 6s, 7s for Xe III), give spectra in the UV region. On
Absolute transition probabilities for 559 strong lines of neutral cerium
Energy Technology Data Exchange (ETDEWEB)
Curry, J J, E-mail: jjcurry@nist.go [National Institute of Standards and Technology, Gaithersburg, MD 20899-8422 (United States)
2009-07-07
Absolute radiative transition probabilities are reported for 559 strong lines of neutral cerium covering the wavelength range 340-880 nm. These transition probabilities are obtained by scaling published relative line intensities (Meggers et al 1975 Tables of Spectral Line Intensities (National Bureau of Standards Monograph 145)) with a smaller set of published absolute transition probabilities (Bisson et al 1991 J. Opt. Soc. Am. B 8 1545). All 559 new values are for lines for which transition probabilities have not previously been available. The estimated relative random uncertainty of the new data is +-35% for nearly all lines.
Trcka, N.; Andova, S.; McIver, A.; D'Argenio, P.; Cuijpers, P.J.L.; Markovski, J.; Morgan, C.; Núñez, M.
2009-01-01
We first study labeled transition systems with explicit successful termination. We establish the notions of strong, weak, and branching bisimulation in terms of boolean matrix theory, introducing thus a novel and powerful algebraic apparatus. Next we consider Markov reward chains which are
A note on the transition probability over Csup(*)-algebras
International Nuclear Information System (INIS)
Alberti, P.M.; Karl-Marx-Universitaet, Leipzig
1983-01-01
The algebraic structure of Uhlmann's transition probability between mixed states on unital Csup(*)-algebras is analyzed. Several improvements of methods to calculate the transition probability are fixed, examples are given (e.g., the case of quasi-local Csup(*)-algebras is dealt with) and two more functional characterizations are proved in general. (orig.)
Shiryaev, A N
1996-01-01
This book contains a systematic treatment of probability from the ground up, starting with intuitive ideas and gradually developing more sophisticated subjects, such as random walks, martingales, Markov chains, ergodic theory, weak convergence of probability measures, stationary stochastic processes, and the Kalman-Bucy filter Many examples are discussed in detail, and there are a large number of exercises The book is accessible to advanced undergraduates and can be used as a text for self-study This new edition contains substantial revisions and updated references The reader will find a deeper study of topics such as the distance between probability measures, metrization of weak convergence, and contiguity of probability measures Proofs for a number of some important results which were merely stated in the first edition have been added The author included new material on the probability of large deviations, and on the central limit theorem for sums of dependent random variables
Directory of Open Access Journals (Sweden)
Nikola Trčka
2009-12-01
Full Text Available We first study labeled transition systems with explicit successful termination. We establish the notions of strong, weak, and branching bisimulation in terms of boolean matrix theory, introducing thus a novel and powerful algebraic apparatus. Next we consider Markov reward chains which are standardly presented in real matrix theory. By interpreting the obtained matrix conditions for bisimulations in this setting, we automatically obtain the definitions of strong, weak, and branching bisimulation for Markov reward chains. The obtained strong and weak bisimulations are shown to coincide with some existing notions, while the obtained branching bisimulation is new, but its usefulness is questionable.
Study of transition probabilities in 192Pt
International Nuclear Information System (INIS)
Roulet, C.; Sergolle, H.; Hubert, P.P.; Lindblad, T.
1978-01-01
The nucleus 192 Pt is Coulomb excited with 370 MeV 84 Kr projectiles. Levels up to spin 8 + (10 + ) in the ground band and up to 6 + in the γ-band are observed. The B(E2) values implied by the observed yields are compared with predictions of different nuclear models. Particular attention is paid to the ground band transition rates and the rotation-alignment model
Perturbation theory for Markov chains via Wasserstein distance
Rudolf, Daniel; Schweizer, Nikolaus
2017-01-01
Perturbation theory for Markov chains addresses the question of how small differences in the transition probabilities of Markov chains are reflected in differences between their distributions. We prove powerful and flexible bounds on the distance of the nth step distributions of two Markov chains
Fluctuating States: What is the Probability of a Thermodynamical Transition?
Directory of Open Access Journals (Sweden)
Álvaro M. Alhambra
2016-10-01
Full Text Available If the second law of thermodynamics forbids a transition from one state to another, then it is still possible to make the transition happen by using a sufficient amount of work. But if we do not have access to this amount of work, can the transition happen probabilistically? In the thermodynamic limit, this probability tends to zero, but here we find that for finite-sized and quantum systems it can be finite. We compute the maximum probability of a transition or a thermodynamical fluctuation from any initial state to any final state and show that this maximum can be achieved for any final state that is block diagonal in the energy eigenbasis. We also find upper and lower bounds on this transition probability, in terms of the work of transition. As a by-product, we introduce a finite set of thermodynamical monotones related to the thermomajorization criteria which governs state transitions and compute the work of transition in terms of them. The trade-off between the probability of a transition and any partial work added to aid in that transition is also considered. Our results have applications in entanglement theory, and we find the amount of entanglement required (or gained when transforming one pure entangled state into any other.
Crawford, Forrest W.; Suchard, Marc A.
2011-01-01
A birth-death process is a continuous-time Markov chain that counts the number of particles in a system over time. In the general process with n current particles, a new particle is born with instantaneous rate λn and a particle dies with instantaneous rate μn. Currently no robust and efficient method exists to evaluate the finite-time transition probabilities in a general birth-death process with arbitrary birth and death rates. In this paper, we first revisit the theory of continued fractions to obtain expressions for the Laplace transforms of these transition probabilities and make explicit an important derivation connecting transition probabilities and continued fractions. We then develop an efficient algorithm for computing these probabilities that analyzes the error associated with approximations in the method. We demonstrate that this error-controlled method agrees with known solutions and outperforms previous approaches to computing these probabilities. Finally, we apply our novel method to several important problems in ecology, evolution, and genetics. PMID:21984359
Utilizing Gaze Behavior for Inferring Task Transitions Using Abstract Hidden Markov Models
Directory of Open Access Journals (Sweden)
Daniel Fernando Tello Gamarra
2016-12-01
Full Text Available We demonstrate an improved method for utilizing observed gaze behavior and show that it is useful in inferring hand movement intent during goal directed tasks. The task dynamics and the relationship between hand and gaze behavior are learned using an Abstract Hidden Markov Model (AHMM. We show that the predicted hand movement transitions occur consistently earlier in AHMM models with gaze than those models that do not include gaze observations.
Energy Technology Data Exchange (ETDEWEB)
Mora, Ll
1989-11-01
The aim of this work is the generation of sequences of hourly global radiation which have similar statistically characteristics of real sequences for the city of Madrid (Spain). For this generation, a first order Markov model has been proposed. The input parameters of simulation method are the following: The maximum value of hourly radiation and the average monthly value of the transparency normalized index. The maximum value of hourly radiation has been calculated as a function of the solar height by an empirical expression. The transparency normalized index has been defined as the ratio among the measured hourly global radiation to the maximum value for the corresponding solar height. The method is based on the following observations: -The transparency normalized index shows a significant correlation only for two consecutive hours. -The months with the same average transparency normalized indies have similar probability density function. Global solar radiation, time series, simulation, Markov transition matrix, solar energy.
A Bayesian model for binary Markov chains
Directory of Open Access Journals (Sweden)
Belkheir Essebbar
2004-02-01
Full Text Available This note is concerned with Bayesian estimation of the transition probabilities of a binary Markov chain observed from heterogeneous individuals. The model is founded on the Jeffreys' prior which allows for transition probabilities to be correlated. The Bayesian estimator is approximated by means of Monte Carlo Markov chain (MCMC techniques. The performance of the Bayesian estimates is illustrated by analyzing a small simulated data set.
Non-equilibrium random matrix theory. Transition probabilities
Energy Technology Data Exchange (ETDEWEB)
Pedro, Francisco Gil [Univ. Autonoma de Madrid (Spain). Dept. de Fisica Teorica; Westphal, Alexander [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany). Gruppe Theorie
2016-06-15
In this letter we present an analytic method for calculating the transition probability between two random Gaussian matrices with given eigenvalue spectra in the context of Dyson Brownian motion. We show that in the Coulomb gas language, in large N limit, memory of the initial state is preserved in the form of a universal linear potential acting on the eigenvalues. We compute the likelihood of any given transition as a function of time, showing that as memory of the initial state is lost, transition probabilities converge to those of the static ensemble.
Non-equilibrium random matrix theory. Transition probabilities
International Nuclear Information System (INIS)
Pedro, Francisco Gil; Westphal, Alexander
2016-06-01
In this letter we present an analytic method for calculating the transition probability between two random Gaussian matrices with given eigenvalue spectra in the context of Dyson Brownian motion. We show that in the Coulomb gas language, in large N limit, memory of the initial state is preserved in the form of a universal linear potential acting on the eigenvalues. We compute the likelihood of any given transition as a function of time, showing that as memory of the initial state is lost, transition probabilities converge to those of the static ensemble.
International Nuclear Information System (INIS)
Mukhamedov, Farrukh; Saburov, Mansoor
2010-06-01
In the present paper we study forward Quantum Markov Chains (QMC) defined on a Cayley tree. Using the tree structure of graphs, we give a construction of quantum Markov chains on a Cayley tree. By means of such constructions we prove the existence of a phase transition for the XY-model on a Cayley tree of order three in QMC scheme. By the phase transition we mean the existence of two distinct QMC for the given family of interaction operators {K }. (author)
Quantum processes: probability fluxes, transition probabilities in unit time and vacuum vibrations
International Nuclear Information System (INIS)
Oleinik, V.P.; Arepjev, Ju D.
1989-01-01
Transition probabilities in unit time and probability fluxes are compared in studying the elementary quantum processes -the decay of a bound state under the action of time-varying and constant electric fields. It is shown that the difference between these quantities may be considerable, and so the use of transition probabilities W instead of probability fluxes Π, in calculating the particle fluxes, may lead to serious errors. The quantity W represents the rate of change with time of the population of the energy levels relating partly to the real states and partly to the virtual ones, and it cannot be directly measured in experiment. The vacuum background is shown to be continuously distorted when a perturbation acts on a system. Because of this the viewpoint of an observer on the physical properties of real particles continuously varies with time. This fact is not taken into consideration in the conventional theory of quantum transitions based on using the notion of probability amplitude. As a result, the probability amplitudes lose their physical meaning. All the physical information on quantum dynamics of a system is contained in the mean values of physical quantities. The existence of considerable differences between the quantities W and Π permits one in principle to make a choice of the correct theory of quantum transitions on the basis of experimental data. (author)
Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions
International Nuclear Information System (INIS)
Nedialkova, Lilia V.; Amat, Miguel A.; Kevrekidis, Ioannis G.; Hummer, Gerhard
2014-01-01
Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space
Diffusion maps, clustering and fuzzy Markov modeling in peptide folding transitions
Energy Technology Data Exchange (ETDEWEB)
Nedialkova, Lilia V.; Amat, Miguel A. [Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544 (United States); Kevrekidis, Ioannis G., E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de [Department of Chemical and Biological Engineering and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544 (United States); Hummer, Gerhard, E-mail: yannis@princeton.edu, E-mail: gerhard.hummer@biophys.mpg.de [Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max-von-Laue-Str. 3, 60438 Frankfurt am Main (Germany)
2014-09-21
Using the helix-coil transitions of alanine pentapeptide as an illustrative example, we demonstrate the use of diffusion maps in the analysis of molecular dynamics simulation trajectories. Diffusion maps and other nonlinear data-mining techniques provide powerful tools to visualize the distribution of structures in conformation space. The resulting low-dimensional representations help in partitioning conformation space, and in constructing Markov state models that capture the conformational dynamics. In an initial step, we use diffusion maps to reduce the dimensionality of the conformational dynamics of Ala5. The resulting pretreated data are then used in a clustering step. The identified clusters show excellent overlap with clusters obtained previously by using the backbone dihedral angles as input, with small—but nontrivial—differences reflecting torsional degrees of freedom ignored in the earlier approach. We then construct a Markov state model describing the conformational dynamics in terms of a discrete-time random walk between the clusters. We show that by combining fuzzy C-means clustering with a transition-based assignment of states, we can construct robust Markov state models. This state-assignment procedure suppresses short-time memory effects that result from the non-Markovianity of the dynamics projected onto the space of clusters. In a comparison with previous work, we demonstrate how manifold learning techniques may complement and enhance informed intuition commonly used to construct reduced descriptions of the dynamics in molecular conformation space.
DEFF Research Database (Denmark)
Kessler, Timo Christian; Nilsson, Bertel; Klint, Knud Erik
2010-01-01
(TPROGS) of alternating geological facies. The second method, multiple-point statistics, uses training images to estimate the conditional probability of sand-lenses at a certain location. Both methods respect field observations such as local stratigraphy, however, only the multiple-point statistics can...... of sand-lenses in clay till. Sand-lenses mainly account for horizontal transport and are prioritised in this study. Based on field observations, the distribution has been modeled using two different geostatistical approaches. One method uses a Markov chain model calculating the transition probabilities...
Hartfiel, Darald J
1998-01-01
In this study extending classical Markov chain theory to handle fluctuating transition matrices, the author develops a theory of Markov set-chains and provides numerous examples showing how that theory can be applied. Chapters are concluded with a discussion of related research. Readers who can benefit from this monograph are those interested in, or involved with, systems whose data is imprecise or that fluctuate with time. A background equivalent to a course in linear algebra and one in probability theory should be sufficient.
Collective fluctuations in magnetized plasma: Transition probability approach
International Nuclear Information System (INIS)
Sosenko, P.P.
1997-01-01
Statistical plasma electrodynamics is elaborated with special emphasis on the transition probability approach and quasi-particles, and on modern applications to magnetized plasmas. Fluctuation spectra in the magnetized plasma are calculated in the range of low frequencies (with respect to the cyclotron one), and the conditions for the transition from incoherent to collective fluctuations are established. The role of finite-Larmor-radius effects and particle polarization drift in such a transition is explained. The ion collective features in fluctuation spectra are studied. 63 refs., 30 figs
International Nuclear Information System (INIS)
Floriani, Elena; Lima, Ricardo; Ourrad, Ouerdia; Spinelli, Lionel
2016-01-01
Highlights: • The flux through a Markov chain of a conserved quantity (mass) is studied. • Mass is supplied by an external source and ends in the absorbing states of the chain. • Meaningful for modeling open systems whose dynamics has a Markov property. • The analytical expression of mass distribution is given for a constant source. • The expression of mass distribution is given for periodic or random sources. - Abstract: In this paper we study the flux through a finite Markov chain of a quantity, that we will call mass, which moves through the states of the chain according to the Markov transition probabilities. Mass is supplied by an external source and accumulates in the absorbing states of the chain. We believe that studying how this conserved quantity evolves through the transient (non-absorbing) states of the chain could be useful for the modelization of open systems whose dynamics has a Markov property.
Transition probabilities between levels of K and K+
International Nuclear Information System (INIS)
Campos Gutierrez, J.; Martin Vicente, A.
1984-01-01
In this work transition probabilities between Ievels of n < 11 for K and for the known of K+ are calculated. Two computer programs based on the Coulomb approximation and the most suitable coupling schemes has been used. Lifetimes of all these levels are also calculated. (Author)
Analysis of a quantum Markov chain
International Nuclear Information System (INIS)
Marbeau, J.; Gudder, S.
1990-01-01
A quantum chain is analogous to a classical stationary Markov chain except that the probability measure is replaced by a complex amplitude measure and the transition probability matrix is replaced by a transition amplitude matrix. After considering the general situation, we study a particular example of a quantum chain whose transition amplitude matrix has the form of a Dirichlet matrix. Such matrices generate a discrete analog of the usual continuum Feynman amplitude. We then compute the probability distribution for these quantum chains
Directory of Open Access Journals (Sweden)
Thadeu Keller Filho
2006-09-01
Full Text Available O objetivo deste trabalho foi verificar se as ocorrências de dias secos e chuvosos são condicionalmente dependentes da seqüência dos três dias secos e chuvosos anteriores, numa zona pluviometricamente homogênea, por meio da cadeia não-homogênea de Markov de terceira ordem. Os resultados mostraram que as probabilidades diárias de transição podem ser adequadamente estimadas, com base em dados agregados bimestralmente, seguidas de interpolação por meio de funções sinusoidais. Além disso, evidenciou-se que, naquela zona, as ocorrências diárias de chuva são condicionalmente dependentes da seqüência de dias secos e chuvosos nos três dias anteriores. A cadeia não-homogênea de Markov de terceira ordem é um importante instrumento para a análise da dependência entre as seqüências de dias secos e chuvosos em determinadas regiões.The aim of this work was to verify if the occurrence of dry and wet days are conditionally dependent on the sequences of the dry and wet three preceding days, in a rainfall homogeneous area, using the nonhomogeneous third-order Markov chains. The results showed that daily transition probabilities can be properly estimated from two-month aggregate data, and then adjusted by means of sinusoidal functions. Besides, it was evidenced that everyday rain events in that area are conditionally dependent on the sequences of the dry and wet three days previous to occurrences. The third-order nonhomogeneous Markov chains are an important instrument for the analysis of the dependence between sequences of dry and wet days in certain areas.
Human Inferences about Sequences: A Minimal Transition Probability Model.
Directory of Open Access Journals (Sweden)
Florent Meyniel
2016-12-01
Full Text Available The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge.
Absolute Kr I and Kr II transition probabilities
International Nuclear Information System (INIS)
Brandt, T.; Helbig, V.; Nick, K.P.
1982-01-01
Transition probabilities for 11 KrI and 9 KrII lines between 366.5 and 599.3nm were obtained from measurements with a wall-stabilised arc at atmospheric pressure in pure krypton. The population densities of the excited krypton levels were calculated under the assumption of LTE from electron densities measured by laser interferometry. The uncertainties for the KrI and the KrII data are 15 and 25% respectively. (author)
Pemodelan Markov Switching Autoregressive
Ariyani, Fiqria Devi; Warsito, Budi; Yasin, Hasbi
2014-01-01
Transition from depreciation to appreciation of exchange rate is one of regime switching that ignored by classic time series model, such as ARIMA, ARCH, or GARCH. Therefore, economic variables are modeled by Markov Switching Autoregressive (MSAR) which consider the regime switching. MLE is not applicable to parameters estimation because regime is an unobservable variable. So that filtering and smoothing process are applied to see the regime probabilities of observation. Using this model, tran...
Energy levels and transition probabilities for Fe XXV ions
Energy Technology Data Exchange (ETDEWEB)
Norrington, P.H.; Kingston, A.E.; Boone, A.W. [Department of Applied Maths and Theoretical Physics, Queen' s University, Belfast BT7 1NN (United Kingdom)
2000-05-14
The energy levels of the 1s{sup 2}, 1s2l and 1s3l states of helium-like iron Fe XXV have been calculated using two sets of configuration-interaction wavefunctions. One set of wavefunctions was generated using the fully relativistic GRASP code and the other was obtained using CIV3, in which relativistic effects are introduced using the Breit-Pauli approximation. For transitions from the ground state to the n=2 and 3 states and for transitions between the n=2 and 3 states, the calculated excitation energies obtained by these two independent methods are in very good agreement and there is good agreement between these results and recent theoretical and experimental results. However, there is considerable disagreement between the various excitation energies for the transitions among the n=2 and also among the n=3 states. The two sets of wavefunctions are also used to calculate the E1, E2, M1 and M2 transition probabilities between all of the 1s{sup 2}, 1s2l and 1s3l states of helium-like iron Fe XXV. The results from the two calculations are found to be similar and to compare very well with other recent results for {delta}n=1 or 2 transitions. For {delta}n=0 transitions the agreement is much less satisfactory; this is mainly due to differences in the excitation energies. (author)
Adaptive Partially Hidden Markov Models
DEFF Research Database (Denmark)
Forchhammer, Søren Otto; Rasmussen, Tage
1996-01-01
Partially Hidden Markov Models (PHMM) have recently been introduced. The transition and emission probabilities are conditioned on the past. In this report, the PHMM is extended with a multiple token version. The different versions of the PHMM are applied to bi-level image coding....
Aralis, Hilary; Brookmeyer, Ron
2017-01-01
Multistate models provide an important method for analyzing a wide range of life history processes including disease progression and patient recovery following medical intervention. Panel data consisting of the states occupied by an individual at a series of discrete time points are often used to estimate transition intensities of the underlying continuous-time process. When transition intensities depend on the time elapsed in the current state and back transitions between states are possible, this intermittent observation process presents difficulties in estimation due to intractability of the likelihood function. In this manuscript, we present an iterative stochastic expectation-maximization algorithm that relies on a simulation-based approximation to the likelihood function and implement this algorithm using rejection sampling. In a simulation study, we demonstrate the feasibility and performance of the proposed procedure. We then demonstrate application of the algorithm to a study of dementia, the Nun Study, consisting of intermittently-observed elderly subjects in one of four possible states corresponding to intact cognition, impaired cognition, dementia, and death. We show that the proposed stochastic expectation-maximization algorithm substantially reduces bias in model parameter estimates compared to an alternative approach used in the literature, minimal path estimation. We conclude that in estimating intermittently observed semi-Markov models, the proposed approach is a computationally feasible and accurate estimation procedure that leads to substantial improvements in back transition estimates.
Gac, J. M.; Żebrowski, J. J.
A chaotic transition occurs when a continuous change of one of the parameters of the system causes a discontinuous change in the properties of the chaotic attractor of the system. Such phenomena are present in many dynamical systems, in which a chaotic behavior occurs. The best known of these transitions are: the period-doubling bifurcation cascade, intermittency and crises. The effect of dichotomous Markov noise (DMN) on the properties of systems with chaotic transitions is discussed. DMN is a very simple two-valued stochastic process, with constant transition rates between the two states. In spite of its simplicity, this kind of noise is a very powerful tool to describe various phenomena present in many physical, chemical or biological systems. Many interesting phenomena induced by DMN are known. However, there is no research on the effect of this kind of noise on intermittency or crises. We present the change of the mean laminar phase length and of laminar phase length distribution caused by DMN modulating the parameters of a system with intermittency and the modification of the mean life time on the pre-crisis attractor in the case of a boundary crisis. The results obtained analytically are compared with numerical simulations for several simple dynamical systems.
Oscillator strengths and transition probabilities for the intercombination transitions in Fe XXII
International Nuclear Information System (INIS)
Glass, R.
1979-01-01
Oscillator strengths and transition probabilities are evaluated for the intercombination transitions between the 2s 2 2p, 2s 2p 2 and 2p 3 states of Fe XXII using configuration interaction wavefunctions. The fine-structure splittings have also been calculated. Some significant differences with previous calculations are obtained
Correlation effects on transition probabilities in Mo vi
International Nuclear Information System (INIS)
Froese Fischer, Charlotte
2011-01-01
The effect of correlation on transition probabilities for transitions in Mo vi from 4p 6 4d 2 D and 4p 6 5s 2 S to 4p 6 4f, 4p 6 5p, 4p 6 5f, 4p 5 4d 2 with J = 1/2-7/2 is investigated. Non-relativistic correlation studies show the near degeneracy of 4p 5 4d 2 ( 3 F) 2 F o and 4p 5 4d 2 ( 1 G) 2 F o configuration state functions and their strong interaction with 4p 6 5f 2 F o . The multiconfiguration Dirac-Hartree-Fock method is used to include relativistic effects and correlation simultaneously. Wavefunction composition is compared with other theory and with the least-squares fitted values recently published by Reader (2010 J. Phys. B: At. Mol. Opt. Phys. 43 074024). Transition probability data are provided along with data required for accessing accuracy. Results are compared with other published values.
Markov Chain: A Predictive Model for Manpower Planning ...
African Journals Online (AJOL)
ADOWIE PERE
Keywords: Markov Chain, Transition Probability Matrix, Manpower Planning, Recruitment, Promotion, .... movement of the workforce in Jordan productivity .... Planning periods, with T being the horizon, the value of t represents a session.
Markov chain: a predictive model for manpower planning | Ezugwu ...
African Journals Online (AJOL)
In respect of organizational management, numerous previous studies have ... and to forecast the academic staff structure of the university in the next five years. ... Keywords: Markov Chain, Transition Probability Matrix, Manpower Planning, ...
Scale-invariant transition probabilities in free word association trajectories
Directory of Open Access Journals (Sweden)
Martin Elias Costa
2009-09-01
Full Text Available Free-word association has been used as a vehicle to understand the organization of human thoughts. The original studies relied mainly on qualitative assertions, yielding the widely intuitive notion that trajectories of word associations are structured, yet considerably more random than organized linguistic text. Here we set to determine a precise characterization of this space, generating a large number of word association trajectories in a web implemented game. We embedded the trajectories in the graph of word co-occurrences from a linguistic corpus. To constrain possible transport models we measured the memory loss and the cycling probability. These two measures could not be reconciled by a bounded diffusive model since the cycling probability was very high (16 % of order-2 cycles implying a majority of short-range associations whereas the memory loss was very rapid (converging to the asymptotic value in ∼ 7 steps which, in turn, forced a high fraction of long-range associations. We show that memory loss and cycling probabilities of free word association trajectories can be simultaneously accounted by a model in which transitions are determined by a scale invariant probability distribution.
Markov processes and controlled Markov chains
Filar, Jerzy; Chen, Anyue
2002-01-01
The general theory of stochastic processes and the more specialized theory of Markov processes evolved enormously in the second half of the last century. In parallel, the theory of controlled Markov chains (or Markov decision processes) was being pioneered by control engineers and operations researchers. Researchers in Markov processes and controlled Markov chains have been, for a long time, aware of the synergies between these two subject areas. However, this may be the first volume dedicated to highlighting these synergies and, almost certainly, it is the first volume that emphasizes the contributions of the vibrant and growing Chinese school of probability. The chapters that appear in this book reflect both the maturity and the vitality of modern day Markov processes and controlled Markov chains. They also will provide an opportunity to trace the connections that have emerged between the work done by members of the Chinese school of probability and the work done by the European, US, Central and South Ameri...
Markov Transition Model to Dementia with Death as a Competing Event.
Wei, Shaoceng; Xu, Liou; Kryscio, Richard J
2014-12-01
This study evaluates the effect of death as a competing event to the development of dementia in a longitudinal study of the cognitive status of elderly subjects. A multi-state Markov model with three transient states: intact cognition, mild cognitive impairment (M.C.I.) and global impairment (G.I.) and one absorbing state: dementia is used to model the cognitive panel data; transitions among states depend on four covariates age, education, prior state (intact cognition, or M.C.I., or G.I.) and the presence/absence of an apolipoprotein E-4 allele (APOE4). A Weibull model and a Cox proportional hazards (Cox PH) model are used to fit the survival from death based on age at entry and the APOE4 status. A shared random effect correlates this survival time with the transition model. Simulation studies determine the sensitivity of the maximum likelihood estimates to the violations of the Weibull and Cox PH model assumptions. Results are illustrated with an application to the Nun Study, a longitudinal cohort of 672 participants 75+ years of age at baseline and followed longitudinally with up to ten cognitive assessments per nun.
Capturing the state transitions of seizure-like events using Hidden Markov models.
Guirgis, Mirna; Serletis, Demitre; Carlen, Peter L; Bardakjian, Berj L
2011-01-01
The purpose of this study was to investigate the number of states present in the progression of a seizure-like event (SLE). Of particular interest is to determine if there are more than two clearly defined states, as this would suggest that there is a distinct state preceding an SLE. Whole-intact hippocampus from C57/BL mice was used to model epileptiform activity induced by the perfusion of a low Mg(2+)/high K(+) solution while extracellular field potentials were recorded from CA3 pyramidal neurons. Hidden Markov models (HMM) were used to model the state transitions of the recorded SLEs by incorporating various features of the Hilbert transform into the training algorithm; specifically, 2- and 3-state HMMs were explored. Although the 2-state model was able to distinguish between SLE and nonSLE behavior, it provided no improvements compared to visual inspection alone. However, the 3-state model was able to capture two distinct nonSLE states that visual inspection failed to discriminate. Moreover, by developing an HMM based system a priori knowledge of the state transitions was not required making this an ideal platform for seizure prediction algorithms.
Construction of unitary matrices from observable transition probabilities
International Nuclear Information System (INIS)
Peres, A.
1989-01-01
An ideal measuring apparatus defines an orthonormal basis vertical strokeu m ) in Hilbert space. Another apparatus defines another basis vertical strokeυ μ ). Both apparatuses together allow to measure the transition probabilities P mμ =vertical stroke(u m vertical strokeυ μ )vertical stroke 2 . The problem is: Given all the elements of a doubly stochastic matrix P mμ , find a unitary matrix U mμ such that P mμ =vertical strokeU mμ vertical stroke 2 . The number of unknown nontrivial phases is equal to the number of independent equations to satisfy. The problem can therefore be solved provided that the values of the P mμ satisfy some inequalities. (orig.)
Transition probabilities in neutron-rich Se,8684
Litzinger, J.; Blazhev, A.; Dewald, A.; Didierjean, F.; Duchêne, G.; Fransen, C.; Lozeva, R.; Sieja, K.; Verney, D.; de Angelis, G.; Bazzacco, D.; Birkenbach, B.; Bottoni, S.; Bracco, A.; Braunroth, T.; Cederwall, B.; Corradi, L.; Crespi, F. C. L.; Désesquelles, P.; Eberth, J.; Ellinger, E.; Farnea, E.; Fioretto, E.; Gernhäuser, R.; Goasduff, A.; Görgen, A.; Gottardo, A.; Grebosz, J.; Hackstein, M.; Hess, H.; Ibrahim, F.; Jolie, J.; Jungclaus, A.; Kolos, K.; Korten, W.; Leoni, S.; Lunardi, S.; Maj, A.; Menegazzo, R.; Mengoni, D.; Michelagnoli, C.; Mijatovic, T.; Million, B.; Möller, O.; Modamio, V.; Montagnoli, G.; Montanari, D.; Morales, A. I.; Napoli, D. R.; Niikura, M.; Pollarolo, G.; Pullia, A.; Quintana, B.; Recchia, F.; Reiter, P.; Rosso, D.; Sahin, E.; Salsac, M. D.; Scarlassara, F.; Söderström, P.-A.; Stefanini, A. M.; Stezowski, O.; Szilner, S.; Theisen, Ch.; Valiente Dobón, J. J.; Vandone, V.; Vogt, A.
2015-12-01
Reduced quadrupole transition probabilities for low-lying transitions in neutron-rich Se,8684 are investigated with a recoil distance Doppler shift (RDDS) experiment. The experiment was performed at the Istituto Nazionale di Fisica Nucleare (INFN) Laboratori Nazionali di Legnaro using the Cologne Plunger device for the RDDS technique and the AGATA Demonstrator array for the γ -ray detection coupled to the PRISMA magnetic spectrometer for an event-by-event particle identification. In 86Se the level lifetime of the yrast 21+ state and an upper limit for the lifetime of the 41+ state are determined for the first time. The results of 86Se are in agreement with previously reported predictions of large-scale shell-model calculations using Ni78-I and Ni78-II effective interactions. In addition, intrinsic shape parameters of lowest yrast states in 86Se are calculated. In semimagic 84Se level lifetimes of the yrast 41+ and 61+ states are determined for the first time. Large-scale shell-model calculations using effective interactions Ni78-II, JUN45, jj4b, and jj4pna are performed. The calculations describe B (E 2 ;21+→01+) and B (E 2 ;61+→41+) fairly well and point out problems in reproducing the experimental B (E 2 ;41+→21+) .
Calculation of transition probabilities using the multiconfiguration Dirac-Fock method
International Nuclear Information System (INIS)
Kim, Yong Ki; Desclaux, Jean Paul; Indelicato, Paul
1998-01-01
The performance of the multiconfiguration Dirac-Fock (MCDF) method in calculating transition probabilities of atoms is reviewed. In general, the MCDF wave functions will lead to transition probabilities accurate to ∼ 10% or better for strong, electric-dipole allowed transitions for small atoms. However, it is more difficult to get reliable transition probabilities for weak transitions. Also, some MCDF wave functions for a specific J quantum number may not reduce to the appropriate L and S quantum numbers in the nonrelativistic limit. Transition probabilities calculated from such MCDF wave functions for nonrelativistically forbidden transitions are unreliable. Remedies for such cases are discussed
Mittag, Maria; Takegata, Rika; Winkler, István
2016-09-14
Representations encoding the probabilities of auditory events do not directly support predictive processing. In contrast, information about the probability with which a given sound follows another (transitional probability) allows predictions of upcoming sounds. We tested whether behavioral and cortical auditory deviance detection (the latter indexed by the mismatch negativity event-related potential) relies on probabilities of sound patterns or on transitional probabilities. We presented healthy adult volunteers with three types of rare tone-triplets among frequent standard triplets of high-low-high (H-L-H) or L-H-L pitch structure: proximity deviant (H-H-H/L-L-L), reversal deviant (L-H-L/H-L-H), and first-tone deviant (L-L-H/H-H-L). If deviance detection was based on pattern probability, reversal and first-tone deviants should be detected with similar latency because both differ from the standard at the first pattern position. If deviance detection was based on transitional probabilities, then reversal deviants should be the most difficult to detect because, unlike the other two deviants, they contain no low-probability pitch transitions. The data clearly showed that both behavioral and cortical auditory deviance detection uses transitional probabilities. Thus, the memory traces underlying cortical deviance detection may provide a link between stimulus probability-based change/novelty detectors operating at lower levels of the auditory system and higher auditory cognitive functions that involve predictive processing. Our research presents the first definite evidence for the auditory system prioritizing transitional probabilities over probabilities of individual sensory events. Forming representations for transitional probabilities paves the way for predictions of upcoming sounds. Several recent theories suggest that predictive processing provides the general basis of human perception, including important auditory functions, such as auditory scene analysis. Our
International Nuclear Information System (INIS)
Hirschmann, H.
1983-06-01
The consequences of the basic assumptions of the semi-Markov process as defined from a homogeneous renewal process with a stationary Markov condition are reviewed. The notion of the semi-Markov process is generalized by its redefinition from a nonstationary Markov renewal process. For both the nongeneralized and the generalized case a representation of the first order conditional state probabilities is derived in terms of the transition probabilities of the Markov renewal process. Some useful calculation rules (regeneration rules) are derived for the conditional state probabilities of the semi-Markov process. Compared to the semi-Markov process in its usual definition the generalized process allows the analysis of a larger class of systems. For instance systems with arbitrarily distributed lifetimes of their components can be described. There is also a chance to describe systems which are modified during time by forces or manipulations from outside. (Auth.)
Hsiu Chen, Chen; Wen, Fur-Hsing; Hou, Ming-Mo; Hsieh, Chia-Hsun; Chou, Wen-Chi; Chen, Jen-Shi; Chang, Wen-Cheng; Tang, Siew Tzuh
2017-09-01
Developing accurate prognostic awareness, a cornerstone of preference-based end-of-life (EOL) care decision-making, is a dynamic process involving more prognostic-awareness states than knowing or not knowing. Understanding the transition probabilities and time spent in each prognostic-awareness state can help clinicians identify trigger points for facilitating transitions toward accurate prognostic awareness. We examined transition probabilities in distinct prognostic-awareness states between consecutive time points in 247 cancer patients' last 6 months and estimated the time spent in each state. Prognostic awareness was categorized into four states: (a) unknown and not wanting to know, state 1; (b) unknown but wanting to know, state 2; (c) inaccurate awareness, state 3; and (d) accurate awareness, state 4. Transitional probabilities were examined by multistate Markov modeling. Initially, 59.5% of patients had accurate prognostic awareness, whereas the probabilities of being in states 1-3 were 8.1%, 17.4%, and 15.0%, respectively. Patients' prognostic awareness generally remained unchanged (probabilities of remaining in the same state: 45.5%-92.9%). If prognostic awareness changed, it tended to shift toward higher prognostic-awareness states (probabilities of shifting to state 4 were 23.2%-36.6% for patients initially in states 1-3, followed by probabilities of shifting to state 3 for those in states 1 and 2 [9.8%-10.1%]). Patients were estimated to spend 1.29, 0.42, 0.68, and 3.61 months in states 1-4, respectively, in their last 6 months. Terminally ill cancer patients' prognostic awareness generally remained unchanged, with a tendency to become more aware of their prognosis. Health care professionals should facilitate patients' transitions toward accurate prognostic awareness in a timely manner to promote preference-based EOL decisions. Terminally ill Taiwanese cancer patients' prognostic awareness generally remained stable, with a tendency toward developing
Wang, Kang-Ning; Sun, Zan-Dong; Dong, Ning
2015-12-01
Economic shale gas production requires hydraulic fracture stimulation to increase the formation permeability. Hydraulic fracturing strongly depends on geomechanical parameters such as Young's modulus and Poisson's ratio. Fracture-prone sweet spots can be predicted by prestack inversion, which is an ill-posed problem; thus, regularization is needed to obtain unique and stable solutions. To characterize gas-bearing shale sedimentary bodies, elastic parameter variations are regarded as an anisotropic Markov random field. Bayesian statistics are adopted for transforming prestack inversion to the maximum posterior probability. Two energy functions for the lateral and vertical directions are used to describe the distribution, and the expectation-maximization algorithm is used to estimate the hyperparameters of the prior probability of elastic parameters. Finally, the inversion yields clear geological boundaries, high vertical resolution, and reasonable lateral continuity using the conjugate gradient method to minimize the objective function. Antinoise and imaging ability of the method were tested using synthetic and real data.
Transition probabilities and radiative decay constants of the excited levels of Ne
International Nuclear Information System (INIS)
Wosinski, L.
1981-01-01
Transition probabilities for eight optical transitions between the 3p and 3d neon levels have been measured by the ''plasma transparency method''. The transitions probabilities are placed on an absolute scale by use of the recently reported values for the 4p→3s transitions. The measurements of induced changes in populations allowed the determination of the ratios of the radiative decay constants for the 4p and 3d levels. The experimental results are compared with the theoretically calculated transitions probabilities of Murphy and Lilly. (author)
Measurements of atomic transition probabilities in highly ionized atoms by fast ion beams
International Nuclear Information System (INIS)
Martinson, I.; Curtis, L.J.; Lindgaerd, A.
1977-01-01
A summary is given of the beam-foil method by which level lifetimes and transition probabilities can be determined in atoms and ions. Results are presented for systems of particular interest for fusion research, such as the Li, Be, Na, Mg, Cu and Zn isoelectronic sequences. The available experimental material is compared to theoretical transition probabilities. (author)
Wei, Shaoceng; Kryscio, Richard J
2016-12-01
Continuous-time multi-state stochastic processes are useful for modeling the flow of subjects from intact cognition to dementia with mild cognitive impairment and global impairment as intervening transient cognitive states and death as a competing risk. Each subject's cognition is assessed periodically resulting in interval censoring for the cognitive states while death without dementia is not interval censored. Since back transitions among the transient states are possible, Markov chains are often applied to this type of panel data. In this manuscript, we apply a semi-Markov process in which we assume that the waiting times are Weibull distributed except for transitions from the baseline state, which are exponentially distributed and in which we assume no additional changes in cognition occur between two assessments. We implement a quasi-Monte Carlo (QMC) method to calculate the higher order integration needed for likelihood estimation. We apply our model to a real dataset, the Nun Study, a cohort of 461 participants. © The Author(s) 2014.
Process Modeling for Energy Usage in “Smart House” System with a Help of Markov Discrete Chain
Directory of Open Access Journals (Sweden)
Victor Kravets
2016-05-01
Full Text Available Method for evaluating economic efficiency of technical systems using discrete Markov chains modelling illustrated by the system of “Smart house”, consisting, for example, of the three independently functioning elements. Dynamic model of a random power consumption process in the form of a symmetrical state graph of heterogeneous discrete Markov chain is built. The corresponding mathematical model of a random Markov process of power consumption in the “smart house” system in recurrent matrix form is being developed. Technique of statistical determination of probability of random transition elements of the system and the corresponding to the transition probability matrix of the discrete inhomogeneous Markov chain are developed. Statistically determined random transitions of system elements power consumption and the corresponding distribution laws are introduced. The matrix of transition prices, expectations for the possible states of a system price transition and, eventually, the cost of Markov process of power consumption throughout the day.
Analytic results for asymmetric random walk with exponential transition probabilities
International Nuclear Information System (INIS)
Gutkowicz-Krusin, D.; Procaccia, I.; Ross, J.
1978-01-01
We present here exact analytic results for a random walk on a one-dimensional lattice with asymmetric, exponentially distributed jump probabilities. We derive the generating functions of such a walk for a perfect lattice and for a lattice with absorbing boundaries. We obtain solutions for some interesting moment properties, such as mean first passage time, drift velocity, dispersion, and branching ratio for absorption. The symmetric exponential walk is solved as a special case. The scaling of the mean first passage time with the size of the system for the exponentially distributed walk is determined by the symmetry and is independent of the range
The determination of transition probabilities with an inductively-coupled plasma discharge
International Nuclear Information System (INIS)
Nieuwoudt, G.
1984-03-01
The 27 MHz inductively-coupled plasma discharge (ICP) is used for the determination of relative transition probabilities of the 451, 459 and 470 nm argon spectral lines. The temperature of the argon plasma is determined with hydrogen as thermometric specie, because of the accurate transition probabilities ( approximately 1% uncertainty) there of. The relative transition probabilities of the specific argon spectral lines were determined by substitution of the measured spectral radiances thereof, together with the hydrogen temperature, in the two-line equation of temperature measurement
Quantum Zeno and anti-Zeno effects measured by transition probabilities
Energy Technology Data Exchange (ETDEWEB)
Zhang, Wenxian, E-mail: wxzhang@whu.edu.cn [School of Physics and Technology, Wuhan University, Wuhan, Hubei 430072 (China); Department of Optical Science and Engineering, Fudan University, Shanghai 200433 (China); CEMS, RIKEN, Saitama 351-0198 (Japan); Kavli Institute for Theoretical Physics China, CAS, Beijing 100190 (China); Kofman, A.G. [CEMS, RIKEN, Saitama 351-0198 (Japan); Department of Physics, The University of Michigan, Ann Arbor, MI 48109-1040 (United States); Zhuang, Jun [Department of Optical Science and Engineering, Fudan University, Shanghai 200433 (China); You, J.Q. [Beijing Computational Science Research Center, Beijing 10084 (China); Department of Physics, Fudan University, Shanghai 200433 (China); CEMS, RIKEN, Saitama 351-0198 (Japan); Nori, Franco [CEMS, RIKEN, Saitama 351-0198 (Japan); Department of Physics, The University of Michigan, Ann Arbor, MI 48109-1040 (United States)
2013-10-30
Using numerical calculations, we compare the transition probabilities of many spins in random magnetic fields, subject to either frequent projective measurements, frequent phase modulations, or a mix of modulations and measurements. For various distribution functions, we find the transition probability under frequent modulations is suppressed most if the pulse delay is short and the evolution time is larger than a critical value. Furthermore, decay freezing occurs only under frequent modulations as the pulse delay approaches zero. In the large pulse-delay region, however, the transition probabilities under frequent modulations are highest among the three control methods.
Transition probabilities up to I = 36+ in 160Yb
International Nuclear Information System (INIS)
Johnson, N.R.; McGowan, F.K.; Winchell, D.F.; Baktash, C.; Garrett, J.D.; Lee, I.Y.; Wells, J.C.; Chaturvedi, L.; Gao, W.B.; Ma, W.C.; Pilotte, S.; Yu, C.H.
1992-01-01
During the past dozen years or so, numerous groups have worked on the properties of the ytterbium nuclei and we at Oak Ridge have been actively involved in the study of many of these nuclei. We have concentrated on lifetime measurements of their high-spin states because it is from the lifetime of a state that one can determine Q t , the transition quadrupole moment. The importance of obtaining a Q t value is in that it reflects the intrinsic part of the wave function and, hence, provides an indicator of the collectivity. This paper presents the results from recent Doppler broadened line shape measurements in 16O Yb at very large rotational frequencies of bar Hω∼0.36 - 0.50 MeV (I = 22 + -36 + )
Are Einstein's transition probabilities for spontaneous emission constant in plasmas?
Griem, H. R.; Huang, Y. W.; Wang, J.-S.; Moreno, J. C.
1991-01-01
An investigation is conducted with a ruby laser to experimentally confirm the quenching of spontaneous emission coefficients and propose a mechanism for the phenomenon. Results of previous experiments are examined to determine the consistency and validity of interpretations of the spontaneous emissions. For the C IV 3s-3p and 2s-3p transitions, the line-intensity ratios are found to be dependent on the separation of the laser from the target. Density gradients and Stark broadening are proposed to interpret the results in a way that does not invalidate the Einstein A values. The interpretation is extended to C III and N V, both of which demonstrate similar changes in A values in previous experiments. The apparent quenching of Ar II by photon collisions is explained by Rabi oscillations and power broadening in the argon-ion laser cavity. It is concluded that the changes in A values cannot result from dense plasma effects.
Tveito, Aslak; Lines, Glenn T; Edwards, Andrew G; McCulloch, Andrew
2016-07-01
Markov models are ubiquitously used to represent the function of single ion channels. However, solving the inverse problem to construct a Markov model of single channel dynamics from bilayer or patch-clamp recordings remains challenging, particularly for channels involving complex gating processes. Methods for solving the inverse problem are generally based on data from voltage clamp measurements. Here, we describe an alternative approach to this problem based on measurements of voltage traces. The voltage traces define probability density functions of the functional states of an ion channel. These probability density functions can also be computed by solving a deterministic system of partial differential equations. The inversion is based on tuning the rates of the Markov models used in the deterministic system of partial differential equations such that the solution mimics the properties of the probability density function gathered from (pseudo) experimental data as well as possible. The optimization is done by defining a cost function to measure the difference between the deterministic solution and the solution based on experimental data. By evoking the properties of this function, it is possible to infer whether the rates of the Markov model are identifiable by our method. We present applications to Markov model well-known from the literature. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
Markov chain analysis of single spin flip Ising simulations
International Nuclear Information System (INIS)
Hennecke, M.
1997-01-01
The Markov processes defined by random and loop-based schemes for single spin flip attempts in Monte Carlo simulations of the 2D Ising model are investigated, by explicitly constructing their transition matrices. Their analysis reveals that loops over all lattice sites using a Metropolis-type single spin flip probability often do not define ergodic Markov chains, and have distorted dynamical properties even if they are ergodic. The transition matrices also enable a comparison of the dynamics of random versus loop spin selection and Glauber versus Metropolis probabilities
Systematics of Absolute Gamma Ray Transition Probabilities in Deformed Odd-A Nuclei
Energy Technology Data Exchange (ETDEWEB)
Malmskog, S G
1965-11-15
All known experimentally determined absolute gamma ray transition probabilities between different intrinsic states of deformed odd-A nuclei in the rare earth, region (153 < A < 181) and in the actinide region (A {>=} 227) are compared with transition probabilities (Weisskopf and Nilsson estimate). Systematic deviations from the theoretical values are found. Possible explanations for these deviations are given. This discussion includes Coriolis coupling, {delta}K ={+-}2 band-mixing effects and pairing interaction.
Optical transition probabilities in electron-vibration-rotation spectra of diatomic molecules
International Nuclear Information System (INIS)
Kuznetsova, L.A.; Kuz'menko, N.E.; Kuzyakov, Yu.Ya.; Plastinin, Yu.A.
1974-01-01
The present review systematizes the data on the absolute probabilities of electron transitions in diatomic molecules, which have been published since the beginning of 1961 and up to the end of 1973, and those on the relative transition probabilities, which have been published since the beginning of 1966 till the end of 1973. The review discussed the theoretical relationships underlying the experimental techniques of determining the absolute transition probabilities. Modifications of the techniques under discussion are not specially examined; the details of interest can be found, however, in the references cited. The factual material-, such as the values of the absolute probabilities of electron transitions, the dependences of the electron transition moments on the internuclear distance and the values of the Franck-Condon factors,- is presented in tables 1, 2 and 4, respectively, embracing all the relevant works known to the present authors. Along with a complete systematization of the transition probability data, the authors have attempted a critical analysis of the available data in order to select the most reliable results. The recommended values of the squared matrix elements of the electron transition dipole moments are given in table 3. The last chaper of the work compares the results of calculations of the Franck-Condon factors obtained with the different milecular potentials [ru
New measurements of spontaneous transition probabilities for beryllium-like ions
International Nuclear Information System (INIS)
Lang, J.; Hardcastle, R.A.; McWhirter, R.W.P.; Spurrett, P.H.
1986-06-01
The authors describe measurements of spectral line intensities for pairs of transitions having common upper levels and thus derive the branching ratios of their spontaneous radiative transition probabilities. These are then combined with the results of measurements of the radiative lifetimes of the upper levels by other authors to obtain values of the individual transition probabilities. The results are for transitions in NIV, OV and NeVII and are given with a claimed accuracy of between 7% and 38%. These are compared with values calculated theoretically. For some of the simpler electric dipole transitions good agreement is found. On the other hand for some of the other transitions which in certain cases are only possible because of configuration interaction disparities between the present measurements and theory are as large as x5. (author)
Energy-level scheme and transition probabilities of Si-like ions
International Nuclear Information System (INIS)
Huang, K.N.
1984-01-01
Theoretical energy levels and transition probabilities are presented for 27 low-lying levels of silicon-like ions from Z = 15 to Z = 106. The multiconfiguration Dirac-Fock technique is used to calculate energy levels and wave functions. The Breit interaction and Lamb shift contributions are calculated perturbatively as corrections to the Dirac-Fock energy. The M1 and E2 transitions between the first nine levels and the E1 transitions between excited and the ground levels are presented
Carpenter, Kenneth M; Jiang, Huiping; Sullivan, Maria A; Bisaga, Adam; Comer, Sandra D; Raby, Wilfrid Noel; Brooks, Adam C; Nunes, Edward V
2009-03-01
This study investigated the process of change by modeling transitions among four clinical states encountered in 64 detoxified opiate-dependent individuals treated with daily oral naltrexone: no opiate use, blocked opiate use (i.e., opiate use while adhering to oral naltrexone), unblocked opiate use (i.e., opiate use after having discontinued oral naltrexone), and treatment dropout. The effects of baseline characteristics and two psychosocial interventions of differing intensity, behavioral naltrexone therapy (BNT) and compliance enhancement (CE), on these transitions were studied. Participants using greater quantities of opiates were more likely than other participants to be retained in BNT relative to CE. Markov modeling indicated a transition from abstinence to treatment dropout was approximately 3.56 times greater among participants in CE relative to participants in BNT, indicating the more comprehensive psychosocial intervention kept participants engaged in treatment longer. Transitions to stopping treatment were more likely to occur after unblocked opiate use in both treatments. Continued opiate use while being blocked accounted for a relatively low proportion of transitions to abstinence and may have more deleterious effects later in a treatment episode. (PsycINFO Database Record (c) 2009 APA, all rights reserved).
Jamaluddin, Fadhilah; Rahim, Rahela Abdul
2015-12-01
Markov Chain has been introduced since the 1913 for the purpose of studying the flow of data for a consecutive number of years of the data and also forecasting. The important feature in Markov Chain is obtaining the accurate Transition Probability Matrix (TPM). However to obtain the suitable TPM is hard especially in involving long-term modeling due to unavailability of data. This paper aims to enhance the classical Markov Chain by introducing Exponential Smoothing technique in developing the appropriate TPM.
A transition-constrained discrete hidden Markov model for automatic sleep staging
Directory of Open Access Journals (Sweden)
Pan Shing-Tai
2012-08-01
Full Text Available Abstract Background Approximately one-third of the human lifespan is spent sleeping. To diagnose sleep problems, all-night polysomnographic (PSG recordings including electroencephalograms (EEGs, electrooculograms (EOGs and electromyograms (EMGs, are usually acquired from the patient and scored by a well-trained expert according to Rechtschaffen & Kales (R&K rules. Visual sleep scoring is a time-consuming and subjective process. Therefore, the development of an automatic sleep scoring method is desirable. Method The EEG, EOG and EMG signals from twenty subjects were measured. In addition to selecting sleep characteristics based on the 1968 R&K rules, features utilized in other research were collected. Thirteen features were utilized including temporal and spectrum analyses of the EEG, EOG and EMG signals, and a total of 158 hours of sleep data were recorded. Ten subjects were used to train the Discrete Hidden Markov Model (DHMM, and the remaining ten were tested by the trained DHMM for recognition. Furthermore, the 2-fold cross validation was performed during this experiment. Results Overall agreement between the expert and the results presented is 85.29%. With the exception of S1, the sensitivities of each stage were more than 81%. The most accurate stage was SWS (94.9%, and the least-accurately classified stage was S1 ( Conclusion The results of the experiments demonstrate that the proposed method significantly enhances the recognition rate when compared with prior studies.
International Nuclear Information System (INIS)
Kaschek, K.; Ernst, G.K.; Boetticher, W.
1984-01-01
Absolute transition probabilities of nine 5s-5p transitions of Kr I have been evaluated by using the hook method. The plasma was produced in a shock tube. The population density of the 5s-levels was calculated, under the assumption of LTE, from the electron density and the ground state number measured by means of a dual wavelength interferometer. An evaluation is given which proves the validity of the LTE assumption. (orig.)
International Nuclear Information System (INIS)
Savane, Y.Sy.
1996-11-01
The structure of the low-lying states in the even-even semi-magic nuclei ( 106-114 50 Sn) and the reduced transition probabilities B(E2, 6 + 1 → 4 = 1 ) for E2-transition have been investigated in the frame of the quasiparticle-phonon nuclear model. The model wave function includes a quasiparticle + two phonons components. It is shown that the small values of the transitions are connected with the non collective structure of the states. The calculated values are in agreement with the observed property of decreasing of the transition with increasing of mass number. (author). 16 refs, 6 tabs
Consistent Estimation of Partition Markov Models
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Jesús E. García
2017-04-01
Full Text Available The Partition Markov Model characterizes the process by a partition L of the state space, where the elements in each part of L share the same transition probability to an arbitrary element in the alphabet. This model aims to answer the following questions: what is the minimal number of parameters needed to specify a Markov chain and how to estimate these parameters. In order to answer these questions, we build a consistent strategy for model selection which consist of: giving a size n realization of the process, finding a model within the Partition Markov class, with a minimal number of parts to represent the process law. From the strategy, we derive a measure that establishes a metric in the state space. In addition, we show that if the law of the process is Markovian, then, eventually, when n goes to infinity, L will be retrieved. We show an application to model internet navigation patterns.
Markov Chain Models for the Stochastic Modeling of Pitting Corrosion
Directory of Open Access Journals (Sweden)
A. Valor
2013-01-01
Full Text Available The stochastic nature of pitting corrosion of metallic structures has been widely recognized. It is assumed that this kind of deterioration retains no memory of the past, so only the current state of the damage influences its future development. This characteristic allows pitting corrosion to be categorized as a Markov process. In this paper, two different models of pitting corrosion, developed using Markov chains, are presented. Firstly, a continuous-time, nonhomogeneous linear growth (pure birth Markov process is used to model external pitting corrosion in underground pipelines. A closed-form solution of the system of Kolmogorov's forward equations is used to describe the transition probability function in a discrete pit depth space. The transition probability function is identified by correlating the stochastic pit depth mean with the empirical deterministic mean. In the second model, the distribution of maximum pit depths in a pitting experiment is successfully modeled after the combination of two stochastic processes: pit initiation and pit growth. Pit generation is modeled as a nonhomogeneous Poisson process, in which induction time is simulated as the realization of a Weibull process. Pit growth is simulated using a nonhomogeneous Markov process. An analytical solution of Kolmogorov's system of equations is also found for the transition probabilities from the first Markov state. Extreme value statistics is employed to find the distribution of maximum pit depths.
Measurements of transition probabilities in the range from vacuum ultraviolet to infrared
International Nuclear Information System (INIS)
Peraza Fernandez, M.C.
1992-01-01
In this memory we describe the design, testing and calibration of different spectrometers to measure transition probabilities from the vacuum ultraviolet to the infrared spectral region. For the infrared measurements we have designed and performed a phase sensitive detection system, using an InGaAs photodiode like detector. With this system we have determined the transition probabilities of infrared lines of KrI and XeI. For these lines we haven't found previous measurements. In the vacuum ultraviolet spectral region we have designed a 3 m normal incidence monochromator where we have installed an optical multichannel analyzer. We have tested its accurate working, obtaining the absorption spectrum of KrI. In the visible region we have obtained the emission spectrum of Al using different spectral: hallow-cathode lamp and Nd: YAG laser produced Al plasma. With these spectra we have determined different atomic parameters like transition probabilities and electron temperatures.(author). 83 refs
The deviation matrix of a continuous-time Markov chain
Coolen-Schrijner, P.; van Doorn, E.A.
2001-01-01
The deviation matrix of an ergodic, continuous-time Markov chain with transition probability matrix $P(.)$ and ergodic matrix $\\Pi$ is the matrix $D \\equiv \\int_0^{\\infty} (P(t)-\\Pi)dt$. We give conditions for $D$ to exist and discuss properties and a representation of $D$. The deviation matrix of a
The deviation matrix of a continuous-time Markov chain
Coolen-Schrijner, Pauline; van Doorn, Erik A.
2002-01-01
he deviation matrix of an ergodic, continuous-time Markov chain with transition probability matrix $P(.)$ and ergodic matrix $\\Pi$ is the matrix $D \\equiv \\int_0^{\\infty} (P(t)-\\Pi)dt$. We give conditions for $D$ to exist and discuss properties and a representation of $D$. The deviation matrix of a
Power plant reliability calculation with Markov chain models
International Nuclear Information System (INIS)
Senegacnik, A.; Tuma, M.
1998-01-01
In the paper power plant operation is modelled using continuous time Markov chains with discrete state space. The model is used to compute the power plant reliability and the importance and influence of individual states, as well as the transition probabilities between states. For comparison the model is fitted to data for coal and nuclear power plants recorded over several years. (orig.) [de
Embedding a State Space Model Into a Markov Decision Process
DEFF Research Database (Denmark)
Nielsen, Lars Relund; Jørgensen, Erik; Højsgaard, Søren
2011-01-01
In agriculture Markov decision processes (MDPs) with finite state and action space are often used to model sequential decision making over time. For instance, states in the process represent possible levels of traits of the animal and transition probabilities are based on biological models...
Absolute Transition Probabilities from the 453.1 keV Level in 183W
International Nuclear Information System (INIS)
Malmskog, S.G.
1966-10-01
The half life of the 453.1 keV level in 183 W has been measured by the delayed coincidence method to 18.4 ± 0.5 nsec. This determines twelve absolute M1 and E2 transition probabilities, out of which nine are K-forbidden. All transition probabilities are compared with the single particle estimate. The three K-allowed E2, ΔK = 2 transition rates to the 1/2 - (510) rotational band are furthermore compared with the Nilsson model. An attempt to give a quantitative explanation of the observed transition rates has been made by including the effects from admixtures into the single particle wave functions
Absolute Transition Probabilities from the 453.1 keV Level in {sup 183}W
Energy Technology Data Exchange (ETDEWEB)
Malmskog, S G
1966-10-15
The half life of the 453.1 keV level in {sup 183}W has been measured by the delayed coincidence method to 18.4 {+-} 0.5 nsec. This determines twelve absolute M1 and E2 transition probabilities, out of which nine are K-forbidden. All transition probabilities are compared with the single particle estimate. The three K-allowed E2, {delta}K = 2 transition rates to the 1/2{sup -} (510) rotational band are furthermore compared with the Nilsson model. An attempt to give a quantitative explanation of the observed transition rates has been made by including the effects from admixtures into the single particle wave functions.
Directory of Open Access Journals (Sweden)
Nandi Soumyadeep
2007-03-01
Full Text Available Abstract Background Profile Hidden Markov Models (HMM are statistical representations of protein families derived from patterns of sequence conservation in multiple alignments and have been used in identifying remote homologues with considerable success. These conservation patterns arise from fold specific signals, shared across multiple families, and function specific signals unique to the families. The availability of sequences pre-classified according to their function permits the use of negative training sequences to improve the specificity of the HMM, both by optimizing the threshold cutoff and by modifying emission probabilities to minimize the influence of fold-specific signals. A protocol to generate family specific HMMs is described that first constructs a profile HMM from an alignment of the family's sequences and then uses this model to identify sequences belonging to other classes that score above the default threshold (false positives. Ten-fold cross validation is used to optimise the discrimination threshold score for the model. The advent of fast multiple alignment methods enables the use of the profile alignments to align the true and false positive sequences, and the resulting alignments are used to modify the emission probabilities in the original model. Results The protocol, called HMM-ModE, was validated on a set of sequences belonging to six sub-families of the AGC family of kinases. These sequences have an average sequence similarity of 63% among the group though each sub-group has a different substrate specificity. The optimisation of discrimination threshold, by using negative sequences scored against the model improves specificity in test cases from an average of 21% to 98%. Further discrimination by the HMM after modifying model probabilities using negative training sequences is provided in a few cases, the average specificity rising to 99%. Similar improvements were obtained with a sample of G-Protein coupled receptors
Relativistic transition probabilities for F-like ions with 10 Z 49
International Nuclear Information System (INIS)
Santos, J.P.; Madruga, C.; Parente, F.; Indelicato, P.
2005-01-01
In the present work we have calculated several relativistic transition probabilities for the F-like ions with 10 Z 49, in the framework of the Multi-Configuration Dirac-Fock method, for applications on laserphysics and astrophysics. The lines considered correspond to transitions between levels of 2p 4 3s, 2p 4 3p and 2p 4 3d configurations. The spectral fine structure is taken into consideration and the results for individual lines are given
International Nuclear Information System (INIS)
Rajput, B.S.
1977-01-01
Using the reduced expansions of second quantized electromagnetic vector potential operator in terms of irreducible representations of Pioncare group in the interaction Hamiltonian, the exact matrix elements of interaction of electromagnetic field with a hydrogenic atom have been derived and the contributions of transitions for different combinations of angular momentum quantum numbers to the transition probabilities of various lines in Lyman-, Balmer-, and Paschen-series have been computed. (author)
International Nuclear Information System (INIS)
Gagne, J.M.; Mongeau, B.; Demers, Y.; Pianarosa, P.
1981-01-01
From collision-induced fluorescence spectroscopy measurements, we have determined the transition probability Aof the 5971-A transition in neutral uranium. Our value, A 5971 = (5.9 +- 1.8) x 10 5 sec -1 , is, within experimental error, in good agreement with the previous determination of Corliss, A 5971 = (7.3 +- 3.0) x 10 5 sec -1 [J. Res. Nat. Bur. Stand. Sect. A 80,1 (1976)
Markov chain modelling of pitting corrosion in underground pipelines
Energy Technology Data Exchange (ETDEWEB)
Caleyo, F. [Departamento de Ingenieri' a Metalurgica, ESIQIE, IPN, UPALM Edif. 7, Zacatenco, Mexico D. F. 07738 (Mexico)], E-mail: fcaleyo@gmail.com; Velazquez, J.C. [Departamento de Ingenieri' a Metalurgica, ESIQIE, IPN, UPALM Edif. 7, Zacatenco, Mexico D. F. 07738 (Mexico); Valor, A. [Facultad de Fisica, Universidad de La Habana, San Lazaro y L, Vedado, 10400 La Habana (Cuba); Hallen, J.M. [Departamento de Ingenieri' a Metalurgica, ESIQIE, IPN, UPALM Edif. 7, Zacatenco, Mexico D. F. 07738 (Mexico)
2009-09-15
A continuous-time, non-homogenous linear growth (pure birth) Markov process has been used to model external pitting corrosion in underground pipelines. The closed form solution of Kolmogorov's forward equations for this type of Markov process is used to describe the transition probability function in a discrete pit depth space. The identification of the transition probability function can be achieved by correlating the stochastic pit depth mean with the deterministic mean obtained experimentally. Monte-Carlo simulations previously reported have been used to predict the time evolution of the mean value of the pit depth distribution for different soil textural classes. The simulated distributions have been used to create an empirical Markov chain-based stochastic model for predicting the evolution of pitting corrosion depth and rate distributions from the observed properties of the soil. The proposed model has also been applied to pitting corrosion data from pipeline repeated in-line inspections and laboratory immersion experiments.
Markov chain modelling of pitting corrosion in underground pipelines
International Nuclear Information System (INIS)
Caleyo, F.; Velazquez, J.C.; Valor, A.; Hallen, J.M.
2009-01-01
A continuous-time, non-homogenous linear growth (pure birth) Markov process has been used to model external pitting corrosion in underground pipelines. The closed form solution of Kolmogorov's forward equations for this type of Markov process is used to describe the transition probability function in a discrete pit depth space. The identification of the transition probability function can be achieved by correlating the stochastic pit depth mean with the deterministic mean obtained experimentally. Monte-Carlo simulations previously reported have been used to predict the time evolution of the mean value of the pit depth distribution for different soil textural classes. The simulated distributions have been used to create an empirical Markov chain-based stochastic model for predicting the evolution of pitting corrosion depth and rate distributions from the observed properties of the soil. The proposed model has also been applied to pitting corrosion data from pipeline repeated in-line inspections and laboratory immersion experiments.
Markov Chain Modelling for Short-Term NDVI Time Series Forecasting
Directory of Open Access Journals (Sweden)
Stepčenko Artūrs
2016-12-01
Full Text Available In this paper, the NDVI time series forecasting model has been developed based on the use of discrete time, continuous state Markov chain of suitable order. The normalised difference vegetation index (NDVI is an indicator that describes the amount of chlorophyll (the green mass and shows the relative density and health of vegetation; therefore, it is an important variable for vegetation forecasting. A Markov chain is a stochastic process that consists of a state space. This stochastic process undergoes transitions from one state to another in the state space with some probabilities. A Markov chain forecast model is flexible in accommodating various forecast assumptions and structures. The present paper discusses the considerations and techniques in building a Markov chain forecast model at each step. Continuous state Markov chain model is analytically described. Finally, the application of the proposed Markov chain model is illustrated with reference to a set of NDVI time series data.
Sebastian, Tunny; Jeyaseelan, Visalakshi; Jeyaseelan, Lakshmanan; Anandan, Shalini; George, Sebastian; Bangdiwala, Shrikant I
2018-01-01
Hidden Markov models are stochastic models in which the observations are assumed to follow a mixture distribution, but the parameters of the components are governed by a Markov chain which is unobservable. The issues related to the estimation of Poisson-hidden Markov models in which the observations are coming from mixture of Poisson distributions and the parameters of the component Poisson distributions are governed by an m-state Markov chain with an unknown transition probability matrix are explained here. These methods were applied to the data on Vibrio cholerae counts reported every month for 11-year span at Christian Medical College, Vellore, India. Using Viterbi algorithm, the best estimate of the state sequence was obtained and hence the transition probability matrix. The mean passage time between the states were estimated. The 95% confidence interval for the mean passage time was estimated via Monte Carlo simulation. The three hidden states of the estimated Markov chain are labelled as 'Low', 'Moderate' and 'High' with the mean counts of 1.4, 6.6 and 20.2 and the estimated average duration of stay of 3, 3 and 4 months, respectively. Environmental risk factors were studied using Markov ordinal logistic regression analysis. No significant association was found between disease severity levels and climate components.
Kwasniok, Frank
2013-11-01
A time series analysis method for predicting the probability density of a dynamical system is proposed. A nonstationary parametric model of the probability density is estimated from data within a maximum likelihood framework and then extrapolated to forecast the future probability density and explore the system for critical transitions or tipping points. A full systematic account of parameter uncertainty is taken. The technique is generic, independent of the underlying dynamics of the system. The method is verified on simulated data and then applied to prediction of Arctic sea-ice extent.
Prognostics for Steam Generator Tube Rupture using Markov Chain model
International Nuclear Information System (INIS)
Kim, Gibeom; Heo, Gyunyoung; Kim, Hyeonmin
2016-01-01
This paper will describe the prognostics method for evaluating and forecasting the ageing effect and demonstrate the procedure of prognostics for the Steam Generator Tube Rupture (SGTR) accident. Authors will propose the data-driven method so called MCMC (Markov Chain Monte Carlo) which is preferred to the physical-model method in terms of flexibility and availability. Degradation data is represented as growth of burst probability over time. Markov chain model is performed based on transition probability of state. And the state must be discrete variable. Therefore, burst probability that is continuous variable have to be changed into discrete variable to apply Markov chain model to the degradation data. The Markov chain model which is one of prognostics methods was described and the pilot demonstration for a SGTR accident was performed as a case study. The Markov chain model is strong since it is possible to be performed without physical models as long as enough data are available. However, in the case of the discrete Markov chain used in this study, there must be loss of information while the given data is discretized and assigned to the finite number of states. In this process, original information might not be reflected on prediction sufficiently. This should be noted as the limitation of discrete models. Now we will be studying on other prognostics methods such as GPM (General Path Model) which is also data-driven method as well as the particle filer which belongs to physical-model method and conducting comparison analysis
Solan, Eilon; Vieille, Nicolas
2015-01-01
We study irreducible time-homogenous Markov chains with finite state space in discrete time. We obtain results on the sensitivity of the stationary distribution and other statistical quantities with respect to perturbations of the transition matrix. We define a new closeness relation between transition matrices, and use graph-theoretic techniques, in contrast with the matrix analysis techniques previously used.
Implicit Segmentation of a Stream of Syllables Based on Transitional Probabilities: An MEG Study
Teinonen, Tuomas; Huotilainen, Minna
2012-01-01
Statistical segmentation of continuous speech, i.e., the ability to utilise transitional probabilities between syllables in order to detect word boundaries, is reflected in the brain's auditory event-related potentials (ERPs). The N1 and N400 ERP components are typically enhanced for word onsets compared to random syllables during active…
E2 transition probabilities between Nilsson states in odd-A nuclei
International Nuclear Information System (INIS)
Krpic, D.K.; Savic, I.M.; Anicin, I.V.
1976-01-01
Presented here are the matrices needed for the calculation of E2 transition probabilities between all pairs of Nilsson states with ΔN = 0 and ΔK = 0, 1, 2. The needed coefficients of states are tabulated by Nilsson and by Davidson
Moers, C.; Meyer, A.S.; Janse, E.
2017-01-01
High-frequency units are usually processed faster than low-frequency units in language comprehension and language production. Frequency effects have been shown for words as well as word combinations. Word co-occurrence effects can be operationalized in terms of transitional probability (TP). TPs
Biedenharn transformation in the theory of H ion. Probabilities of radiative transitions
International Nuclear Information System (INIS)
Zapryagaev, S.A.
1987-01-01
The solution of the Dirac equation in the Coulomb field is investigated by means of an anti-unitary transformation, reducing the set of relativistic equations to a non-relativistic equation. The obtained solutions are used to calculate probabilities of radiational transitions between fine-structure and hyperfine-structure levels of the H ion with an arbitrary nuclear charge
Structure of states and reduced probabilities of electromagnetic transitions in 169Yb
International Nuclear Information System (INIS)
Bonch-Osmolovskaya, N.A.; Morozov, V.A.; Khudajberdyev, Eh.N.
1988-01-01
The effect of accounting the Pauli principle on the structure and energy of nonrotational states of 169 Yb deformed nucleus as well as on reduced probabilities of E2-transitions B(E2) is studied within the framework of the quasiparticle-phonon model (QPM). The amplitudes of states mixing due to Coriolis interaction and reduced probabilities of gamma transition within the framework of nonadiabatic rotation model are also calculated. The results are compared with calculations made within QPM with account of Coriolis interaction but excluding the Pauli principle in the wave state function. It is shown that to describe correctly both the level structure and reduced probabilities B(E2) it is necessary to include all types of interaction : quasiparticle interaction with phonons with account of the Pauli principle in the wave state functions and Coriolis interactions. Now no uniform theoretical approach exists
International Nuclear Information System (INIS)
Liang Jinling; Lam, James; Wang Zidong
2009-01-01
This Letter is concerned with the robust state estimation problem for uncertain time-delay Markovian jumping genetic regulatory networks (GRNs) with SUM logic, where the uncertainties enter into both the network parameters and the mode transition rate. The nonlinear functions describing the feedback regulation are assumed to satisfy the sector-like conditions. The main purpose of the problem addressed is to design a linear estimator to approximate the true concentrations of the mRNA and protein through available measurement outputs. By resorting to the Lyapunov functional method and some stochastic analysis tools, it is shown that if a set of linear matrix inequalities (LMIs) is feasible, the desired state estimator, that can ensure the estimation error dynamics to be globally robustly asymptotically stable in the mean square, exists. The obtained LMI conditions are dependent on both the lower and the upper bounds of the delays. An illustrative example is presented to demonstrate the feasibility of the proposed estimation schemes.
Diaby, Vakaramoko; Adunlin, Georges; Montero, Alberto J
2014-02-01
(Markov) that needs the parameterization of transition probabilities, and only has summary KM plots available.
International Nuclear Information System (INIS)
Jankowski, K.; Smentek-Mielczarek, L.
1981-01-01
Results of model studies of the impact of electron correlation on the forced electric dipole transition probabilities between states of the 4fsup(N) configuration are reported for the [ 3 P] 0 - [ 3 F] 4 , [ 3 H] 4 transitions in Pr 3+ : LaCl 3 and for [ 7 F] 0 - [ 5 D] 2 , [ 7 F] 1 - [ 5 D] 1 hypersensitive transitions in Eu 3+ : LaCl 3 . For the former system the correlation effects cause a modification of earlier results by 40-95 per cent, whereas for the latter the probability changes by as much as two orders of magnitude. The great changes found in the case of hypersensitive transitions suggest that electron correlation effects may belong to the most important factors determining the nature of these transitions. Several types of effective correlation operators are considered and their relative importance is discussed. The results indicate that intermediate configurations including g orbitals are very important for the description of correlation effects. (author)
Theoretical Study of Energy Levels and Transition Probabilities of Boron Atom
Tian Yi, Zhang; Neng Wu, Zheng
2009-08-01
Full Text PDF Though the electrons configuration for boron atom is simple and boron atom has long been of interest for many researchers, the theoretical studies for properties of BI are not systematic, there are only few results reported on energy levels of high excited states of boron, and transition measurements are generally restricted to transitions involving ground states and low excited states without considering fine structure effects, provided only multiplet results, values for transitions between high excited states are seldom performed. In this article, by using the scheme of the weakest bound electron potential model theory calculations for energy levels of five series are performed and with the same method we give the transition probabilities between excited states with considering fine structure effects. The comprehensive set of calculations attempted in this paper could be of some value to workers in the field because of the lack of published calculations for the BI systems. The perturbations coming from foreign perturbers are taken into account in studying the energy levels. Good agreement between our results and the accepted values taken from NIST has been obtained. We also reported some values of energy levels and transition probabilities not existing on the NIST data bases.
Energy Technology Data Exchange (ETDEWEB)
Saloman, Edward B. [Dakota Consulting, Inc., 1110 Bonifant Street, Suite 310, Silver Spring, MD 20910 (United States); Kramida, Alexander [National Institute of Standards and Technology, Gaithersburg, MD 20899 (United States)
2017-08-01
The energy levels, observed spectral lines, and transition probabilities of the neutral vanadium atom, V i, have been compiled. Also included are values for some forbidden lines that may be of interest to the astrophysical community. Experimental Landé g -factors and leading percentage compositions for the levels are included where available, as well as wavelengths calculated from the energy levels (Ritz wavelengths). Wavelengths are reported for 3985 transitions, and 549 energy levels are determined. The observed relative intensities normalized to a common scale are provided.
Markov Chain Ontology Analysis (MCOA).
Frost, H Robert; McCray, Alexa T
2012-02-03
Biomedical ontologies have become an increasingly critical lens through which researchers analyze the genomic, clinical and bibliographic data that fuels scientific research. Of particular relevance are methods, such as enrichment analysis, that quantify the importance of ontology classes relative to a collection of domain data. Current analytical techniques, however, remain limited in their ability to handle many important types of structural complexity encountered in real biological systems including class overlaps, continuously valued data, inter-instance relationships, non-hierarchical relationships between classes, semantic distance and sparse data. In this paper, we describe a methodology called Markov Chain Ontology Analysis (MCOA) and illustrate its use through a MCOA-based enrichment analysis application based on a generative model of gene activation. MCOA models the classes in an ontology, the instances from an associated dataset and all directional inter-class, class-to-instance and inter-instance relationships as a single finite ergodic Markov chain. The adjusted transition probability matrix for this Markov chain enables the calculation of eigenvector values that quantify the importance of each ontology class relative to other classes and the associated data set members. On both controlled Gene Ontology (GO) data sets created with Escherichia coli, Drosophila melanogaster and Homo sapiens annotations and real gene expression data extracted from the Gene Expression Omnibus (GEO), the MCOA enrichment analysis approach provides the best performance of comparable state-of-the-art methods. A methodology based on Markov chain models and network analytic metrics can help detect the relevant signal within large, highly interdependent and noisy data sets and, for applications such as enrichment analysis, has been shown to generate superior performance on both real and simulated data relative to existing state-of-the-art approaches.
Liu, Zhao; Zhu, Yunhong; Wu, Chenxue
2016-01-01
Spatial-temporal k-anonymity has become a mainstream approach among techniques for protection of users’ privacy in location-based services (LBS) applications, and has been applied to several variants such as LBS snapshot queries and continuous queries. Analyzing large-scale spatial-temporal anonymity sets may benefit several LBS applications. In this paper, we propose two location prediction methods based on transition probability matrices constructing from sequential rules for spatial-temporal k-anonymity dataset. First, we define single-step sequential rules mined from sequential spatial-temporal k-anonymity datasets generated from continuous LBS queries for multiple users. We then construct transition probability matrices from mined single-step sequential rules, and normalize the transition probabilities in the transition matrices. Next, we regard a mobility model for an LBS requester as a stationary stochastic process and compute the n-step transition probability matrices by raising the normalized transition probability matrices to the power n. Furthermore, we propose two location prediction methods: rough prediction and accurate prediction. The former achieves the probabilities of arriving at target locations along simple paths those include only current locations, target locations and transition steps. By iteratively combining the probabilities for simple paths with n steps and the probabilities for detailed paths with n-1 steps, the latter method calculates transition probabilities for detailed paths with n steps from current locations to target locations. Finally, we conduct extensive experiments, and correctness and flexibility of our proposed algorithm have been verified. PMID:27508502
The Time Course of the Probability of Transition Into and Out of REM Sleep
Bassi, Alejandro; Vivaldi, Ennio A.; Ocampo-Garcés, Adrián
2009-01-01
Study Objectives: A model of rapid eye movement (REM) sleep expression is proposed that assumes underlying regulatory mechanisms operating as inhomogenous Poisson processes, the overt results of which are the transitions into and out of REM sleep. Design: Based on spontaneously occurring REM sleep episodes (“Episode”) and intervals without REM sleep (“Interval”), 3 variables are defined and evaluated over discrete 15-second epochs using a nonlinear logistic regression method: “Propensity” is the instantaneous rate of into-REM transition occurrence throughout an Interval, “Volatility” is the instantaneous rate of out-of-REM transition occurrence throughout an Episode, and “Opportunity” is the probability of being in non-REM (NREM) sleep at a given time throughout an Interval, a requisite for transition. Setting: 12:12 light:dark cycle, isolated boxes. Participants: Sixteen male Sprague-Dawley rats Interventions: None. Spontaneous sleep cycles. Measurements and Results: The highest levels of volatility and propensity occur, respectively, at the very beginning of Episodes and Intervals. The new condition stabilizes rapidly, and variables reach nadirs at minute 1.25 and 2.50, respectively. Afterward, volatility increases markedly, reaching values close to the initial level. Propensity increases moderately, the increment being stronger through NREM sleep bouts occurring at the end of long Intervals. Short-term homeostasis is evidenced by longer REM sleep episodes lowering propensity in the following Interval. Conclusions: The stabilization after transitions into Episodes or Intervals and the destabilization after remaining for some time in either condition may be described as resulting from continuous processes building up during Episodes and Intervals. These processes underlie the overt occurrence of transitions. Citation: Bassi A; Vivaldi EA; Ocampo-Garcées A. The time course of the probability of transition into and out of REM sleep. SLEEP 2009
Transition probabilities of some Si II lines obtained by laser produced plasma emission
International Nuclear Information System (INIS)
Blanco, F.; Botho, B.; Campos, J.
1995-01-01
The absolute transition probabilities for 28 Si II spectral lines have been determined by measurement of emission line intensities from laser-produced plasmas of Si in Ar and Kr atmospheres. The studied plasma has a temperature of about 2 . 10 4 K and 10 17 cm -3 electron density. The local thermodynamic equilibrium conditions and plasma homogeneity have been checked. The results are compared with the available experimental and theoretical data and with present Hartree-Fock calculations in LS coupling. (orig.)
Hidden measurements, hidden variables and the volume representation of transition probabilities
Oliynyk, Todd A.
2005-01-01
We construct, for any finite dimension $n$, a new hidden measurement model for quantum mechanics based on representing quantum transition probabilities by the volume of regions in projective Hilbert space. For $n=2$ our model is equivalent to the Aerts sphere model and serves as a generalization of it for dimensions $n \\geq 3$. We also show how to construct a hidden variables scheme based on hidden measurements and we discuss how joint distributions arise in our hidden variables scheme and th...
Ho, Lam Si Tung; Xu, Jason; Crawford, Forrest W; Minin, Vladimir N; Suchard, Marc A
2018-03-01
Birth-death processes track the size of a univariate population, but many biological systems involve interaction between populations, necessitating models for two or more populations simultaneously. A lack of efficient methods for evaluating finite-time transition probabilities of bivariate processes, however, has restricted statistical inference in these models. Researchers rely on computationally expensive methods such as matrix exponentiation or Monte Carlo approximation, restricting likelihood-based inference to small systems, or indirect methods such as approximate Bayesian computation. In this paper, we introduce the birth/birth-death process, a tractable bivariate extension of the birth-death process, where rates are allowed to be nonlinear. We develop an efficient algorithm to calculate its transition probabilities using a continued fraction representation of their Laplace transforms. Next, we identify several exemplary models arising in molecular epidemiology, macro-parasite evolution, and infectious disease modeling that fall within this class, and demonstrate advantages of our proposed method over existing approaches to inference in these models. Notably, the ubiquitous stochastic susceptible-infectious-removed (SIR) model falls within this class, and we emphasize that computable transition probabilities newly enable direct inference of parameters in the SIR model. We also propose a very fast method for approximating the transition probabilities under the SIR model via a novel branching process simplification, and compare it to the continued fraction representation method with application to the 17th century plague in Eyam. Although the two methods produce similar maximum a posteriori estimates, the branching process approximation fails to capture the correlation structure in the joint posterior distribution.
Energy Technology Data Exchange (ETDEWEB)
Pal' chikov, V.G. [National Research Institute for Physical-Technical and Radiotechnical Measurements - VNIIFTRI (Russian Federation)], E-mail: vitpal@mail.ru
2000-08-15
A quantum-electrodynamical (QED) perturbation theory is developed for hydrogen and hydrogen-like atomic systems with interaction between bound electrons and radiative field being treated as the perturbation. The dependence of the perturbed energy of levels on hyperfine structure (hfs) effects and on the higher-order Stark effect is investigated. Numerical results have been obtained for the transition probability between the hfs components of hydrogen-like bismuth.
Berlow, Noah; Pal, Ranadip
2011-01-01
Genetic Regulatory Networks (GRNs) are frequently modeled as Markov Chains providing the transition probabilities of moving from one state of the network to another. The inverse problem of inference of the Markov Chain from noisy and limited experimental data is an ill posed problem and often generates multiple model possibilities instead of a unique one. In this article, we address the issue of intervention in a genetic regulatory network represented by a family of Markov Chains. The purpose of intervention is to alter the steady state probability distribution of the GRN as the steady states are considered to be representative of the phenotypes. We consider robust stationary control policies with best expected behavior. The extreme computational complexity involved in search of robust stationary control policies is mitigated by using a sequential approach to control policy generation and utilizing computationally efficient techniques for updating the stationary probability distribution of a Markov chain following a rank one perturbation.
Markov Chains and Markov Processes
Ogunbayo, Segun
2016-01-01
Markov chain, which was named after Andrew Markov is a mathematical system that transfers a state to another state. Many real world systems contain uncertainty. This study helps us to understand the basic idea of a Markov chain and how is been useful in our daily lives. For some times there had been suspense on distinct predictions and future existences. Also in different games there had been different expectations or results involved. That is the reason why we need Markov chains to predict o...
E2 and M1 Transition Probabilities in Odd Mass Hg Nuclei
Energy Technology Data Exchange (ETDEWEB)
Berg, V; Baecklin, A; Fogelberg, B; Malmskog, S G
1969-10-15
L- and M-subshell ratios have been measured for the 39.5 keV transition in {sup 193}Hg and the 37.1 and 16.2 keV transitions in {sup 195}Hg yielding 0.38 {+-} 0.12 , <0.02 and 0.08 {+-} 0.03 per cent E2, respectively. The half-lives of the 39.5 keV level in {sup 193}Hg and the 53.3 and 37.1 keV levels in {sup 195}Hg have been measured by the delayed coincidence method, yielding values of 0.63 {+-} 0.03, 0.72 {+-} 0.03 and <0.05 nsec respectively. A systematic compilation of reduced E2 and M1 transition probabilities in odd mass Pt, Hg and Pb nuclei is given and compared to theoretical predictions.
Fine-structure energy levels, oscillator strengths and transition probabilities in Ni XVI
International Nuclear Information System (INIS)
Deb, N.C.; Msezane, A.Z.
2001-01-01
Fine-structure energy levels relative to the ground state, oscillator strengths and transition probabilities for transitions among the lowest 40 fine-structure levels belonging to the configurations 3s 2 3p, 3s3p 2 , 3s 2 3d, 3p 3 and 3s3p3d of Ni XVI are calculated using a large scale CI in program CIV3 of Hibbert. Relativistic effects are included through the Breit-Pauli approximation via spin-orbit, spin-other-orbit, spin-spin, Darwin and mass correction terms. The existing discrepancies between the calculated and measured values for many of the relative energy positions are resolved in the present calculation which yields excellent agreement with measurement. Also, many of our oscillator strengths for allowed and intercombination transitions are in very good agreement with the recommended data by the National Institute of Standard and Technology (NIST). (orig.)
Nitz, D. E.; Curry, J. J.; Buuck, M.; DeMann, A.; Mitchell, N.; Shull, W.
2018-02-01
We report radiative transition probabilities for 5029 emission lines of neutral cerium within the wavelength range 417-1110 nm. Transition probabilities for only 4% of these lines have been previously measured. These results are obtained from a Boltzmann analysis of two high resolution Fourier transform emission spectra used in previous studies of cerium, obtained from the digital archives of the National Solar Observatory at Kitt Peak. The set of transition probabilities used for the Boltzmann analysis are those published by Lawler et al (2010 J. Phys. B: At. Mol. Opt. Phys. 43 085701). Comparisons of branching ratios and transition probabilities for lines common to the two spectra provide important self-consistency checks and test for the presence of self-absorption effects. Estimated 1σ uncertainties for our transition probability results range from 10% to 18%.
Dynamic modeling of presence of occupants using inhomogeneous Markov chains
DEFF Research Database (Denmark)
Andersen, Philip Hvidthøft Delff; Iversen, Anne; Madsen, Henrik
2014-01-01
on time of day, and by use of a filter of the observations it is able to capture per-employee sequence dynamics. Simulations using this method are compared with simulations using homogeneous Markov chains and show far better ability to reproduce key properties of the data. The method is based...... on inhomogeneous Markov chains with where the transition probabilities are estimated using generalized linear models with polynomials, B-splines, and a filter of passed observations as inputs. For treating the dispersion of the data series, a hierarchical model structure is used where one model is for low presence...
On a Markov chain roulette-type game
International Nuclear Information System (INIS)
El-Shehawey, M A; El-Shreef, Gh A
2009-01-01
A Markov chain on non-negative integers which arises in a roulette-type game is discussed. The transition probabilities are p 01 = ρ, p Nj = δ Nj , p i,i+W = q, p i,i-1 = p = 1 - q, 1 ≤ W < N, 0 ≤ ρ ≤ 1, N - W < j ≤ N and i = 1, 2, ..., N - W. Using formulae for the determinant of a partitioned matrix, a closed form expression for the solution of the Markov chain roulette-type game is deduced. The present analysis is supported by two mathematical models from tumor growth and war with bargaining
Markov Chain Monte Carlo Methods
Indian Academy of Sciences (India)
Systat Software Asia-Pacific. Ltd., in Bangalore, where the technical work for the development of the statistical software Systat takes ... In Part 4, we discuss some applications of the Markov ... one can construct the joint probability distribution of.
Calculating Absolute Transition Probabilities for Deformed Nuclei in the Rare-Earth Region
Stratman, Anne; Casarella, Clark; Aprahamian, Ani
2017-09-01
Absolute transition probabilities are the cornerstone of understanding nuclear structure physics in comparison to nuclear models. We have developed a code to calculate absolute transition probabilities from measured lifetimes, using a Python script and a Mathematica notebook. Both of these methods take pertinent quantities such as the lifetime of a given state, the energy and intensity of the emitted gamma ray, and the multipolarities of the transitions to calculate the appropriate B(E1), B(E2), B(M1) or in general, any B(σλ) values. The program allows for the inclusion of mixing ratios of different multipolarities and the electron conversion of gamma-rays to correct for their intensities, and yields results in absolute units or results normalized to Weisskopf units. The code has been tested against available data in a wide range of nuclei from the rare earth region (28 in total), including 146-154Sm, 154-160Gd, 158-164Dy, 162-170Er, 168-176Yb, and 174-182Hf. It will be available from the Notre Dame Nuclear Science Laboratory webpage for use by the community. This work was supported by the University of Notre Dame College of Science, and by the National Science Foundation, under Contract PHY-1419765.
Energy Technology Data Exchange (ETDEWEB)
Saloman, Edward B. [Dakota Consulting, Inc., 1110 Bonifant Street, Suite 310, Silver Spring, MD 20910 (United States); Kramida, Alexander [National Institute of Standards and Technology, Gaithersburg, MD 20899 (United States)
2017-08-01
The energy levels, observed spectral lines, and transition probabilities of singly ionized vanadium, V ii, have been compiled. The experimentally derived energy levels belong to the configurations 3 d {sup 4}, 3 d {sup 3} ns ( n = 4, 5, 6), 3 d {sup 3} np , and 3 d {sup 3} nd ( n = 4, 5), 3 d {sup 3}4 f , 3 d {sup 2}4 s {sup 2}, and 3 d {sup 2}4 s 4 p . Also included are values for some forbidden lines that may be of interest to the astrophysical community. Experimental Landé g -factors and leading percentages for the levels are included when available, as well as Ritz wavelengths calculated from the energy levels. Wavelengths and transition probabilities are reported for 3568 and 1896 transitions, respectively. From the list of observed wavelengths, 407 energy levels are determined. The observed intensities, normalized to a common scale, are provided. From the newly optimized energy levels, a revised value for the ionization energy is derived, 118,030(60) cm{sup −1}, corresponding to 14.634(7) eV. This is 130 cm{sup −1} higher than the previously recommended value from Iglesias et al.
International Nuclear Information System (INIS)
Yokoyama, Keiichi; Sugita, Akihiro; Yamada, Hidetaka; Teranishi, Yoshiaki; Yokoyama, Atsushi
2007-01-01
A preparatory study on the quantum control of the selective transition K(4S 1/2 ) → K(4P J ) (J=1/2, 3/2) in intense laser field is reported. To generate high average power femtosecond laser pulses with enough field intensity, a Ti:Sapphire regenerative amplifier system with a repetition rate of 1 kHz is constructed. The bandwidth and pulse energy are shown to qualify the required values for the completely selective transition with 100% population inversion. A preliminary experiment of the selective excitation shows that the fringe pattern formed by a phase related pulse pair depends on the laser intensity, indicating that the perturbative behavior of the excitation probabilities is not valid any more and the laser intensity reaches a non-perturbative region. (author)
Markov chain model for demersal fish catch analysis in Indonesia
Firdaniza; Gusriani, N.
2018-03-01
As an archipelagic country, Indonesia has considerable potential fishery resources. One of the fish resources that has high economic value is demersal fish. Demersal fish is a fish with a habitat in the muddy seabed. Demersal fish scattered throughout the Indonesian seas. Demersal fish production in each Indonesia’s Fisheries Management Area (FMA) varies each year. In this paper we have discussed the Markov chain model for demersal fish yield analysis throughout all Indonesia’s Fisheries Management Area. Data of demersal fish catch in every FMA in 2005-2014 was obtained from Directorate of Capture Fisheries. From this data a transition probability matrix is determined by the number of transitions from the catch that lie below the median or above the median. The Markov chain model of demersal fish catch data was an ergodic Markov chain model, so that the limiting probability of the Markov chain model can be determined. The predictive value of demersal fishing yields was obtained by calculating the combination of limiting probability with average catch results below the median and above the median. The results showed that for 2018 and long-term demersal fishing results in most of FMA were below the median value.
Energies, wavelengths, and transition probabilities for Ge-like Kr, Mo, Sn, and Xe ions
International Nuclear Information System (INIS)
Nagy, O.; El Sayed, Fatma
2012-01-01
Energy levels, wavelengths, transition probabilities, and oscillator strengths have been calculated for Ge-like Kr, Mo, Sn, and Xe ions among the fine-structure levels of terms belonging to the ([Ar] 3d 10 )4s 2 4p 2 , ([Ar] 3d 10 )4s 4p 3 , ([Ar] 3d 10 )4s 2 4p 4d, and ([Ar] 3d 10 )4p 4 configurations. The fully relativistic multiconfiguration Dirac–Fock method, taking both correlations within the n=4 complex and the quantum electrodynamic effects into account, have been used in the calculations. The results are compared with the available experimental and other theoretical results.
Transition probabilities and dissociation energies of MnH and MnD molecules
International Nuclear Information System (INIS)
Nagarajan, K.; Rajamanickam, N.
1997-01-01
The Frank-Condon factors (vibrational transition probabilities) and r-centroids have been evaluated by the more reliable numerical integration procedure for the bands of A-X system of MnH and MnD molecules, using a suitable potential. By fitting the Hulburt- Hirschfelder function to the experimental potential curve using correlation coefficient, the dissociation energy for the electronic ground states of MnH and MnD molecules, respectively have been estimated as D 0 0 =251±5 KJ.mol -1 and D 0 0 =312±6 KJ.mol -1 . (authors)
International Nuclear Information System (INIS)
Martinson, I.
1982-01-01
A review is given of recent studies of atomic structure (in particular atomic spectra, energy levels and transition probabilities) using fast beams from ion accelerators. Thanks to improved spectral resolution detailed and quite accurate studies of energy levels are now possible, a number of such results will be discussed. The non-autoionizing, multiply excited levels in atoms and ions (including negative ions) are being vigorously investigated at present, some new results will be reported. The accuracy in lifetime determinations continues to improve, and several new ways for reduction of cascading effects have been developed. Some selected examples of recent progress in lifetime measurements are also included. (orig.)
Transition Probabilities in the 1/2+(631) Band in {sup 235}U
Energy Technology Data Exchange (ETDEWEB)
Hoejeberg, M; Malmskog, S G
1969-09-15
Measurements of absolute transition probabilities in the rotational band built on the 1/2{sup +}(631) single particle state in {sup 235}U have been performed using delayed coincidence technique. The following half-lives were obtained: T{sub 1/2} (13.0 keV level) = (0.50 {+-} 0.03) nsec. T{sub 1/2} (51.7 k e V level) = (0.20 {+-} 0.02) nsec. From the deduced B(E2) and B(M1) values magnetic and electric parameters were determined which could be compared with predictions from the Nilsson model.
Use of Markov chains for forecasting labor requirements in black coal mines
Energy Technology Data Exchange (ETDEWEB)
Penar, L.; Przybyla, H.
1987-01-01
Increasing mining depth, deterioration of mining conditions and technology development are causes of changes in labor requirements. In mines with stable coal output these changes in most cases are of a qualitative character, in mines with an increasing or decreasing coal output they are of a quantitative character. Methods for forecasting personnel needs, in particular professional requirements, are discussed. Quantitative and qualitative changes are accurately described by heterogenous Markov chains. A structure consisting of interdependent variables is the subject of a forecast. Changes that occur within the structure of time units is the subject of investigations. For a homogenous Markov chain probabilities of a transition from the i-state to the j-state are determined (the probabilities being time independent). For a heterogenous Markov chain probabilities of a transition from the i-state to the j-state are non-conditioned. The method was developed for the ODRA 1325 computers. 8 refs.
Stochastic Dynamics through Hierarchically Embedded Markov Chains.
Vasconcelos, Vítor V; Santos, Fernando P; Santos, Francisco C; Pacheco, Jorge M
2017-02-03
Studying dynamical phenomena in finite populations often involves Markov processes of significant mathematical and/or computational complexity, which rapidly becomes prohibitive with increasing population size or an increasing number of individual configuration states. Here, we develop a framework that allows us to define a hierarchy of approximations to the stationary distribution of general systems that can be described as discrete Markov processes with time invariant transition probabilities and (possibly) a large number of states. This results in an efficient method for studying social and biological communities in the presence of stochastic effects-such as mutations in evolutionary dynamics and a random exploration of choices in social systems-including situations where the dynamics encompasses the existence of stable polymorphic configurations, thus overcoming the limitations of existing methods. The present formalism is shown to be general in scope, widely applicable, and of relevance to a variety of interdisciplinary problems.
International Nuclear Information System (INIS)
Kastner, S.O.
1980-01-01
Closed expressions are obtained for the conditional probabilities qsub(i)sub(j)sub(,)sub(k) required in evaluating particular ratios of atomic level populations, using a Markov-chain representation of the system of levels. The total transition probability between two arbitrary levels is also evaluated and its relation to population ratios is clarified. It is shown that Seaton's cascade matrix is a subset of the total transition probability matrix. (orig.)
2016-03-24
current USD) 179 179 164 1990 0 Improved Water Source (% population with access ) 178 175 175 1960 1 Life Expectancy (years) 182 182 1990 2 Military...Microsoft Office visual basic ( VBA ) based consolidated 42 database tool was developed to compile the 39 separate identifying-information and data...conflict remains in conflictP A brief discussion of the accessibility of states within a Markov model is warranted before we proceed further
Evolving the structure of hidden Markov Models
DEFF Research Database (Denmark)
won, K. J.; Prugel-Bennett, A.; Krogh, A.
2006-01-01
A genetic algorithm (GA) is proposed for finding the structure of hidden Markov Models (HMMs) used for biological sequence analysis. The GA is designed to preserve biologically meaningful building blocks. The search through the space of HMM structures is combined with optimization of the emission...... and transition probabilities using the classic Baum-Welch algorithm. The system is tested on the problem of finding the promoter and coding region of C. jejuni. The resulting HMM has a superior discrimination ability to a handcrafted model that has been published in the literature....
Vulnerability of networks of interacting Markov chains.
Kocarev, L; Zlatanov, N; Trajanov, D
2010-05-13
The concept of vulnerability is introduced for a model of random, dynamical interactions on networks. In this model, known as the influence model, the nodes are arranged in an arbitrary network, while the evolution of the status at a node is according to an internal Markov chain, but with transition probabilities that depend not only on the current status of that node but also on the statuses of the neighbouring nodes. Vulnerability is treated analytically and numerically for several networks with different topological structures, as well as for two real networks--the network of infrastructures and the EU power grid--identifying the most vulnerable nodes of these networks.
Absolute M1 and E2 Transition Probabilities in 233U
International Nuclear Information System (INIS)
Malmskog, S.G.; Hoejeberg, M.
1967-08-01
Using the delayed coincidence technique, the following half lives have been determined for different excited states in 233 U: T 1/2 (311.9 keV level) = (1.20 ± 0.15) x 10 -10 sec, T 1/2 (340.5 keV level) = (5.2 ± 1.0) x 10 -11 sec, T 1/2 (398.6 keV level) = (5.5 ± 2.0) x 10 -11 sec and T 1/2 (415.8 keV level) -11 sec. From these half life determinations, together with earlier known electron intensities and conversion coefficients, 22 reduced B(Ml) and B(E2) transition probabilities (including 9 limits) have been deduced. The rotational transitions give information on the parameters δ and (g K - g R ) . The experimental M1 and E2 transition rates between members of different bands have been analysed in terms of the predictions of the Nilsson model, taking also pairing correlations and Coriolis coupling effects into account
Absolute M1 and E2 Transition Probabilities in 2{sup 33}U
Energy Technology Data Exchange (ETDEWEB)
Malmskog, S G; Hoejeberg, M
1967-08-15
Using the delayed coincidence technique, the following half lives have been determined for different excited states in {sup 233}U: T{sub 1/2} (311.9 keV level) = (1.20 {+-} 0.15) x 10{sup -10} sec, T{sub 1/2} (340.5 keV level) = (5.2 {+-} 1.0) x 10{sup -11} sec, T{sub 1/2} (398.6 keV level) = (5.5 {+-} 2.0) x 10{sup -11} sec and T{sub 1/2} (415.8 keV level) < 3 x 10{sup -11}sec. From these half life determinations, together with earlier known electron intensities and conversion coefficients, 22 reduced B(Ml) and B(E2) transition probabilities (including 9 limits) have been deduced. The rotational transitions give information on the parameters {delta} and (g{sub K} - g{sub R}) . The experimental M1 and E2 transition rates between members of different bands have been analysed in terms of the predictions of the Nilsson model, taking also pairing correlations and Coriolis coupling effects into account.
Geometric allocation approaches in Markov chain Monte Carlo
International Nuclear Information System (INIS)
Todo, S; Suwa, H
2013-01-01
The Markov chain Monte Carlo method is a versatile tool in statistical physics to evaluate multi-dimensional integrals numerically. For the method to work effectively, we must consider the following key issues: the choice of ensemble, the selection of candidate states, the optimization of transition kernel, algorithm for choosing a configuration according to the transition probabilities. We show that the unconventional approaches based on the geometric allocation of probabilities or weights can improve the dynamics and scaling of the Monte Carlo simulation in several aspects. Particularly, the approach using the irreversible kernel can reduce or sometimes completely eliminate the rejection of trial move in the Markov chain. We also discuss how the space-time interchange technique together with Walker's method of aliases can reduce the computational time especially for the case where the number of candidates is large, such as models with long-range interactions
Directory of Open Access Journals (Sweden)
Dan Ye
2013-01-01
Full Text Available This paper is concerned with delay-dependent stochastic stability for time-delay Markovian jump systems (MJSs with sector-bounded nonlinearities and more general transition probabilities. Different from the previous results where the transition probability matrix is completely known, a more general transition probability matrix is considered which includes completely known elements, boundary known elements, and completely unknown ones. In order to get less conservative criterion, the state and transition probability information is used as much as possible to construct the Lyapunov-Krasovskii functional and deal with stability analysis. The delay-dependent sufficient conditions are derived in terms of linear matrix inequalities to guarantee the stability of systems. Finally, numerical examples are exploited to demonstrate the effectiveness of the proposed method.
Energy Technology Data Exchange (ETDEWEB)
Korhonen, Marko [Department of Mathematics and Statistics, University of Helsinki, FIN-00014 (Finland); Lee, Eunghyun [Centre de Recherches Mathématiques (CRM), Université de Montréal, Quebec H3C 3J7 (Canada)
2014-01-15
We treat the N-particle zero range process whose jumping rates satisfy a certain condition. This condition is required to use the Bethe ansatz and the resulting model is the q-boson model by Sasamoto and Wadati [“Exact results for one-dimensional totally asymmetric diffusion models,” J. Phys. A 31, 6057–6071 (1998)] or the q-totally asymmetric zero range process (TAZRP) by Borodin and Corwin [“Macdonald processes,” Probab. Theory Relat. Fields (to be published)]. We find the explicit formula of the transition probability of the q-TAZRP via the Bethe ansatz. By using the transition probability we find the probability distribution of the left-most particle's position at time t. To find the probability for the left-most particle's position we find a new identity corresponding to identity for the asymmetric simple exclusion process by Tracy and Widom [“Integral formulas for the asymmetric simple exclusion process,” Commun. Math. Phys. 279, 815–844 (2008)]. For the initial state that all particles occupy a single site, the probability distribution of the left-most particle's position at time t is represented by the contour integral of a determinant.
Hidden Markov Model Application to Transfer The Trader Online Forex Brokers
Directory of Open Access Journals (Sweden)
Farida Suharleni
2012-05-01
Full Text Available Hidden Markov Model is elaboration of Markov chain, which is applicable to cases that can’t directly observe. In this research, Hidden Markov Model is used to know trader’s transition to broker forex online. In Hidden Markov Model, observed state is observable part and hidden state is hidden part. Hidden Markov Model allows modeling system that contains interrelated observed state and hidden state. As observed state in trader’s transition to broker forex online is category 1, category 2, category 3, category 4, category 5 by condition of every broker forex online, whereas as hidden state is broker forex online Marketiva, Masterforex, Instaforex, FBS and Others. First step on application of Hidden Markov Model in this research is making construction model by making a probability of transition matrix (A from every broker forex online. Next step is making a probability of observation matrix (B by making conditional probability of five categories, that is category 1, category 2, category 3, category 4, category 5 by condition of every broker forex online and also need to determine an initial state probability (π from every broker forex online. The last step is using Viterbi algorithm to find hidden state sequences that is broker forex online sequences which is the most possible based on model and observed state that is the five categories. Application of Hidden Markov Model is done by making program with Viterbi algorithm using Delphi 7.0 software with observed state based on simulation data. Example: By the number of observation T = 5 and observed state sequences O = (2,4,3,5,1 is found hidden state sequences which the most possible with observed state O as following : where X1 = FBS, X2 = Masterforex, X3 = Marketiva, X4 = Others, and X5 = Instaforex.
Estimation with Right-Censored Observations Under A Semi-Markov Model.
Zhao, Lihui; Hu, X Joan
2013-06-01
The semi-Markov process often provides a better framework than the classical Markov process for the analysis of events with multiple states. The purpose of this paper is twofold. First, we show that in the presence of right censoring, when the right end-point of the support of the censoring time is strictly less than the right end-point of the support of the semi-Markov kernel, the transition probability of the semi-Markov process is nonidentifiable, and the estimators proposed in the literature are inconsistent in general. We derive the set of all attainable values for the transition probability based on the censored data, and we propose a nonparametric inference procedure for the transition probability using this set. Second, the conventional approach to constructing confidence bands is not applicable for the semi-Markov kernel and the sojourn time distribution. We propose new perturbation resampling methods to construct these confidence bands. Different weights and transformations are explored in the construction. We use simulation to examine our proposals and illustrate them with hospitalization data from a recent cancer survivor study.
Low-spin electromagnetic transition probabilities in {sup 102,104}Cd
Energy Technology Data Exchange (ETDEWEB)
Jolie, J.; Dewald, A.; Fransen, C.; Linnemann, A.; Melon, B.; Moeller, O. [Inst. fuer Kernphysik, Univ. zu Koeln (Germany); Boelaert, N. [Inst. fuer Kernphysik, Univ. zu Koeln (Germany); Dept. of Subatomic and Radiation Physics, Gent Univ. (Belgium); Smirnova, N.; Heyde, K. [Dept. of Subatomic and Radiation Physics, Gent Univ. (Belgium)
2007-07-01
Lifetimes of low-lying states in {sup 102,104}Cd were determined by using the recoil distance Doppler shift technique with a plunger device and a Ge array consisting of five HP Ge detectors and one Euroball cluster detector. The experiments were carried out at the Cologne FN Tandem accelerator using the {sup 92,94}Mo({sup 12}C,2n){sup 102,104}Cd reactions. The differential decay curve method in coincidence mode was employed to derive the lifetime of the first excited 2{sup +} state in both nuclei and the first excited 4{sup +} state in {sup 104}Cd. The corresponding E2 transition probabilities agree well with large scale shell-model calculations. (orig.)
Measurement of transition probabilities in Kr II UV and visible spectral lines
International Nuclear Information System (INIS)
Mar, S; Val, J A del; RodrIguez, F; Pelaez, R J; Gonzalez, V R; Gonzalo, A B; Castro, A de; Aparicio, J A
2006-01-01
This work reports an extensive collection of 120 atomic transition probabilities of Kr II lines in the spectral region 350-720 nm, all of them measured in an emission experiment. For many of them, these are the first data up to the authors' knowledge. Relative intensity measurements have been obtained on a pulsed discharge lamp and the absolute A ki -values have been calculated by considering the available data from the literature as reference for the plasma temperature diagnosis. Excitation temperature (14 000-28 000 K) has been determined by using the Boltzmann-plot method. The plasma electron density (0.2-0.8 x 10 23 m -3 ) has been determined by two-wavelength interferometry. This work extends a previous one already published by our laboratory [1, 2]. Comparisons have also been made with previous literature values
Energies and transition probabilities from the full solution of nuclear quadrupole-octupole model
International Nuclear Information System (INIS)
Strecker, M.; Lenske, H.; Minkov, N.
2013-01-01
A collective model of nuclear quadrupole-octupole vibrations and rotations, originally restricted to a coherent interplay between quadrupole and octupole modes, is now developed for application beyond this restriction. The eigenvalue problem is solved by diagonalizing the unrestricted Hamiltonian in the basis of the analytic solution obtained in the case of the coherent-mode assumption. Within this scheme the yrast alternating-parity band is constructed by the lowest eigenvalues having the appropriate parity at given angular momentum. Additionally we include the calculation of transition probabilities which are fitted with the energies simultaneously. As a result we obtain a unique set of parameters. The obtained model parameters unambiguously determine the shape of the quadrupole-octupole potential. From the resulting wave functions quadrupole deformation expectation values are calculated which are found to be in agreement with experimental values. (author)
Application of the Markov chain approximation to the sunspot observations
International Nuclear Information System (INIS)
Onal, M.
1988-01-01
The positions of the 13,588 sunspot groups observed during the cycle of 1950-1960 at the Istanbul University Observatory have been corrected for the effect of differential rotation. The evolution probability of a sunspot group to the other one in the same region have been determined. By using the Markov chain approximation, the types of these groups and their transition probabilities during the following activity cycle (1950-1960), and the concentration of active regions during 1950-1960 have been estimated. The transition probabilities from the observations of the activity cycle 1960-1970 have been compared with the predicted transition probabilities and a good correlation has been noted. 5 refs.; 2 tabs
Improving Markov Chain Models for Road Profiles Simulation via Definition of States
2012-04-01
wavelet transform in pavement profile analysis," Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility, vol. 47, no. 4...34Estimating Markov Transition Probabilities from Micro -Unit Data," Journal of the Royal Statistical Society. Series C (Applied Statistics), pp. 355-371
Constructing Dynamic Event Trees from Markov Models
International Nuclear Information System (INIS)
Paolo Bucci; Jason Kirschenbaum; Tunc Aldemir; Curtis Smith; Ted Wood
2006-01-01
In the probabilistic risk assessment (PRA) of process plants, Markov models can be used to model accurately the complex dynamic interactions between plant physical process variables (e.g., temperature, pressure, etc.) and the instrumentation and control system that monitors and manages the process. One limitation of this approach that has prevented its use in nuclear power plant PRAs is the difficulty of integrating the results of a Markov analysis into an existing PRA. In this paper, we explore a new approach to the generation of failure scenarios and their compilation into dynamic event trees from a Markov model of the system. These event trees can be integrated into an existing PRA using software tools such as SAPHIRE. To implement our approach, we first construct a discrete-time Markov chain modeling the system of interest by: (a) partitioning the process variable state space into magnitude intervals (cells), (b) using analytical equations or a system simulator to determine the transition probabilities between the cells through the cell-to-cell mapping technique, and, (c) using given failure/repair data for all the components of interest. The Markov transition matrix thus generated can be thought of as a process model describing the stochastic dynamic behavior of the finite-state system. We can therefore search the state space starting from a set of initial states to explore all possible paths to failure (scenarios) with associated probabilities. We can also construct event trees of arbitrary depth by tracing paths from a chosen initiating event and recording the following events while keeping track of the probabilities associated with each branch in the tree. As an example of our approach, we use the simple level control system often used as benchmark in the literature with one process variable (liquid level in a tank), and three control units: a drain unit and two supply units. Each unit includes a separate level sensor to observe the liquid level in the tank
Moers, Cornelia; Meyer, Antje; Janse, Esther
2017-06-01
High-frequency units are usually processed faster than low-frequency units in language comprehension and language production. Frequency effects have been shown for words as well as word combinations. Word co-occurrence effects can be operationalized in terms of transitional probability (TP). TPs reflect how probable a word is, conditioned by its right or left neighbouring word. This corpus study investigates whether three different age groups-younger children (8-12 years), adolescents (12-18 years) and older (62-95 years) Dutch speakers-show frequency and TP context effects on spoken word durations in reading aloud, and whether age groups differ in the size of these effects. Results show consistent effects of TP on word durations for all age groups. Thus, TP seems to influence the processing of words in context, beyond the well-established effect of word frequency, across the entire age range. However, the study also indicates that age groups differ in the size of TP effects, with older adults having smaller TP effects than adolescent readers. Our results show that probabilistic reduction effects in reading aloud may at least partly stem from contextual facilitation that leads to faster reading times in skilled readers, as well as in young language learners.
Kostyalik, Diána; Vas, Szilvia; Kátai, Zita; Kitka, Tamás; Gyertyán, István; Bagdy, Gyorgy; Tóthfalusi, László
2014-11-19
Shortened rapid eye movement (REM) sleep latency and increased REM sleep amount are presumed biological markers of depression. These sleep alterations are also observable in several animal models of depression as well as during the rebound sleep after selective REM sleep deprivation (RD). Furthermore, REM sleep fragmentation is typically associated with stress procedures and anxiety. The selective serotonin reuptake inhibitor (SSRI) antidepressants reduce REM sleep time and increase REM latency after acute dosing in normal condition and even during REM rebound following RD. However, their therapeutic outcome evolves only after weeks of treatment, and the effects of chronic treatment in REM-deprived animals have not been studied yet. Chronic escitalopram- (10 mg/kg/day, osmotic minipump for 24 days) or vehicle-treated rats were subjected to a 3-day-long RD on day 21 using the flower pot procedure or kept in home cage. On day 24, fronto-parietal electroencephalogram, electromyogram and motility were recorded in the first 2 h of the passive phase. The observed sleep patterns were characterized applying standard sleep metrics, by modelling the transitions between sleep phases using Markov chains and by spectral analysis. Based on Markov chain analysis, chronic escitalopram treatment attenuated the REM sleep fragmentation [accelerated transition rates between REM and non-REM (NREM) stages, decreased REM sleep residence time between two transitions] during the rebound sleep. Additionally, the antidepressant avoided the frequent awakenings during the first 30 min of recovery period. The spectral analysis showed that the SSRI prevented the RD-caused elevation in theta (5-9 Hz) power during slow-wave sleep. Conversely, based on the aggregate sleep metrics, escitalopram had only moderate effects and it did not significantly attenuate the REM rebound after RD. In conclusion, chronic SSRI treatment is capable of reducing several effects on sleep which might be the consequence
Generalized Markov branching models
Li, Junping
2005-01-01
In this thesis, we first considered a modified Markov branching process incorporating both state-independent immigration and resurrection. After establishing the criteria for regularity and uniqueness, explicit expressions for the extinction probability and mean extinction time are presented. The criteria for recurrence and ergodicity are also established. In addition, an explicit expression for the equilibrium distribution is presented.\\ud \\ud We then moved on to investigate the basic proper...
Stochastic modeling of pitting corrosion in underground pipelines using Markov chains
Energy Technology Data Exchange (ETDEWEB)
Velazquez, J.C.; Caleyo, F.; Hallen, J.M.; Araujo, J.E. [Instituto Politecnico Nacional (IPN), Mexico D.F. (Mexico). Escuela Superior de Ingenieria Quimica e Industrias Extractivas (ESIQIE); Valor, A. [Universidad de La Habana, La Habana (Cuba)
2009-07-01
A non-homogenous, linear growth (pure birth) Markov process, with discrete states in continuous time, has been used to model external pitting corrosion in underground pipelines. The transition probability function for the pit depth is obtained from the analytical solution of the forward Kolmogorov equations for this process. The parameters of the transition probability function between depth states can be identified from the observed time evolution of the mean of the pit depth distribution. Monte Carlo simulations were used to predict the time evolution of the mean value of the pit depth distribution in soils with different physicochemical characteristics. The simulated distributions have been used to create an empirical Markov-chain-based stochastic model for predicting the evolution of pitting corrosion from the observed properties of the soil in contact with the pipeline. Real- life case studies, involving simulated and measured pit depth distributions are presented to illustrate the application of the proposed Markov chains model. (author)
English, Thomas
2005-01-01
A standard tool of reliability analysis used at NASA-JSC is the event tree. An event tree is simply a probability tree, with the probabilities determining the next step through the tree specified at each node. The nodal probabilities are determined by a reliability study of the physical system at work for a particular node. The reliability study performed at a node is typically referred to as a fault tree analysis, with the potential of a fault tree existing.for each node on the event tree. When examining an event tree it is obvious why the event tree/fault tree approach has been adopted. Typical event trees are quite complex in nature, and the event tree/fault tree approach provides a systematic and organized approach to reliability analysis. The purpose of this study was two fold. Firstly, we wanted to explore the possibility that a semi-Markov process can create dependencies between sojourn times (the times it takes to transition from one state to the next) that can decrease the uncertainty when estimating time to failures. Using a generalized semi-Markov model, we studied a four element reliability model and were able to demonstrate such sojourn time dependencies. Secondly, we wanted to study the use of semi-Markov processes to introduce a time variable into the event tree diagrams that are commonly developed in PRA (Probabilistic Risk Assessment) analyses. Event tree end states which change with time are more representative of failure scenarios than are the usual static probability-derived end states.
Ragain, Stephen; Ugander, Johan
2016-01-01
As datasets capturing human choices grow in richness and scale---particularly in online domains---there is an increasing need for choice models that escape traditional choice-theoretic axioms such as regularity, stochastic transitivity, and Luce's choice axiom. In this work we introduce the Pairwise Choice Markov Chain (PCMC) model of discrete choice, an inferentially tractable model that does not assume any of the above axioms while still satisfying the foundational axiom of uniform expansio...
Directory of Open Access Journals (Sweden)
Fang Ming
2017-01-01
Full Text Available As a main direction of Human Resource Management, employee turnover can provide decision support for managers. In this paper, we aim at predicting the turnover amount of employee on condition of different variable values. The properties of employee and job position are formulated as two variables, where the value of variable varies according to the the state of properties. Additionally, state-transition model is applied to describing employee’s job-state as well as the turnover type. Subsequently, we proposed a semi-Markov model to calculate the conditional turnover amount of employee. Then, we provide a dataset of employee records to illustrate how these models work in reality. Finally, it is proven that the proposed method in this paper is with great significance for managers to develop recruitment plans, promote rules, and retire regulations
DEFF Research Database (Denmark)
Asmussen, Søren; Albrecher, Hansjörg
The book gives a comprehensive treatment of the classical and modern ruin probability theory. Some of the topics are Lundberg's inequality, the Cramér-Lundberg approximation, exact solutions, other approximations (e.g., for heavy-tailed claim size distributions), finite horizon ruin probabilities......, extensions of the classical compound Poisson model to allow for reserve-dependent premiums, Markov-modulation, periodicity, change of measure techniques, phase-type distributions as a computational vehicle and the connection to other applied probability areas, like queueing theory. In this substantially...... updated and extended second version, new topics include stochastic control, fluctuation theory for Levy processes, Gerber–Shiu functions and dependence....
Akita, Tomoyuki; Tanaka, Junko; Ohisa, Masayuki; Sugiyama, Aya; Nishida, Kazuo; Inoue, Shingo; Shirasaka, Takuma
2016-11-01
Simulation studies were performed to predict the future supply and demand for blood donations, and future shortfalls. Using data from all donations in 2006 to 2009, the Markov model was applied to estimate future blood donations until 2050. Based on data concerning the actual use of blood products, the number of blood products needed was estimated based on future population projections. We estimated that the number of blood donations increased from 5,020,000 in 2008 to 5,260,000 in 2012, but will decrease to 4,770,000 units by 2025. In particular, the number of donors in their 20s and 30s decreased every year. Moreover, the number of donations required to supply blood products would have been increased from 5,390,000 in 2012 to 5,660,000 units in 2025. Thus, the estimated shortfall of blood donations is expected to increase each year from 140,000 in 2012 to 890,000 in 2025 and then more than double to 1,670,000 in 2050. If the current blood donation behaviors continue, a shortfall of blood availability is likely to occur in Japan. Insufficient blood donations are mainly related to a projected reduction in population of 20 to 30 year olds, a significant group of donors. Thus, it is crucial to recruit and retain new donors and to develop recommendations for proper use of blood products to minimize unnecessary use. This study provides useful information that can be used by governments to help ensure the adequacy of the blood supply through promoting donations and conserving blood resources. © 2016 AABB.
Finding metastabilities in reversible Markov chains based on incomplete sampling
Directory of Open Access Journals (Sweden)
Fackeldey Konstantin
2017-01-01
Full Text Available In order to fully characterize the state-transition behaviour of finite Markov chains one needs to provide the corresponding transition matrix P. In many applications such as molecular simulation and drug design, the entries of the transition matrix P are estimated by generating realizations of the Markov chain and determining the one-step conditional probability Pij for a transition from one state i to state j. This sampling can be computational very demanding. Therefore, it is a good idea to reduce the sampling effort. The main purpose of this paper is to design a sampling strategy, which provides a partial sampling of only a subset of the rows of such a matrix P. Our proposed approach fits very well to stochastic processes stemming from simulation of molecular systems or random walks on graphs and it is different from the matrix completion approaches which try to approximate the transition matrix by using a low-rank-assumption. It will be shown how Markov chains can be analyzed on the basis of a partial sampling. More precisely. First, we will estimate the stationary distribution from a partially given matrix P. Second, we will estimate the infinitesimal generator Q of P on the basis of this stationary distribution. Third, from the generator we will compute the leading invariant subspace, which should be identical to the leading invariant subspace of P. Forth, we will apply Robust Perron Cluster Analysis (PCCA+ in order to identify metastabilities using this subspace.
Absolute El Transition Probabilities in the Deformed Nuclei 177Yb and Hf179
International Nuclear Information System (INIS)
Malmskog, Sven G.
1964-08-01
The absolute E1-transition probabilities have been measured for two 7/2 - (514) levels populated from the short-lived isomers 177m Yb (6.4 sec) and 179m Hf (18.6 sec), a delayed coincidence set up being used for the purpose. This gave a half life of T 1/2 = (4.48 ± 0.08)x10 -9 sec for the 104 keV level in 177 Yb and T 1/2 = (1.86 ± 0.05)x10 -9 sec for the 217 keV level in 179 Hf. The result has been compared with the calculations made by Nilsson. The effect of the pairing correlation has also been taken into account. An agreement with the experimental values to within a factor of three is obtained. An extrapolated estimate of the half life for the 408.9 keV 7/2 - (514) level in 181 W gives 3x10 -11 sec
Engdahl, N.B.; Vogler, E.T.; Weissmann, G.S.
2010-01-01
River-aquifer exchange is considered within a transition probability framework along the Rio Grande in Albuquerque, New Mexico, to provide a stochastic estimate of aquifer heterogeneity and river loss. Six plausible hydrofacies configurations were determined using categorized drill core and wetland survey data processed through the TPROGS geostatistical package. A base case homogeneous model was also constructed for comparison. River loss was simulated for low, moderate, and high Rio Grande stages and several different riverside drain stage configurations. Heterogeneity effects were quantified by determining the mean and variance of the K field for each realization compared to the root-mean-square (RMS) error of the observed groundwater head data. Simulation results showed that the heterogeneous models produced smaller estimates of loss than the homogeneous approximation. Differences between heterogeneous and homogeneous model results indicate that the use of a homogeneous K in a regional-scale model may result in an overestimation of loss but comparable RMS error. We find that the simulated river loss is dependent on the aquifer structure and is most sensitive to the volumetric proportion of fines within the river channel. Copyright 2010 by the American Geophysical Union.
Absolute El Transition Probabilities in the Deformed Nuclei {sup 177}Yb and {sup 1}
Energy Technology Data Exchange (ETDEWEB)
Malmskog, Sven G
1964-08-15
The absolute E1-transition probabilities have been measured for two 7/2{sup -} (514) levels populated from the short-lived isomers {sup 177m}Yb (6.4 sec) and {sup 179m}Hf (18.6 sec), a delayed coincidence set up being used for the purpose. This gave a half life of T{sub 1/2} = (4.48 {+-} 0.08)x10{sup -9} sec for the 104 keV level in {sup 177}Yb and T{sub 1/2} = (1.86 {+-} 0.05)x10{sup -9} sec for the 217 keV level in {sup 179}Hf. The result has been compared with the calculations made by Nilsson. The effect of the pairing correlation has also been taken into account. An agreement with the experimental values to within a factor of three is obtained. An extrapolated estimate of the half life for the 408.9 keV 7/2{sup -} (514) level in {sup 181}W gives 3x10{sup -11} sec.
The reduced transition probabilities for excited states of rare-earths and actinide even-even nuclei
Energy Technology Data Exchange (ETDEWEB)
Ghumman, S. S. [Department of Physics, Sant Longowal Institute of Engineering and Technology (Deemed University), Longowal, Sangrur-148106, Punjab, India s-ghumman@yahoo.com (India)
2015-08-28
The theoretical B(E2) ratios have been calculated on DF, DR and Krutov models. A simple method based on the work of Arima and Iachello is used to calculate the reduced transition probabilities within SU(3) limit of IBA-I framework. The reduced E2 transition probabilities from second excited states of rare-earths and actinide even–even nuclei calculated from experimental energies and intensities from recent data, have been found to compare better with those calculated on the Krutov model and the SU(3) limit of IBA than the DR and DF models.
HMMEditor: a visual editing tool for profile hidden Markov model
Directory of Open Access Journals (Sweden)
Cheng Jianlin
2008-03-01
Full Text Available Abstract Background Profile Hidden Markov Model (HMM is a powerful statistical model to represent a family of DNA, RNA, and protein sequences. Profile HMM has been widely used in bioinformatics research such as sequence alignment, gene structure prediction, motif identification, protein structure prediction, and biological database search. However, few comprehensive, visual editing tools for profile HMM are publicly available. Results We develop a visual editor for profile Hidden Markov Models (HMMEditor. HMMEditor can visualize the profile HMM architecture, transition probabilities, and emission probabilities. Moreover, it provides functions to edit and save HMM and parameters. Furthermore, HMMEditor allows users to align a sequence against the profile HMM and to visualize the corresponding Viterbi path. Conclusion HMMEditor provides a set of unique functions to visualize and edit a profile HMM. It is a useful tool for biological sequence analysis and modeling. Both HMMEditor software and web service are freely available.
Reviving Markov processes and applications
International Nuclear Information System (INIS)
Cai, H.
1988-01-01
In this dissertation we study a procedure which restarts a Markov process when the process is killed by some arbitrary multiplicative functional. The regenerative nature of this revival procedure is characterized through a Markov renewal equation. An interesting duality between the revival procedure and the classical killing operation is found. Under the condition that the multiplicative functional possesses an intensity, the generators of the revival process can be written down explicitly. An intimate connection is also found between the perturbation of the sample path of a Markov process and the perturbation of a generator (in Kato's sense). The applications of the theory include the study of the processes like piecewise-deterministic Markov process, virtual waiting time process and the first entrance decomposition (taboo probability)
Using hidden Markov models to align multiple sequences.
Mount, David W
2009-07-01
A hidden Markov model (HMM) is a probabilistic model of a multiple sequence alignment (msa) of proteins. In the model, each column of symbols in the alignment is represented by a frequency distribution of the symbols (called a "state"), and insertions and deletions are represented by other states. One moves through the model along a particular path from state to state in a Markov chain (i.e., random choice of next move), trying to match a given sequence. The next matching symbol is chosen from each state, recording its probability (frequency) and also the probability of going to that state from a previous one (the transition probability). State and transition probabilities are multiplied to obtain a probability of the given sequence. The hidden nature of the HMM is due to the lack of information about the value of a specific state, which is instead represented by a probability distribution over all possible values. This article discusses the advantages and disadvantages of HMMs in msa and presents algorithms for calculating an HMM and the conditions for producing the best HMM.
Verification of Open Interactive Markov Chains
Brazdil, Tomas; Hermanns, Holger; Krcal, Jan; Kretinsky, Jan; Rehak, Vojtech
2012-01-01
Interactive Markov chains (IMC) are compositional behavioral models extending both labeled transition systems and continuous-time Markov chains. IMC pair modeling convenience - owed to compositionality properties - with effective verification algorithms and tools - owed to Markov properties. Thus far however, IMC verification did not consider compositionality properties, but considered closed systems. This paper discusses the evaluation of IMC in an open and thus compositional interpretation....
Spectral methods for quantum Markov chains
Energy Technology Data Exchange (ETDEWEB)
Szehr, Oleg
2014-05-08
The aim of this project is to contribute to our understanding of quantum time evolutions, whereby we focus on quantum Markov chains. The latter constitute a natural generalization of the ubiquitous concept of a classical Markov chain to describe evolutions of quantum mechanical systems. We contribute to the theory of such processes by introducing novel methods that allow us to relate the eigenvalue spectrum of the transition map to convergence as well as stability properties of the Markov chain.
Spectral methods for quantum Markov chains
International Nuclear Information System (INIS)
Szehr, Oleg
2014-01-01
The aim of this project is to contribute to our understanding of quantum time evolutions, whereby we focus on quantum Markov chains. The latter constitute a natural generalization of the ubiquitous concept of a classical Markov chain to describe evolutions of quantum mechanical systems. We contribute to the theory of such processes by introducing novel methods that allow us to relate the eigenvalue spectrum of the transition map to convergence as well as stability properties of the Markov chain.
Hidden-Markov-Model Analysis Of Telemanipulator Data
Hannaford, Blake; Lee, Paul
1991-01-01
Mathematical model and procedure based on hidden-Markov-model concept undergoing development for use in analysis and prediction of outputs of force and torque sensors of telerobotic manipulators. In model, overall task broken down into subgoals, and transition probabilities encode ease with which operator completes each subgoal. Process portion of model encodes task-sequence/subgoal structure, and probability-density functions for forces and torques associated with each state of manipulation encode sensor signals that one expects to observe at subgoal. Parameters of model constructed from engineering knowledge of task.
Stark broadening parameters and transition probabilities of persistent lines of Tl II
de Andrés-García, I.; Colón, C.; Fernández-Martínez, F.
2018-05-01
The presence of singly ionized thallium in the stellar atmosphere of the chemically peculiar star χ Lupi was reported by Leckrone et al. in 1999 by analysis of its stellar spectrum obtained with the Goddard High Resolution Spectrograph (GHRS) on board the Hubble Space Telescope. Atomic data about the spectral line of 1307.50 Å and about the hyperfine components of the spectral lines of 1321.71 Å and 1908.64 Å were taken from different sources and used to analyse the isotopic abundance of thallium II in the star χ Lupi. From their results the authors concluded that the photosphere of the star presents an anomalous isotopic composition of Tl II. A study of the atomic parameters of Tl II and of the broadening by the Stark effect of its spectral lines (and therefore of the possible overlaps of these lines) can help to clarify the conclusions about the spectral abundance of Tl II in different stars. In this paper we present calculated values of the atomic transition probabilities and Stark broadening parameters for 49 spectral lines of Tl II obtained by using the Cowan code including core polarization effects and the Griem semiempirical approach. Theoretical values of radiative lifetimes for 11 levels (eight with experimental values in the bibliography) are calculated and compared with the experimental values in order to test the quality of our results. Theoretical trends of the Stark width and shift parameters versus the temperature for spectral lines of astrophysical interest are displayed. Trends of our calculated Stark width for the isoelectronic sequence Tl II-Pb III-Bi IV are also displayed.
Chandrasekar, A; Rakkiyappan, R; Cao, Jinde
2015-10-01
This paper studies the impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach. The array of neural networks are coupled in a random fashion which is governed by Bernoulli random variable. The aim of this paper is to obtain the synchronization criteria, which is suitable for both exactly known and partly unknown transition probabilities such that the coupled neural network is synchronized with mixed time-delay. The considered impulsive effects can be synchronized at partly unknown transition probabilities. Besides, a multiple integral approach is also proposed to strengthen the Markovian jumping randomly coupled neural networks with partly unknown transition probabilities. By making use of Kronecker product and some useful integral inequalities, a novel Lyapunov-Krasovskii functional was designed for handling the coupled neural network with mixed delay and then impulsive synchronization criteria are solvable in a set of linear matrix inequalities. Finally, numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Probabilistic Reachability for Parametric Markov Models
DEFF Research Database (Denmark)
Hahn, Ernst Moritz; Hermanns, Holger; Zhang, Lijun
2011-01-01
Given a parametric Markov model, we consider the problem of computing the rational function expressing the probability of reaching a given set of states. To attack this principal problem, Daws has suggested to first convert the Markov chain into a finite automaton, from which a regular expression...
International Nuclear Information System (INIS)
Nakajima, Kenichi; Hasegawa, Minoru; Inaki, Anri; Wakabayashi, Hiroshi; Takehara, Kazuhiko; Kinuya, Seigo; Hosoya, Tetsuo
2011-01-01
Esophageal complication is common in systemic sclerosis (SSc), but scintigraphic transit patterns based on each subtype have not been understood well. The aim of this study was to develop a new algorithm for integrating a dynamic esophageal transit study and to apply the method to patients with SSc. A total of 40 patients suspected of having SSc were examined by a dynamic esophageal transit study. The subtypes included 32 with definite SSc (15 limited cutaneous type and 17 diffuse cutaneous type) and 8 with probable SSc. The serial esophageal images were shifted and summed to a functional image (sliding sum image) and compared to a conventional condensed image analysis. Esophageal retention fraction at 90 s (R 90 ) and half-time (T 1/2 ) of transit were also measured. The four patterns of the sliding sum image and condensed image agreed in all patients. Abnormal retention patterns were observed in none of the 8 (0%) patients with the probable SSc and in 15 of 32 (47%) patients with definite SSc (p=0.014). The severity of scleroderma assessed by modified Rodnan skin thickness score correlated with that of esophageal retention R 90 (p=0.04). The sliding sum image is a simple and effective method for integrating esophageal transit. Patients with definite SSc and severe scleroderma had significantly higher retention patterns, while probable SSc patients showed no esophageal dysmotility. (author)
Directory of Open Access Journals (Sweden)
Yanbo Li
2014-01-01
Full Text Available This paper is devoted to the investigation of the design of robust guaranteed cost observer for a class of linear singular Markovian jump time-delay systems with generally incomplete transition probability. In this singular model, each transition rate can be completely unknown or only its estimate value is known. Based on stability theory of stochastic differential equations and linear matrix inequality (LMI technique, we design an observer to ensure that, for all uncertainties, the resulting augmented system is regular, impulse free, and robust stochastically stable with the proposed guaranteed cost performance. Finally, a convex optimization problem with LMI constraints is formulated to design the suboptimal guaranteed cost filters for linear singular Markovian jump time-delay systems with generally incomplete transition probability.
Mandal, Anirban; Hunt, Katharine L. C.
2018-05-01
For a perturbed quantum system initially in the ground state, the coefficient ck(t) of excited state k in the time-dependent wave function separates into adiabatic and nonadiabatic terms. The adiabatic term ak(t) accounts for the adjustment of the original ground state to form the new ground state of the instantaneous Hamiltonian H(t), by incorporating excited states of the unperturbed Hamiltonian H0 without transitions; ak(t) follows the adiabatic theorem of Born and Fock. The nonadiabatic term bk(t) describes excitation into another quantum state k; bk(t) is obtained as an integral containing the time derivative of the perturbation. The true transition probability is given by |bk(t)|2, as first stated by Landau and Lifshitz. In this work, we contrast |bk(t)|2 and |ck(t)|2. The latter is the norm-square of the entire excited-state coefficient which is used for the transition probability within Fermi's golden rule. Calculations are performed for a perturbing pulse consisting of a cosine or sine wave in a Gaussian envelope. When the transition frequency ωk0 is on resonance with the frequency ω of the cosine wave, |bk(t)|2 and |ck(t)|2 rise almost monotonically to the same final value; the two are intertwined, but they are out of phase with each other. Off resonance (when ωk0 ≠ ω), |bk(t)|2 and |ck(t)|2 differ significantly during the pulse. They oscillate out of phase and reach different maxima but then fall off to equal final values after the pulse has ended, when ak(t) ≡ 0. If ωk0 ω. While the transition probability is rising, the midpoints between successive maxima and minima fit Gaussian functions of the form a exp[-b(t - d)2]. To our knowledge, this is the first analysis of nonadiabatic transition probabilities during a perturbing pulse.
Probable metal-insulator transition in Ag{sub 4}SSe
Energy Technology Data Exchange (ETDEWEB)
Drebushchak, V.A., E-mail: dva@igm.nsc.ru [V.S. Sobolev Institute of Geology and Mineralogy, SB RAS, Pr. Ac. Koptyuga 3, Novosibirsk 630090 (Russian Federation); Novosibirsk State University, Ul. Pirogova 2, Novosibirsk 630090 (Russian Federation); Pal’yanova, G.A.; Seryotkin, Yu.V. [V.S. Sobolev Institute of Geology and Mineralogy, SB RAS, Pr. Ac. Koptyuga 3, Novosibirsk 630090 (Russian Federation); Novosibirsk State University, Ul. Pirogova 2, Novosibirsk 630090 (Russian Federation); Drebushchak, T.N. [Novosibirsk State University, Ul. Pirogova 2, Novosibirsk 630090 (Russian Federation); Institute of Solid State Chemistry and Mechanochemistry, SB RAS, Ul. Kutateladze 18, Novosibirsk 630128 (Russian Federation)
2015-02-15
Highlights: • New phase transition in Ag{sub 4}SSe was discovered with scanning calorimetry and supported with X-ray powder diffraction. • The thermal effect relates to the anomaly in electrical and thermal conductivity of Ag{sub 4}SSe. • Similar thermal and electrical effects in K{sub 3}Cu{sub 8}S{sub 6} are explained with the metal-insulator transition. - Abstract: New phase transition (285 K) in low-temperature monoclinic Ag{sub 4}SSe was found out below the α-β transition (358 K) after the measurements with differential scanning calorimetry. The transition reveals significant hysteresis (over 30 K). X-ray powder diffraction shows that the superlattice with doubled a and b parameters of the unit cell exists below the new transition point. The signs of this new phase transition can be found in thermal and electrical conductivity of Ag{sub 4}SSe published in literature. Elusive phase transition in Ag{sub 2}Se shows similar properties. The new transition is likely related to the metal-insulator type transition, like K{sub 3}Cu{sub 8}S{sub 6}.
International Nuclear Information System (INIS)
Monthus, Cecile; Garel, Thomas
2009-01-01
For Anderson localization on the Cayley tree, we study the statistics of various observables as a function of the disorder strength W and the number N of generations. We first consider the Landauer transmission T N . In the localized phase, its logarithm follows the traveling wave form T N ≅(ln T N )-bar + ln t* where (i) the disorder-averaged value moves linearly (ln(T N ))-bar≅-N/ξ loc and the localization length diverges as ξ loc ∼(W-W c ) -ν loc with ν loc = 1 and (ii) the variable t* is a fixed random variable with a power-law tail P*(t*) ∼ 1/(t*) 1+β(W) for large t* with 0 N are governed by rare events. In the delocalized phase, the transmission T N remains a finite random variable as N → ∞, and we measure near criticality the essential singularity (ln(T ∞ ))-bar∼-|W c -W| -κ T with κ T ∼ 0.25. We then consider the statistical properties of normalized eigenstates Σ x |ψ(x)| 2 = 1, in particular the entropy S = -Σ x |ψ(x)| 2 ln |ψ(x)| 2 and the inverse participation ratios (IPR) I q = Σ x |ψ(x)| 2q . In the localized phase, the typical entropy diverges as S typ ∼( W-W c ) -ν S with ν S ∼ 1.5, whereas it grows linearly as S typ (N) ∼ N in the delocalized phase. Finally for the IPR, we explain how closely related variables propagate as traveling waves in the delocalized phase. In conclusion, both the localized phase and the delocalized phase are characterized by the traveling wave propagation of some probability distributions, and the Anderson localization/delocalization transition then corresponds to a traveling/non-traveling critical point. Moreover, our results point toward the existence of several length scales that diverge with different exponents ν at criticality
Zipf exponent of trajectory distribution in the hidden Markov model
Bochkarev, V. V.; Lerner, E. Yu
2014-03-01
This paper is the first step of generalization of the previously obtained full classification of the asymptotic behavior of the probability for Markov chain trajectories for the case of hidden Markov models. The main goal is to study the power (Zipf) and nonpower asymptotics of the frequency list of trajectories of hidden Markov frequencys and to obtain explicit formulae for the exponent of the power asymptotics. We consider several simple classes of hidden Markov models. We prove that the asymptotics for a hidden Markov model and for the corresponding Markov chain can be essentially different.
Zipf exponent of trajectory distribution in the hidden Markov model
International Nuclear Information System (INIS)
Bochkarev, V V; Lerner, E Yu
2014-01-01
This paper is the first step of generalization of the previously obtained full classification of the asymptotic behavior of the probability for Markov chain trajectories for the case of hidden Markov models. The main goal is to study the power (Zipf) and nonpower asymptotics of the frequency list of trajectories of hidden Markov frequencys and to obtain explicit formulae for the exponent of the power asymptotics. We consider several simple classes of hidden Markov models. We prove that the asymptotics for a hidden Markov model and for the corresponding Markov chain can be essentially different
Volchenkov, Dima; Dawin, Jean René
A system for using dice to compose music randomly is known as the musical dice game. The discrete time MIDI models of 804 pieces of classical music written by 29 composers have been encoded into the transition matrices and studied by Markov chains. Contrary to human languages, entropy dominates over redundancy, in the musical dice games based on the compositions of classical music. The maximum complexity is achieved on the blocks consisting of just a few notes (8 notes, for the musical dice games generated over Bach's compositions). First passage times to notes can be used to resolve tonality and feature a composer.
Shiryaev, Albert N
2016-01-01
This book contains a systematic treatment of probability from the ground up, starting with intuitive ideas and gradually developing more sophisticated subjects, such as random walks, martingales, Markov chains, the measure-theoretic foundations of probability theory, weak convergence of probability measures, and the central limit theorem. Many examples are discussed in detail, and there are a large number of exercises. The book is accessible to advanced undergraduates and can be used as a text for independent study. To accommodate the greatly expanded material in the third edition of Probability, the book is now divided into two volumes. This first volume contains updated references and substantial revisions of the first three chapters of the second edition. In particular, new material has been added on generating functions, the inclusion-exclusion principle, theorems on monotonic classes (relying on a detailed treatment of “π-λ” systems), and the fundamental theorems of mathematical statistics.
International Nuclear Information System (INIS)
Beshtoev, Kh.M.
2006-01-01
I have considered three-neutrino vacuum transitions and oscillations in the general case and obtained expressions for neutrino wave functions in three cases: with CP violation, without CP violation and in the case when direct ν e - ν τ transitions are absent β(θ 13 ) = 0 (some works indicate this possibility). Then using the existing experimental data some analysis has been fulfilled. This analysis definitely has shown that direct transitions ν e - ν τ cannot be closed for the Solar neutrinos, i. e., β(θ 13 ) ≠ 0. It is also shown that the possibility that β(θ 13 ) = 0 cannot be realized by using the mechanism of resonance enhancement of neutrino oscillations in matter (the Sun). It was found out that the probability of ν e - ν e neutrino transitions is a positive defined value, if in reality neutrino oscillations take place, only if the angle of ν e , ν τ mixing β ≤ 15 - 17 deg
Transition probabilities for two-photon H (1з–2з) and He (1 1з–2 1з ...
Indian Academy of Sciences (India)
Transition amplitudes and transition probabilities for the two-photon 1-2 transition in the hydrogen atom and 11-21 transition in helium atom have been calculated using a partialclosure approach. The dominant term is calculated exactly and the remaining sum over intermediate states is calculated using a mean ...
A hidden Markov model approach to neuron firing patterns.
Camproux, A C; Saunier, F; Chouvet, G; Thalabard, J C; Thomas, G
1996-11-01
Analysis and characterization of neuronal discharge patterns are of interest to neurophysiologists and neuropharmacologists. In this paper we present a hidden Markov model approach to modeling single neuron electrical activity. Basically the model assumes that each interspike interval corresponds to one of several possible states of the neuron. Fitting the model to experimental series of interspike intervals by maximum likelihood allows estimation of the number of possible underlying neuron states, the probability density functions of interspike intervals corresponding to each state, and the transition probabilities between states. We present an application to the analysis of recordings of a locus coeruleus neuron under three pharmacological conditions. The model distinguishes two states during halothane anesthesia and during recovery from halothane anesthesia, and four states after administration of clonidine. The transition probabilities yield additional insights into the mechanisms of neuron firing.
Exact goodness-of-fit tests for Markov chains.
Besag, J; Mondal, D
2013-06-01
Goodness-of-fit tests are useful in assessing whether a statistical model is consistent with available data. However, the usual χ² asymptotics often fail, either because of the paucity of the data or because a nonstandard test statistic is of interest. In this article, we describe exact goodness-of-fit tests for first- and higher order Markov chains, with particular attention given to time-reversible ones. The tests are obtained by conditioning on the sufficient statistics for the transition probabilities and are implemented by simple Monte Carlo sampling or by Markov chain Monte Carlo. They apply both to single and to multiple sequences and allow a free choice of test statistic. Three examples are given. The first concerns multiple sequences of dry and wet January days for the years 1948-1983 at Snoqualmie Falls, Washington State, and suggests that standard analysis may be misleading. The second one is for a four-state DNA sequence and lends support to the original conclusion that a second-order Markov chain provides an adequate fit to the data. The last one is six-state atomistic data arising in molecular conformational dynamics simulation of solvated alanine dipeptide and points to strong evidence against a first-order reversible Markov chain at 6 picosecond time steps. © 2013, The International Biometric Society.
Markov chain aggregation for agent-based models
Banisch, Sven
2016-01-01
This self-contained text develops a Markov chain approach that makes the rigorous analysis of a class of microscopic models that specify the dynamics of complex systems at the individual level possible. It presents a general framework of aggregation in agent-based and related computational models, one which makes use of lumpability and information theory in order to link the micro and macro levels of observation. The starting point is a microscopic Markov chain description of the dynamical process in complete correspondence with the dynamical behavior of the agent-based model (ABM), which is obtained by considering the set of all possible agent configurations as the state space of a huge Markov chain. An explicit formal representation of a resulting “micro-chain” including microscopic transition rates is derived for a class of models by using the random mapping representation of a Markov process. The type of probability distribution used to implement the stochastic part of the model, which defines the upd...
Xing, Wei; Shi, Deheng; Zhang, Jicai; Sun, Jinfeng; Zhu, Zunlue
2018-05-01
This paper calculates the potential energy curves of 21 Λ-S and 42 Ω states, which arise from the first two dissociation asymptotes of the CO+ cation. The calculations are conducted using the complete active space self-consistent field method, which is followed by the valence internally contracted multireference configuration interaction approach with the Davidson correction. To improve the reliability and accuracy of the potential energy curves, core-valence correlation and scalar relativistic corrections, as well as the extrapolation of potential energies to the complete basis set limit are taken into account. The spectroscopic parameters and vibrational levels are determined. The spin-orbit coupling effect on the spectroscopic parameters and vibrational levels is evaluated. To better study the transition probabilities, the transition dipole moments are computed. The Franck-Condon factors and Einstein coefficients of some emissions are calculated. The radiative lifetimes are determined for a number of vibrational levels of several states. The transitions between different Λ-S states are evaluated. Spectroscopic routines for observing these states are proposed. The spectroscopic parameters, vibrational levels, transition dipole moments, and transition probabilities reported in this paper can be considered to be very reliable and can be used as guidelines for detecting these states in an appropriate spectroscopy experiment, especially for the states that were very difficult to observe or were not detected in previous experiments.
International Nuclear Information System (INIS)
Costa, A M; Martins, M C; Santos, J P; Indelicato, P; Parente, F
2006-01-01
Energies and transition probabilities of Kβ hypersatellite lines are computed using the Dirac-Fock model for several values of Z throughout the periodic table. The influence of the Breit interaction on the energy shifts from the corresponding diagram lines and on the Kβ h 1 /Kβ h 3 intensity ratio is evaluated. The widths of the double-K hole levels are calculated for Al and Sc. The results are compared to experiment and to other theoretical calculations
International Nuclear Information System (INIS)
Vargin, A.N.; Ganina, N.A.; Konyukhov, V.K.; Selyakov, V.I.
1975-01-01
The problem of calculation of collisional probabilities of rotational transitions (CPRT) in molecule-molecule and molecule-atom interactions in a three-dimensional space has been solved in this paper. A quasiclassical approach was used. The calculation of collisional probabilities of rotational transitions trajectory was carried out in the following way. The particle motion trajectory was calculated by a classical method and the time dependence of the perturbation operator was obtained, its averaging over wave functions of initial and finite states produced CPRT. The classical calculation of the molecule motion trajectory was justified by triviality of the de Broglie wavelength, compared with characteristic atomic distances, and by triviality of a transfered rotational quantum compared with the energy of translational motion of particles. The results of calculation depend on the chosen interaction potential of collisional particles. It follows from the Messy criterion that the region of nonadiabaticity of interaction may be compared with internuclear distances of a molecule. Therefore, for the description of the interaction a short-range potential is required. Analytical expressions were obtained appropriate for practical calculations for one- and two-quantum rotational transitions of diatomic molecules. The CPRT was averaged over the Maxwell distribution over velocities and analytical dependences on a gas temperature were obtained. The results of the numerical calculation of probabilities for the HCl-HCl, HCl-He, CO-CO interactions are presented to illustrate the method
A Markov chain model for CANDU feeder pipe degradation
International Nuclear Information System (INIS)
Datla, S.; Dinnie, K.; Usmani, A.; Yuan, X.-X.
2008-01-01
There is need for risk based approach to manage feeder pipe degradation to ensure safe operation by minimizing the nuclear safety risk. The current lack of understanding of some fundamental degradation mechanisms will result in uncertainty in predicting the rupture frequency. There are still concerns caused by uncertainties in the inspection techniques and engineering evaluations which should be addressed in the current procedures. A probabilistic approach is therefore useful in quantifying the risk and also it provides a tool for risk based decision making. This paper discusses the application of Markov chain model for feeder pipes in order to predict and manage the risks associated with the existing and future aging-related feeder degradation mechanisms. The major challenge in the approach is the lack of service data in characterizing the transition probabilities of the Markov model. The paper also discusses various approaches in estimating plant specific degradation rates. (author)
A Bayesian method for construction of Markov models to describe dynamics on various time-scales.
Rains, Emily K; Andersen, Hans C
2010-10-14
The dynamics of many biological processes of interest, such as the folding of a protein, are slow and complicated enough that a single molecular dynamics simulation trajectory of the entire process is difficult to obtain in any reasonable amount of time. Moreover, one such simulation may not be sufficient to develop an understanding of the mechanism of the process, and multiple simulations may be necessary. One approach to circumvent this computational barrier is the use of Markov state models. These models are useful because they can be constructed using data from a large number of shorter simulations instead of a single long simulation. This paper presents a new Bayesian method for the construction of Markov models from simulation data. A Markov model is specified by (τ,P,T), where τ is the mesoscopic time step, P is a partition of configuration space into mesostates, and T is an N(P)×N(P) transition rate matrix for transitions between the mesostates in one mesoscopic time step, where N(P) is the number of mesostates in P. The method presented here is different from previous Bayesian methods in several ways. (1) The method uses Bayesian analysis to determine the partition as well as the transition probabilities. (2) The method allows the construction of a Markov model for any chosen mesoscopic time-scale τ. (3) It constructs Markov models for which the diagonal elements of T are all equal to or greater than 0.5. Such a model will be called a "consistent mesoscopic Markov model" (CMMM). Such models have important advantages for providing an understanding of the dynamics on a mesoscopic time-scale. The Bayesian method uses simulation data to find a posterior probability distribution for (P,T) for any chosen τ. This distribution can be regarded as the Bayesian probability that the kinetics observed in the atomistic simulation data on the mesoscopic time-scale τ was generated by the CMMM specified by (P,T). An optimization algorithm is used to find the most
Directory of Open Access Journals (Sweden)
Kostić Marina
2014-01-01
Full Text Available Background/Aim. Recent studies have shown that biological treatments for rheumatoid arthritis can change the course of rheumatoid arthritis and improve functional ability of patients with rheumatoid arthritis. In spite of this fact, use of biological therapy is still limited by high prices of these medicines, especially in countries in socioeconomic transition. The aim of our study was to compare costeffectiveness of a combination of tocilizumab and methotrexate with methotrexate alone for rheumatoid arthritis in Serbia, a country in socioeconomic transition. Methods. For the purpose of our study we designed a Markov model using data on therapy efficacy from the available literature, and data on the costs of health states calculated from records of actual patients treated in the Clinical Center Kragujevac, Serbia. The duration of one cycle in our model was set at one month, and the time horizon was 480 months (40 years. The study was done from the social perspective, and all the costs and outcomes were discounted for 3% per year. Results. Treating rheumatoid arthritis with diseasemodifying antirheumatic drugs (DMARDs alone was more cost-effective in comparison with a combination of biologic treatment with tocilizumab and DMARDs. The total costs for treating a patient with DMARDs for one year were on average 261,945.42 RSD, or 2,497.70 Euro and the total costs for treatment with tocilizimab plus DMARDs were on average 1,959,217.44 RSD, or 18,659.20 Euro. However, these results are susceptible to changes in costs and treatment effects of tocilizumab in patients with more severe forms of rheumatoid arthritis. Conclusion. Our results show that the use of tocilizumab for rheumatoid arthrits in economic environment of Serbia is not cost-effective. Use of tocilizumab for treating rheumatoid arthritis can become affordable, if costs of its use become lower. In order to start using expensive biologic medicines in patients in transitional countries
First and second order Markov chain models for synthetic generation of wind speed time series
International Nuclear Information System (INIS)
Shamshad, A.; Bawadi, M.A.; Wan Hussin, W.M.A.; Majid, T.A.; Sanusi, S.A.M.
2005-01-01
Hourly wind speed time series data of two meteorological stations in Malaysia have been used for stochastic generation of wind speed data using the transition matrix approach of the Markov chain process. The transition probability matrices have been formed using two different approaches: the first approach involves the use of the first order transition probability matrix of a Markov chain, and the second involves the use of a second order transition probability matrix that uses the current and preceding values to describe the next wind speed value. The algorithm to generate the wind speed time series from the transition probability matrices is described. Uniform random number generators have been used for transition between successive time states and within state wind speed values. The ability of each approach to retain the statistical properties of the generated speed is compared with the observed ones. The main statistical properties used for this purpose are mean, standard deviation, median, percentiles, Weibull distribution parameters, autocorrelations and spectral density of wind speed values. The comparison of the observed wind speed and the synthetically generated ones shows that the statistical characteristics are satisfactorily preserved
Markov chain model helps predict pitting corrosion depth and rate in underground pipelines
Energy Technology Data Exchange (ETDEWEB)
Caleyo, F.; Velazquez, J.C.; Hallen, J. M. [ESIQIE, Instituto Politecnico Nacional, Mexico D. F. (Mexico); Esquivel-Amezcua, A. [PEMEX PEP Region Sur, Villahermosa, Tabasco (Mexico); Valor, A. [Universidad de la Habana, Vedado, La Habana (Cuba)
2010-07-01
Recent reports place pipeline corrosion costs in North America at seven billion dollars per year. Pitting corrosion causes the higher percentage of failures among other corrosion mechanisms. This has motivated multiple modelling studies to be focused on corrosion pitting of underground pipelines. In this study, a continuous-time, non-homogenous pure birth Markov chain serves to model external pitting corrosion in buried pipelines. The analytical solution of Kolmogorov's forward equations for this type of Markov process gives the transition probability function in a discrete space of pit depths. The transition probability function can be completely identified by making a correlation between the stochastic pit depth mean and the deterministic mean obtained experimentally. The model proposed in this study can be applied to pitting corrosion data from repeated in-line pipeline inspections. Case studies presented in this work show how pipeline inspection and maintenance planning can be improved by using the proposed Markovian model for pitting corrosion.
Influence of polarization potential on probabilities of free-free transitions of electrons
International Nuclear Information System (INIS)
Dobrolyubov, N.Yu.; Kukin, V.D.; Rostovskij, V.S.
1997-01-01
The method for calculating the matrix element of electrical dipole transition between the continuos spectrum states with an account of existence of coulomb and polarization potentials in the atom external area is considered. The recurrent of formulae, enabling the calculation of contribution to the matrix element from integrals over the area outside the atom with application of values of radial wave functions and their first derivatives at the boundary, are obtained
Coupled two-quantum-transition probability for laser photons and microwave plasmons
International Nuclear Information System (INIS)
Hildebrandt, J.
1985-01-01
The introduction of a plasmon-state vector analogous to a photon-field oscillator allows within the rotating-wave approximation, transformation to a time-independent interaction Hamiltonian, so that Fermi's golden rule can be applied to the two-quantum transition. Although the existence of a vector potential is necessary for the oscillator state vectors, only the multipolar Hamiltonian need be used for the off-resonant frequencies
Quantum Markov Chain Mixing and Dissipative Engineering
DEFF Research Database (Denmark)
Kastoryano, Michael James
2012-01-01
This thesis is the fruit of investigations on the extension of ideas of Markov chain mixing to the quantum setting, and its application to problems of dissipative engineering. A Markov chain describes a statistical process where the probability of future events depends only on the state...... of the system at the present point in time, but not on the history of events. Very many important processes in nature are of this type, therefore a good understanding of their behaviour has turned out to be very fruitful for science. Markov chains always have a non-empty set of limiting distributions...... (stationary states). The aim of Markov chain mixing is to obtain (upper and/or lower) bounds on the number of steps it takes for the Markov chain to reach a stationary state. The natural quantum extensions of these notions are density matrices and quantum channels. We set out to develop a general mathematical...
International Nuclear Information System (INIS)
Gonzalez, A. M.; Ortiz, M.; Campos, J.
1995-01-01
Absolute transition probabilities for lines of CR II, Na II and Sb I were determined by emission spectroscopy of laser induced plasmas. the plasma was produced focusing the emission of a pulsed Nd-Yag laser on solid samples containing the atom in study. the light arising from the plasma region was collected by and spectrometer. the detector used was a time-resolved optical multichannel analyzer (OMA III EG and G). The wavelengths of the measured transitions range from 2000 sto 4100 A. The spectral resolution of the system was 0. 2 A. The method can be used in insulators materials as Cl Na crystals and in metallic samples as Al-Cr and Sn-Sn alloys. to avoid self-absorption effects the alloys were made with low Sb or Cr content. Relative transition probabilities have been determined from measurements of emission-line intensities and were placed on an absolute scale by using, where possible, accurate experimental lifetime values form the literature or theoretical data. From these measurements, values for plasma temperature (8000-24000 K), electron densities (∼∼ 10''16 cm ''-3) and self-absorption coefficients have been obtained. (Author) 56 refs
International Nuclear Information System (INIS)
Gonzalez, A.M.; Ortiz, M.; Campos, J.
1995-09-01
Absolute transition probabilities for lines of Cr II, Na II and Sb I were determined by emission spectroscopy of laser induced plasmas. The plasma was produced focusing the emission of a pulsed Nd-Yag laser on solid samples containing the atom in study. The light arising from the plasma region was collected by and spectrometer. the detector used was a time-resolved optical multichannel analyzer (OMA III EG and G). The wavelengths of the measured transitions range from 2000 to 4100 A. The spectral resolution of the system was 0.2 A. The method can be used in insulators materials as Cl Na crystals and in metallic samples as Al-Cr and Sn-Sb alloys. To avoid self-absorption effects the alloys were made with low Sb or Cr content. Relative transition probabilities have been determined from measurements of emission-line intensities and were placed on an absolute scale by using, where possible, accurate experimental lifetime values form the literature or theoretical data. From these measurements, values for plasma temperature (8000-24000K), electron densities (approx 10 ''16 cm''-3) and self-absorption coefficients have been obtained
Uncovering and testing the fuzzy clusters based on lumped Markov chain in complex network.
Jing, Fan; Jianbin, Xie; Jinlong, Wang; Jinshuai, Qu
2013-01-01
Identifying clusters, namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. By means of a lumped Markov chain model of a random walker, we propose two novel ways of inferring the lumped markov transition matrix. Furthermore, some useful results are proposed based on the analysis of the properties of the lumped Markov process. To find the best partition of complex networks, a novel framework including two algorithms for network partition based on the optimal lumped Markovian dynamics is derived to solve this problem. The algorithms are constructed to minimize the objective function under this framework. It is demonstrated by the simulation experiments that our algorithms can efficiently determine the probabilities with which a node belongs to different clusters during the learning process and naturally supports the fuzzy partition. Moreover, they are successfully applied to real-world network, including the social interactions between members of a karate club.
International Nuclear Information System (INIS)
Shah, M.L.; Sahoo, A.C.; Pulhani, A.K.; Gupta, G.P.; Dikshit, B.; Bhatia, M.S.; Suri, B.M.
2014-01-01
Laser-induced photoionization and fluorescence signals were simultaneously observed in atomic samarium using Nd:YAG-pumped dye lasers. Two-color, three-photon photoionization and two-color fluorescence signals were recorded simultaneously as a function of the second-step laser power for two photoionization pathways. The density matrix formalism has been employed to analyze these signals. Two-color laser-induced fluorescence signal depends on the laser powers used for the first and second-step transitions as well as the first and second-step transition probability whereas two-color, three-photon photoionization signal depends on the third-step transition cross-section at the second-step laser wavelength along with the laser powers and transition probability for the first and second-step transitions. Two-color laser-induced fluorescence was used to measure the second-step transition probability. The second-step transition probability obtained was used to infer the photoionization cross-section. Thus, the methodology combining two-color, three-photon photoionization and two-color fluorescence signals in a single experiment has been established for the first time to measure the second-step transition probability as well as the photoionization cross-section. - Highlights: • Laser-induced photoionization and fluorescence signals have been simultaneously observed. • The density matrix formalism has been employed to analyze these signals. • Two-color laser-induced fluorescence was used to measure the second-step transition probability. • The second-step transition probability obtained was used to infer the photoionization cross-section. • Transition probability and photoionization cross-section have been measured in a single experiment
Yin, Yuan; Shi, Deheng; Sun, Jinfeng; Zhu, Zunlue
2018-03-01
This work calculates the potential energy curves of 9 Λ-S and 28 Ω states of the NCl+ cation. The technique employed is the complete active space self-consistent field method, which is followed by the internally contracted multireference configuration interaction approach with the Davidson correction. The Λ-S states are X2Π, 12Σ+, 14Π, 14Σ+, 14Σ-, 24Π, 14Δ, 16Σ+, and 16Π, which are yielded from the first two dissociation channels of NCl+ cation. The Ω states are generated from these Λ-S states. The 14Π, 14Δ, 16Σ+, and 16Π states are inverted with the spin-orbit coupling effect included. The 14Σ+, 16Σ+, and 16Π states are very weakly bound, whose well depths are only several-hundred cm- 1. One avoided crossing of PECs occurs between the 12Σ+ and 22Σ+ states. To improve the quality of potential energy curves, core-valence correlation and scalar relativistic corrections are included. The potential energies are extrapolated to the complete basis set limit. The spectroscopic parameters and vibrational levels are calculated. The transition dipole moments are computed. The Franck-Condon factors, Einstein coefficients, and radiative lifetimes of many transitions are determined. The spectroscopic approaches are proposed for observing these states according to the transition probabilities. The spin-orbit coupling effect on the spectroscopic and vibrational properties is evaluated. The spectroscopic parameters, vibrational levels, transition dipole moments, as well as transition probabilities reported in this paper could be considered to be very reliable.
Transition probabilities of 36Cl and 36Ar excited states in heavy ion reactions
International Nuclear Information System (INIS)
Costa, G.J.; Alexander, T.K.; Forster, J.S.; McDonald, A.B.; Towner, I.S.
The reactions 2 H( 35 Cl,pγ) and 2 H( 35 Cl,nγ) have been used to determine by the recoil-distance method, the lifetimes of levels in 36 Cl and 36 Ar respectively. Large discrepancies exist in the literature for some lifetimes of 36 Cl levels. Transition rates found for decay of the negative parity states in 36 Ar (4178 (3 - ), 4974 (2 - ) and 5171 (5 - ) keV), are compared whith the Maripuu-Hokken model and RPA and TDA predictions [fr
International Nuclear Information System (INIS)
Danielson, Thomas; Sutton, Jonathan E.; Hin, Céline; Virginia Polytechnic Institute and State University; Savara, Aditya
2017-01-01
Lattice based Kinetic Monte Carlo (KMC) simulations offer a powerful simulation technique for investigating large reaction networks while retaining spatial configuration information, unlike ordinary differential equations. However, large chemical reaction networks can contain reaction processes with rates spanning multiple orders of magnitude. This can lead to the problem of “KMC stiffness” (similar to stiffness in differential equations), where the computational expense has the potential to be overwhelmed by very short time-steps during KMC simulations, with the simulation spending an inordinate amount of KMC steps / cpu-time simulating fast frivolous processes (FFPs) without progressing the system (reaction network). In order to achieve simulation times that are experimentally relevant or desired for predictions, a dynamic throttling algorithm involving separation of the processes into speed-ranks based on event frequencies has been designed and implemented with the intent of decreasing the probability of FFP events, and increasing the probability of slow process events -- allowing rate limiting events to become more likely to be observed in KMC simulations. This Staggered Quasi-Equilibrium Rank-based Throttling for Steady-state (SQERTSS) algorithm designed for use in achieving and simulating steady-state conditions in KMC simulations. Lastly, as shown in this work, the SQERTSS algorithm also works for transient conditions: the correct configuration space and final state will still be achieved if the required assumptions are not violated, with the caveat that the sizes of the time-steps may be distorted during the transient period.
Eigenstates and radiative transition probabilities for Tm3+(4f12) in phosphate and tellurite glasses
International Nuclear Information System (INIS)
Spector, N.; Reisfeld, R.; Boehm, L.
1977-01-01
Electronic wavefunctions of Tm 3+ in intermediate coupling were obtained and used to calculate the Usup((lambda)) matrix elements between all possible states of the 4f 12 configuration. The Judd-Ofelt intensity parameters Ωsub(lambda) obtained for Tm 3+ in phosphate and tellurite glasses were used in conjunction with the Usup((lambda))'s to calculate the forced electric dipole line strengths. The total electric and magnetic radiative transition probabilities are calculated. The entire theoretical spectrum involving the ground and excited levels (from 129 nm to 16447 nm) is given. (Auth.)
RESEARCH ABSORBING STATES OF THE SYSTEM USING MARKOV CHAINS AND FUNDAMENTAL MATRIX
Directory of Open Access Journals (Sweden)
Тетяна Мефодіївна ОЛЕХ
2016-02-01
Full Text Available The article discusses the use Markov chains to research models that reflect the essential properties of systems, including methods of measuring the parameters of projects and assess their effectiveness. In the study carried out by its decomposition system for certain discrete state and create a diagram of transitions between these states. Specificity displays various objects Markov homogeneous chains with discrete states and discrete time determined by the method of calculation of transition probabilities. A model of success criteria for absorbing state system that is universal for all projects. A breakdown of passages to the matrix submatrices. The variation elements under matrix Q n with growth linked to the definition of important quantitative characteristics of absorbing circuits: 1 the probability of achieving the status of absorbing any given; 2 the mean number of steps needed to achieve the absorbing state; 3 the mean time that the system spends in each state to hit irreversible system in absorbing state. Built fundamental matrix that allowed calculating the different characteristics of the system. Considered fundamental matrix for supposedly modeled absorbing Markov chain, which gives the forecast for the behavior of the system in the future regardless of the absolute value of the time elapsed from the starting point. This property illustrates the fundamental matrix Markov process that characterizes it as a process without aftereffect.
Directory of Open Access Journals (Sweden)
A. Dukhovny
1987-01-01
Full Text Available This paper isolates and studies a class of Markov chains with a special quasi-triangular form of the transition matrix [so-called ÃŽÂ”m,n(ÃŽÂ”Ã¢Â€Â²m,n-matrix]. Many discrete stochastic processes encountered in applications (queues, inventories and dams have transition matrices which are special cases of a ÃŽÂ”m,n(ÃŽÂ”Ã¢Â€Â²m,n-matrix. Necessary and sufficient conditions for the ergodicity of a Markov chain with transition ÃŽÂ”m,n(ÃŽÂ”Ã¢Â€Â²m,n-matrix are determined in the article in two equivalent versions. According to the first version, these conditions are expressed in terms of certain restrictions imposed on the generating functions Ai(x of the elements of the i-th row of the transition matrix, i=0,1,2,Ã¢Â€Â¦; in the other version they are connected with the characterization of the roots of a certain associated function in the unit circle of the complex plane. Results obtained in the article generalize, complement, and refine similar results existing in the literature.
Finite Markov processes and their applications
Iosifescu, Marius
2007-01-01
A self-contained treatment of finite Markov chains and processes, this text covers both theory and applications. Author Marius Iosifescu, vice president of the Romanian Academy and director of its Center for Mathematical Statistics, begins with a review of relevant aspects of probability theory and linear algebra. Experienced readers may start with the second chapter, a treatment of fundamental concepts of homogeneous finite Markov chain theory that offers examples of applicable models.The text advances to studies of two basic types of homogeneous finite Markov chains: absorbing and ergodic ch
Electromagnetic transition probabilities in the natural-parity rotational bands of 155,157Gd
International Nuclear Information System (INIS)
Kusakari, H.; Oshima, M.; Uchikura, A.; Sugawara, M.; Tomotani, A.; Ichikawa, S.; Iimura, H.; Morikawa, T.; Inamura, T.; Matsuzaki, M.
1992-01-01
The ground-state rotational bands of 155 Gd and 157 Gd have been investigated through multiple Coulomb excitation with beams of 240-MeV 58 Ni and 305-MeV 81 Br. Gamma-ray branchings and E2/M1 mixing ratios were determined by γ-ray angular-distribution measurement. Nuclear lifetimes of levels up to I=21/2 and 23/2 for 155,157 Gd, respectively, have been measured using the Doppler-shift recoil-distance method. The observed signature dependence of M1 transition rates was found to be inverted in relation to the quasiparticle energy splitting. The data are analyzed in terms of the cranking model
Transitions probabilities up to I = 36{sup +} in {sup 160}Yb
Energy Technology Data Exchange (ETDEWEB)
Johnson, N R; McGowan, F K; Winchell, D F; Baktash, C; Garrett, J D; Lee, I Y [Oak Ridge National Lab., TN (United States); Wells, J C [Oak Ridge National Lab., TN (United States); [Tennessee Technological Univ., Cookeville, TN (United States); Chaturvedi, L; Gao, W B; Ma, W C [Vanderbilt Univ., Nashville, TN (United States); Pilotte, S; Yu, C H [Tennessee Univ., Knoxville, TN (United States)
1992-08-01
During the past dozen years or so, numerous groups have worked on the properties of the ytterbium nucleus, and we at Oak Ridge have been actively involved in the study of many of these nuclei. We have concentrated on lifetime measurements of their high-spin states, because it is from the lifetime of a state that one can determine Q{sub t}, the transition quadrupole moment. The importance of obtaining a Q{sub t} value is that it reflects the intrinsic part of the wave function, and hence provides an indication of the collectivity. This paper presents the results from recent Doppler broadened line shape measurements in {sup 160}Yb at very large rotational frequencies of {Dirac_h}{omega} {approx} 0.36 - 0.50 MeV (I = 22{sup +} - 36{sup +}). (author) 16 refs., 3 figs.
Probability and stochastic modeling
Rotar, Vladimir I
2012-01-01
Basic NotionsSample Space and EventsProbabilitiesCounting TechniquesIndependence and Conditional ProbabilityIndependenceConditioningThe Borel-Cantelli TheoremDiscrete Random VariablesRandom Variables and VectorsExpected ValueVariance and Other Moments. Inequalities for DeviationsSome Basic DistributionsConvergence of Random Variables. The Law of Large NumbersConditional ExpectationGenerating Functions. Branching Processes. Random Walk RevisitedBranching Processes Generating Functions Branching Processes Revisited More on Random WalkMarkov ChainsDefinitions and Examples. Probability Distributions of Markov ChainsThe First Step Analysis. Passage TimesVariables Defined on a Markov ChainErgodicity and Stationary DistributionsA Classification of States and ErgodicityContinuous Random VariablesContinuous DistributionsSome Basic Distributions Continuous Multivariate Distributions Sums of Independent Random Variables Conditional Distributions and ExpectationsDistributions in the General Case. SimulationDistribution F...
Cost Probability Analysis of China's Nuclear Fuel Cycle Transition
International Nuclear Information System (INIS)
Gao, R. X.; Ko, W. I.; Lee, S. H.
2015-01-01
The Chinese government has already determined to develop the closed nuclear fuel cycle, its long-term roadmap of spent fuel management has not been decided yet. Currently, it seems that China's booming economy gives abundant financial assurance to develop nuclear programs in full play according to its near-term national plans. However, the viability and sustainability of nuclear power always depends critically on its economics. Therefore, it is necessary to conduct a well focused cost-benefit and objective analysis of China's ongoing nuclear power programs with the future prospects. In this study, we conduct a comparative analysis of electricity generation cost in four reference nuclear fuel cycle transition scenarios by 2050. Direct disposal is assumed to produce the cheapest LCT as low as 62.688 mills/kWh compared to the other options. However, after performing a relative uncertainty study, the results show that the capital cost of reactor is the key cost component which leads to the cost gap
Cost Probability Analysis of China's Nuclear Fuel Cycle Transition
Energy Technology Data Exchange (ETDEWEB)
Gao, R. X. [Univ. of Science and Technology, Daejeon (Korea, Republic of); Ko, W. I.; Lee, S. H. [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)
2015-05-15
The Chinese government has already determined to develop the closed nuclear fuel cycle, its long-term roadmap of spent fuel management has not been decided yet. Currently, it seems that China's booming economy gives abundant financial assurance to develop nuclear programs in full play according to its near-term national plans. However, the viability and sustainability of nuclear power always depends critically on its economics. Therefore, it is necessary to conduct a well focused cost-benefit and objective analysis of China's ongoing nuclear power programs with the future prospects. In this study, we conduct a comparative analysis of electricity generation cost in four reference nuclear fuel cycle transition scenarios by 2050. Direct disposal is assumed to produce the cheapest LCT as low as 62.688 mills/kWh compared to the other options. However, after performing a relative uncertainty study, the results show that the capital cost of reactor is the key cost component which leads to the cost gap.
Energy Technology Data Exchange (ETDEWEB)
Qin, X.; Zhang, S. D. [Qufu Normal University, Qufu (China)
2014-12-15
The six doublet and the two quartet electronic states ({sup 2}Σ{sup +}(2), {sup 2}Σ{sup -}, {sup 2}Π(2), {sup 2}Δ, {sup 4}Σ{sup -}, and {sup 4}Π) of the OH radical have been studied using the multi-reference configuration interaction (MRCI) method where the Davidson correction, core-valence interaction and relativistic effect are considered with large basis sets of aug-cc-pv5z, aug-cc-pcv5z, and cc-pv5z-DK, respectively. Potential energy curves (PECs) and dipole moment functions are also calculated for these states for internuclear distances ranging from 0.05 nm to 0.80 nm. All possible vibrational levels and rotational constants for the bound state X{sup 2}Π and A{sup 2}Σ{sup +} of OH are predicted by numerical solving the radial Schroedinger equation through the Level program, and spectroscopic parameters, which are in good agreements with experimental results, are obtained. Transition dipole moments between the ground state X{sup 2}Π and other excited states are also computed using MRCI, and the transition probability, lifetime, and Franck-Condon factors for the A{sup 2}Σ{sup +} - X{sup 2}Π transition are discussed and compared with existing experimental values.
Transition probabilities for the alkali isoelectronic sequences Li I, Na I, K I, Rb I, Cs I, FR I
International Nuclear Information System (INIS)
Lindgard, A.; Nielsen, S.E.
1977-01-01
Dipole transition probabilities, oscillator strengths, lifetimes (mean lives), and branching ratios derived from a numerical Coulomb approximation are presented for experimentally identified (and some extrapolated) states n< or =12, l< or =4 for each of the following members of the alkali sequences (Z/sub net/ is the net charge of the corresponding ion): Li I Z/sub net/=1-15, 17-24 Rb I Z/sub net/=1-6 Na I Z/sub net/=1-24 Cs I Z/sub net/=1-5 K I Z/sub net/=1-7 Fr I Z/sub net/=2,4. The results are presented in transition diagrams and in tables giving energy-level values and transition wavelengths as well. An appendix on hydrogen results for 5< or =n< or =12, 4< or =l< or =11 is included to represent the high-angular-momentum states of all members of the alkali isoelectronic sequences
Markov stochasticity coordinates
International Nuclear Information System (INIS)
Eliazar, Iddo
2017-01-01
Markov dynamics constitute one of the most fundamental models of random motion between the states of a system of interest. Markov dynamics have diverse applications in many fields of science and engineering, and are particularly applicable in the context of random motion in networks. In this paper we present a two-dimensional gauging method of the randomness of Markov dynamics. The method–termed Markov Stochasticity Coordinates–is established, discussed, and exemplified. Also, the method is tweaked to quantify the stochasticity of the first-passage-times of Markov dynamics, and the socioeconomic equality and mobility in human societies.
Abdulla, Parosh Aziz; Henda, Noomene Ben; Mayr, Richard
2007-01-01
We consider qualitative and quantitative verification problems for infinite-state Markov chains. We call a Markov chain decisive w.r.t. a given set of target states F if it almost certainly eventually reaches either F or a state from which F can no longer be reached. While all finite Markov chains are trivially decisive (for every set F), this also holds for many classes of infinite Markov chains. Infinite Markov chains which contain a finite attractor are decisive w.r.t. every set F. In part...
Markov stochasticity coordinates
Energy Technology Data Exchange (ETDEWEB)
Eliazar, Iddo, E-mail: iddo.eliazar@intel.com
2017-01-15
Markov dynamics constitute one of the most fundamental models of random motion between the states of a system of interest. Markov dynamics have diverse applications in many fields of science and engineering, and are particularly applicable in the context of random motion in networks. In this paper we present a two-dimensional gauging method of the randomness of Markov dynamics. The method–termed Markov Stochasticity Coordinates–is established, discussed, and exemplified. Also, the method is tweaked to quantify the stochasticity of the first-passage-times of Markov dynamics, and the socioeconomic equality and mobility in human societies.
Directory of Open Access Journals (Sweden)
Jean B. Lasserre
2000-01-01
Full Text Available We consider the class of Markov kernels for which the weak or strong Feller property fails to hold at some discontinuity set. We provide a simple necessary and sufficient condition for existence of an invariant probability measure as well as a Foster-Lyapunov sufficient condition. We also characterize a subclass, the quasi (weak or strong Feller kernels, for which the sequences of expected occupation measures share the same asymptotic properties as for (weak or strong Feller kernels. In particular, it is shown that the sequences of expected occupation measures of strong and quasi strong-Feller kernels with an invariant probability measure converge setwise to an invariant measure.
Large deviations for Markov chains in the positive quadrant
Energy Technology Data Exchange (ETDEWEB)
Borovkov, A A; Mogul' skii, A A [S.L. Sobolev Institute for Mathematics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk (Russian Federation)
2001-10-31
The paper deals with so-called N-partially space-homogeneous time-homogeneous Markov chains X(y,n), n=0,1,2,..., X(y,0)=y, in the positive quadrant. These Markov chains are characterized by the following property of the transition probabilities P(y,A)=P(X(y,1) element of A): for some N{>=}0 the measure P(y,dx) depends only on x{sub 2}, y{sub 2}, and x{sub 1}-y{sub 1} in the domain x{sub 1}>N, y{sub 1}>N, and only on x{sub 1}, y{sub 1}, and x{sub 2}-y{sub 2} in the domain x{sub 2}>N, y{sub 2}>N. For such chains the asymptotic behaviour is found for a fixed set B as s{yields}{infinity}, |x|{yields}{infinity}, and n{yields}{infinity}. Some other conditions on the growth of parameters are also considered, for example, |x-y|{yields}{infinity}, |y|{yields}{infinity}. A study is made of the structure of the most probable trajectories, which give the main contribution to this asymptotics, and a number of other results pertaining to the topic are established. Similar results are obtained for the narrower class of 0-partially homogeneous ergodic chains under less restrictive moment conditions on the transition probabilities P(y,dx). Moreover, exact asymptotic expressions for the probabilities P(X(0,n) element of x+B) are found for 0-partially homogeneous ergodic chains under some additional conditions. The interest in partially homogeneous Markov chains in positive octants is due to the mathematical aspects (new and interesting problems arise in the framework of general large deviation theory) as well as applied issues, for such chains prove to be quite accurate mathematical models for numerous basic types of queueing and communication networks such as the widely known Jackson networks, polling systems, or communication networks associated with the ALOHA algorithm. There is a vast literature dealing with the analysis of these objects. The present paper is an attempt to find the extent to which an asymptotic analysis is possible for Markov chains of this type in their general
Marković, Veroljub; Kostić, Marina; Iličković, Ivana; Janković, Slobodan M
2014-09-01
Recent studies have shown that fidaxomicin, a novel antibiotic, can reduce the rate of complications and mortality in patients with colitis induced by Clostridium difficile. Introduction of fidaxomicin in clinical practice is limited by its high costs. The purpose of this study was to estimate the cost effectiveness of using fidaxomicin versus vancomycin in patients with colitis induced by C. difficile who did not respond to oral metronidazole. We constructed a Markov model that was than simulated by Monte-Carlo simulation using 1000 virtual patients with colitis induced by C. difficile. The perspective in our model was institutional. The time horizon was 3 months. Values of transition probabilities and therapy outcomes were estimated from the available literature, the prices of health services were obtained from the Republic Institute for Health Insurance Tariff Book, and the price of fidaxomicin was derived from data gained from the drug manufacturer. The total costs of treating one statistical patient for 3 months with fidaxomicin were higher (48,106.19 ± 118.07 Republic of Serbia dinars [RSD]; 95% confidence interval 47,988.12-48,224.27) than the total costs of treating with vancomycin (25,872.85 ± 41.44 RSD; 95% confidence interval 25,831.41-25,914.29). Our results showed that the treatment of infections induced by C. difficile with fidaxomicin correlated with a lower rate of mortality and with a smaller number of colectomies. The incremental cost-effectiveness ratio of fidaxomicin versus vancomycin for colitis induced by C. difficile per saved life was estimated at 2.97 million RSD and for one avoided colectomy at 10.07 million RSD. Results of our model indicate that fidaxomicin is a cost-effective therapy compared with vancomycin in patients with colitis induced by C. difficile if the outcome is life-year saved. However, if the outcome is the number of avoided colectomies, then fidaxomycin is not a cost-effective option compared with vancomycin. Copyright
Absolute transition probabilities in the NeI 3p-3s fine structure by beam-gas-dye laser spectroscopy
International Nuclear Information System (INIS)
Hartmetz, P.; Schmoranzer, H.
1983-01-01
The beam-gas-dye laser two-step excitation technique is further developed and applied to the direct measurement of absolute atomic transition probabilities in the NeI 3p-3s fine-structure transition array with a maximum experimental error of 5%. (orig.)
International Nuclear Information System (INIS)
Patnaik, R.; Patra, R.; Satpathy, L.
1975-01-01
For even-even nuclei, the excitation energy E2 and the reduced transition probability B (E2) between the ground state and the first excited 2 + state have been considered. On the basis of different models, it is shown that for a nucleus N, Z the relations E2N, Z + E2N + 2,Z + 2 - E2N + 2, Z - E2N, Z + 2 approx. = 0 and B (E2)N, Z + B (E2)N + 2,Z + 2 - B (E2)N + 2,Z - B (E2)N, Z + 2 approx. = 0 hold good, except in certain specified regions. The goodness of these difference equations is tested with the available experimental data. The difference equation of Ross and Bhaduri is shown to follow from our approach. Some predictions of unmeasured E2 and B (E2) values have been made
Tang, Yinan; Chen, Ping
2014-06-01
The sub-prime crisis in the U.S. reveals the limitation of diversification strategy based on mean-variance analysis. A regime switch and a turning point can be observed using a high moment representation and time-dependent transition probability. Up-down price movements are induced by interactions among agents, which can be described by the birth-death (BD) process. Financial instability is visible by dramatically increasing 3rd to 5th moments one-quarter before and during the crisis. The sudden rising high moments provide effective warning signals of a regime-switch or a coming crisis. The critical condition of a market breakdown can be identified from nonlinear stochastic dynamics. The master equation approach of population dynamics provides a unified theory of a calm and turbulent market.
Zhou, Dan; Shi, Deheng; Sun, Jinfeng; Zhu, Zunlue
2018-03-01
In this work, we calculate the potential energy curves of 16 Λ-S and 36 Ω states of beryllium boride (BeB) radical using the complete active space self-consistent field method, followed by the valence internally contracted multireference configuration interaction approach with Davidson correction. The 16 Λ-S states are the X2Π, A2Σ+, B2Π, C2Δ, D2Ʃ-, E2Σ+, G2Π, I2Σ+, a4Σ-, b4Π, c4Σ-, d4Δ, e4Σ+, g4Π, h4Π, and 24Σ+, which are obtained from the first three dissociation channels of the BeB radical. The Ω states are obtained from the Λ-S states. Of the Λ-S states, the G2Π, I2Σ+, and h4Π states exhibit double well curves. The G2Π, b4Π, and g4Π states are inverted with the spin-orbit coupling effect included. The d4Δ, e4Σ+, and g4Π states as well as the second well of the h4Π state are very weakly bound. Avoided crossings exist between the G2Π and H2Π states, the A2Σ+ and E2Σ+ states, the c4Σ- and f4Σ- states, the g4Π and h4Π states, the I2Σ+ and 42Σ+ states, as well as the 24Σ+ and 34Σ+ states. To improve the quality of the potential energy curves, core-valence correlation and scalar relativistic corrections, as well as the extrapolation of the potential energies to the complete basis set limit, are included. The transition dipole moments are computed. Spectroscopic parameters and vibrational levels are determined along with Franck-Condon factors, Einstein coefficients, and radiative lifetimes of many electronic transitions. The transition probabilities are evaluated. The spin-orbit coupling effect on the spectroscopic parameters and vibrational levels is discussed. The spectroscopic parameters, vibrational levels, and transition probabilities reported in this paper can be considered very reliable and can be employed to predict these states in an appropriate spectroscopy experiment.
A New GMRES(m Method for Markov Chains
Directory of Open Access Journals (Sweden)
Bing-Yuan Pu
2013-01-01
Full Text Available This paper presents a class of new accelerated restarted GMRES method for calculating the stationary probability vector of an irreducible Markov chain. We focus on the mechanism of this new hybrid method by showing how to periodically combine the GMRES and vector extrapolation method into a much efficient one for improving the convergence rate in Markov chain problems. Numerical experiments are carried out to demonstrate the efficiency of our new algorithm on several typical Markov chain problems.
Reliability Analysis of 6-Component Star Markov Repairable System with Spatial Dependence
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Liying Wang
2017-01-01
Full Text Available Star repairable systems with spatial dependence consist of a center component and several peripheral components. The peripheral components are arranged around the center component, and the performance of each component depends on its spatial “neighbors.” Vector-Markov process is adapted to describe the performance of the system. The state space and transition rate matrix corresponding to the 6-component star Markov repairable system with spatial dependence are presented via probability analysis method. Several reliability indices, such as the availability, the probabilities of visiting the safety, the degradation, the alert, and the failed state sets, are obtained by Laplace transform method and a numerical example is provided to illustrate the results.
Network Security Risk Assessment System Based on Attack Graph and Markov Chain
Sun, Fuxiong; Pi, Juntao; Lv, Jin; Cao, Tian
2017-10-01
Network security risk assessment technology can be found in advance of the network problems and related vulnerabilities, it has become an important means to solve the problem of network security. Based on attack graph and Markov chain, this paper provides a Network Security Risk Assessment Model (NSRAM). Based on the network infiltration tests, NSRAM generates the attack graph by the breadth traversal algorithm. Combines with the international standard CVSS, the attack probability of atomic nodes are counted, and then the attack transition probabilities of ones are calculated by Markov chain. NSRAM selects the optimal attack path after comprehensive measurement to assessment network security risk. The simulation results show that NSRAM can reflect the actual situation of network security objectively.
Application of Markov chains-entropy to analysis of depositional environments
Energy Technology Data Exchange (ETDEWEB)
Men Guizhen; Shi Xiaohong; Zhao Shuzhi
1989-01-01
The paper systematically and comprehensively discussed application of Markov chains-entropy to analysis of depositional environments of the upper Carboniferous series Taiyuan Formation in Anjialing, Pingshuo open-cast mine, Shanxi. Definite geological meanings were given respectively to calculated values of transition probability matrix, extremity probability matrix, substitution matrix and the entropy. The lithologic successions of coarse-fine-coarse grained layers from bottom upwards in the coal-bearing series made up the general symmetric cyclic patterns. It was suggested that the coal-bearing strata deposited in the coal-forming environment in delta plain-littoral swamps. Quantitative study of cyclic visibility and variation of formation was conducted. The assemblage relation among stratigraphic sequences and the significance of predicting vertical change were emphasized. Results of study showed that overall analysis of Markov chains was an effective method for analysis of depositional environments of coal-bearing strata. 2 refs., 5 figs.
Markov chains and mixing times
Levin, David A
2017-01-01
Markov Chains and Mixing Times is a magical book, managing to be both friendly and deep. It gently introduces probabilistic techniques so that an outsider can follow. At the same time, it is the first book covering the geometric theory of Markov chains and has much that will be new to experts. It is certainly THE book that I will use to teach from. I recommend it to all comers, an amazing achievement. -Persi Diaconis, Mary V. Sunseri Professor of Statistics and Mathematics, Stanford University Mixing times are an active research topic within many fields from statistical physics to the theory of algorithms, as well as having intrinsic interest within mathematical probability and exploiting discrete analogs of important geometry concepts. The first edition became an instant classic, being accessible to advanced undergraduates and yet bringing readers close to current research frontiers. This second edition adds chapters on monotone chains, the exclusion process and hitting time parameters. Having both exercises...
Finding exact constants in a Markov model of Zipfs law generation
Bochkarev, V. V.; Lerner, E. Yu.; Nikiforov, A. A.; Pismenskiy, A. A.
2017-12-01
According to the classical Zipfs law, the word frequency is a power function of the word rank with an exponent -1. The objective of this work is to find multiplicative constant in a Markov model of word generation. Previously, the case of independent letters was mathematically strictly investigated in [Bochkarev V V and Lerner E Yu 2017 International Journal of Mathematics and Mathematical Sciences Article ID 914374]. Unfortunately, the methods used in this paper cannot be generalized in case of Markov chains. The search of the correct formulation of the Markov generalization of this results was performed using experiments with different ergodic matrices of transition probability P. Combinatory technique allowed taking into account all the words with probability of more than e -300 in case of 2 by 2 matrices. It was experimentally proved that the required constant in the limit is equal to the value reciprocal to conditional entropy of matrix row P with weights presenting the elements of the vector π of the stationary distribution of the Markov chain.
Observation uncertainty in reversible Markov chains.
Metzner, Philipp; Weber, Marcus; Schütte, Christof
2010-09-01
In many applications one is interested in finding a simplified model which captures the essential dynamical behavior of a real life process. If the essential dynamics can be assumed to be (approximately) memoryless then a reasonable choice for a model is a Markov model whose parameters are estimated by means of Bayesian inference from an observed time series. We propose an efficient Monte Carlo Markov chain framework to assess the uncertainty of the Markov model and related observables. The derived Gibbs sampler allows for sampling distributions of transition matrices subject to reversibility and/or sparsity constraints. The performance of the suggested sampling scheme is demonstrated and discussed for a variety of model examples. The uncertainty analysis of functions of the Markov model under investigation is discussed in application to the identification of conformations of the trialanine molecule via Robust Perron Cluster Analysis (PCCA+) .
Grabski
2014-01-01
Semi-Markov Processes: Applications in System Reliability and Maintenance is a modern view of discrete state space and continuous time semi-Markov processes and their applications in reliability and maintenance. The book explains how to construct semi-Markov models and discusses the different reliability parameters and characteristics that can be obtained from those models. The book is a useful resource for mathematicians, engineering practitioners, and PhD and MSc students who want to understand the basic concepts and results of semi-Markov process theory. Clearly defines the properties and
Understanding eye movements in face recognition using hidden Markov models.
Chuk, Tim; Chan, Antoni B; Hsiao, Janet H
2014-09-16
We use a hidden Markov model (HMM) based approach to analyze eye movement data in face recognition. HMMs are statistical models that are specialized in handling time-series data. We conducted a face recognition task with Asian participants, and model each participant's eye movement pattern with an HMM, which summarized the participant's scan paths in face recognition with both regions of interest and the transition probabilities among them. By clustering these HMMs, we showed that participants' eye movements could be categorized into holistic or analytic patterns, demonstrating significant individual differences even within the same culture. Participants with the analytic pattern had longer response times, but did not differ significantly in recognition accuracy from those with the holistic pattern. We also found that correct and wrong recognitions were associated with distinctive eye movement patterns; the difference between the two patterns lies in the transitions rather than locations of the fixations alone. © 2014 ARVO.
Energy Technology Data Exchange (ETDEWEB)
Sieres, J. A.; Vazquez, M.; Fernandez-Seara, J.
2004-07-01
In this paper, the generation of sequences of daily radiation values using a library of Markov transition matrices is analysed. The paper describes the fundamentals of the method employed and how sequences of daily radiation can be generated using as input monthly averaged values of the clearness index. The method is applied to the location where the Solar Energy Lab Weather Station of the University of Vigo (longitude 8 degree 41' 18'' O, latitude 42 degree 10' 9'' N) is placed. Radiation sequences are generated for the years 2002 and 2003 and the results are compared with measured radiation values. Results of statistical tests show a bad performance of the generation method for the location studied. (Author)
Determinants of Transitions across Formal/Informal sectors in Egypt
Tansel, Aysit; Ozdemir, Zeynel / A.
2014-01-01
Informality is a salient feature of labor market in Egypt as it is the case with many developing countries. This is the first study of the determinants of worker transitions between various labor market states using panel data from Egypt. We first provide a diagnosis of dynamic worker flows across different labor market states. We develop transition probabilities by gender across different labor market states utilizing Markov transition processes. Next we identify the effects of individual, h...
DEFF Research Database (Denmark)
Justesen, Jørn
2005-01-01
A simple construction of two-dimensional (2-D) fields is presented. Rows and columns are outcomes of the same Markov chain. The entropy can be calculated explicitly.......A simple construction of two-dimensional (2-D) fields is presented. Rows and columns are outcomes of the same Markov chain. The entropy can be calculated explicitly....
Simulation of daily rainfall through markov chain modeling
International Nuclear Information System (INIS)
Sadiq, N.
2015-01-01
Being an agricultural country, the inhabitants of dry land in cultivated areas mainly rely on the daily rainfall for watering their fields. A stochastic model based on first order Markov Chain was developed to simulate daily rainfall data for Multan, D. I. Khan, Nawabshah, Chilas and Barkhan for the period 1981-2010. Transitional probability matrices of first order Markov Chain was utilized to generate the daily rainfall occurrence while gamma distribution was used to generate the daily rainfall amount. In order to achieve the parametric values of mentioned cities, method of moments is used to estimate the shape and scale parameters which lead to synthetic sequence generation as per gamma distribution. In this study, unconditional and conditional probabilities of wet and dry days in sum with means and standard deviations are considered as the essential parameters for the simulated stochastic generation of daily rainfalls. It has been found that the computerized synthetic rainfall series concurred pretty well with the actual observed rainfall series. (author)
Transition probabilities in neutron-rich Se,8280 and the role of the ν g9 /2 orbital
Litzinger, J.; Blazhev, A.; Dewald, A.; Didierjean, F.; Duchêne, G.; Fransen, C.; Lozeva, R.; Verney, D.; de Angelis, G.; Bazzacco, D.; Birkenbach, B.; Bottoni, S.; Bracco, A.; Braunroth, T.; Cederwall, B.; Corradi, L.; Crespi, F. C. L.; Désesquelles, P.; Eberth, J.; Ellinger, E.; Farnea, E.; Fioretto, E.; Gernhäuser, R.; Goasduff, A.; Görgen, A.; Gottardo, A.; Grebosz, J.; Hackstein, M.; Hess, H.; Ibrahim, F.; Jolie, J.; Jungclaus, A.; Kolos, K.; Korten, W.; Leoni, S.; Lunardi, S.; Maj, A.; Menegazzo, R.; Mengoni, D.; Michelagnoli, C.; Mijatovic, T.; Million, B.; Möller, O.; Modamio, V.; Montagnoli, G.; Montanari, D.; Morales, A. I.; Napoli, D. R.; Niikura, M.; Pietralla, N.; Pollarolo, G.; Pullia, A.; Quintana, B.; Recchia, F.; Reiter, P.; Rosso, D.; Sahin, E.; Salsac, M. D.; Scarlassara, F.; Söderström, P.-A.; Stefanini, A. M.; Stezowski, O.; Szilner, S.; Theisen, Ch.; Valiente-Dobón, J. J.; Vandone, V.; Vogt, A.
2018-04-01
Transition probabilities of intermediate-spin yrast and non-yrast excitations in Se,8280 were investigated in a recoil distance Doppler-shift (RDDS) experiment performed at the Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di Legnaro. The Cologne Plunger device for deep inelastic scattering was used for the RDDS technique and was combined with the AGATA Demonstrator array for the γ -ray detection and coupled to the PRISMA magnetic spectrometer for an event-by-event particle identification. In 80Se, the level lifetimes of the yrast (61+) and (81+) states and of a non-yrast band feeding the yrast 41+ state are determined. A spin and parity assignment of the head of this sideband is discussed based on the experimental results and supported by large-scale shell-model calculations. In 82Se, the level lifetimes of the yrast 61+ state and the yrare 42+ state and lifetime limits of the yrast (101+) state and of the 51- state are determined. Although the experimental results contain large uncertainties, they are interpreted with care in terms of large-scale shell-model calculations using the effective interactions JUN45 and jj44b. The excited states' wave functions are investigated and discussed with respect to the role of the neutron g9 /2 orbital.
International Nuclear Information System (INIS)
Moeller, O.
2005-01-01
This work reports on results from two Recoil-Distance-Doppler-Shift lifetime measurements of excited states in 155 Dy and 178 Os. The experiments were carried out at the GASP spektrometer of the Laboratori Nazional i di Legnaro in combination with the Cologne plunger apparatus. The main purpose of the performed experiments was to test the predictions of the X(5) critical point symmetry in these two nuclei. In 156 Dy and 178 Os 29 lifetimes of excited states were derived using the Differential-Decay-Curve method. In weaker reaction channels the nuclei 155 Dy, 157 Dy and 177 Os were populated. In these nuclei 32 additional lifetimes were measured, most of them for the first time. In order to calculate absolute transition probabilities from the measured lifetimes of the first excited band in 156 Dy, essential branching ratios were derived from the measured data with a very small systematic error ( 178 Os confirm the consistency of a X(5) description in these nuclei. A comparision with the well established X(5)-like nuclei in the N=90 isotones gives an agreement with the X(5) description of at least the same quality. (orig.)
Entropy, complexity, and Markov diagrams for random walk cancer models.
Newton, Paul K; Mason, Jeremy; Hurt, Brian; Bethel, Kelly; Bazhenova, Lyudmila; Nieva, Jorge; Kuhn, Peter
2014-12-19
The notion of entropy is used to compare the complexity associated with 12 common cancers based on metastatic tumor distribution autopsy data. We characterize power-law distributions, entropy, and Kullback-Liebler divergence associated with each primary cancer as compared with data for all cancer types aggregated. We then correlate entropy values with other measures of complexity associated with Markov chain dynamical systems models of progression. The Markov transition matrix associated with each cancer is associated with a directed graph model where nodes are anatomical locations where a metastatic tumor could develop, and edge weightings are transition probabilities of progression from site to site. The steady-state distribution corresponds to the autopsy data distribution. Entropy correlates well with the overall complexity of the reduced directed graph structure for each cancer and with a measure of systemic interconnectedness of the graph, called graph conductance. The models suggest that grouping cancers according to their entropy values, with skin, breast, kidney, and lung cancers being prototypical high entropy cancers, stomach, uterine, pancreatic and ovarian being mid-level entropy cancers, and colorectal, cervical, bladder, and prostate cancers being prototypical low entropy cancers, provides a potentially useful framework for viewing metastatic cancer in terms of predictability, complexity, and metastatic potential.
Basic problems and solution methods for two-dimensional continuous 3 × 3 order hidden Markov model
International Nuclear Information System (INIS)
Wang, Guo-gang; Tang, Gui-jin; Gan, Zong-liang; Cui, Zi-guan; Zhu, Xiu-chang
2016-01-01
A novel model referred to as two-dimensional continuous 3 × 3 order hidden Markov model is put forward to avoid the disadvantages of the classical hypothesis of two-dimensional continuous hidden Markov model. This paper presents three equivalent definitions of the model, in which the state transition probability relies on not only immediate horizontal and vertical states but also immediate diagonal state, and in which the probability density of the observation relies on not only current state but also immediate horizontal and vertical states. The paper focuses on the three basic problems of the model, namely probability density calculation, parameters estimation and path backtracking. Some algorithms solving the questions are theoretically derived, by exploiting the idea that the sequences of states on rows or columns of the model can be viewed as states of a one-dimensional continuous 1 × 2 order hidden Markov model. Simulation results further demonstrate the performance of the algorithms. Because there are more statistical characteristics in the structure of the proposed new model, it can more accurately describe some practical problems, as compared to two-dimensional continuous hidden Markov model.
Markov chain of distances between parked cars
International Nuclear Information System (INIS)
Seba, Petr
2008-01-01
We describe the distribution of distances between parked cars as a solution of certain Markov processes and show that its solution is obtained with the help of a distributional fixed point equation. Under certain conditions the process is solved explicitly. The resulting probability density is compared with the actual parking data measured in the city. (fast track communication)
Efficient Modelling and Generation of Markov Automata
Koutny, M.; Timmer, Mark; Ulidowski, I.; Katoen, Joost P.; van de Pol, Jan Cornelis; Stoelinga, Mariëlle Ida Antoinette
This paper introduces a framework for the efficient modelling and generation of Markov automata. It consists of (1) the data-rich process-algebraic language MAPA, allowing concise modelling of systems with nondeterminism, probability and Markovian timing; (2) a restricted form of the language, the
International Nuclear Information System (INIS)
Son, Kwang Seop; Kim, Dong Hoon; Kim, Chang Hwoi; Kang, Hyun Gook
2016-01-01
The Markov analysis is a technique for modeling system state transitions and calculating the probability of reaching various system states. While it is a proper tool for modeling complex system designs involving timing, sequencing, repair, redundancy, and fault tolerance, as the complexity or size of the system increases, so does the number of states of interest, leading to difficulty in constructing and solving the Markov model. This paper introduces a systematic approach of Markov modeling to analyze the dependability of a complex fault-tolerant system. This method is based on the decomposition of the system into independent subsystem sets, and the system-level failure rate and the unavailability rate for the decomposed subsystems. A Markov model for the target system is easily constructed using the system-level failure and unavailability rates for the subsystems, which can be treated separately. This approach can decrease the number of states to consider simultaneously in the target system by building Markov models of the independent subsystems stage by stage, and results in an exact solution for the Markov model of the whole target system. To apply this method we construct a Markov model for the reactor protection system found in nuclear power plants, a system configured with four identical channels and various fault-tolerant architectures. The results show that the proposed method in this study treats the complex architecture of the system in an efficient manner using the merits of the Markov model, such as a time dependent analysis and a sequential process analysis. - Highlights: • Systematic approach of Markov modeling for system dependability analysis is proposed based on the independent subsystem set, its failure rate and unavailability rate. • As an application example, we construct the Markov model for the digital reactor protection system configured with four identical and independent channels, and various fault-tolerant architectures. • The
Basic problems solving for two-dimensional discrete 3 × 4 order hidden markov model
International Nuclear Information System (INIS)
Wang, Guo-gang; Gan, Zong-liang; Tang, Gui-jin; Cui, Zi-guan; Zhu, Xiu-chang
2016-01-01
A novel model is proposed to overcome the shortages of the classical hypothesis of the two-dimensional discrete hidden Markov model. In the proposed model, the state transition probability depends on not only immediate horizontal and vertical states but also on immediate diagonal state, and the observation symbol probability depends on not only current state but also on immediate horizontal, vertical and diagonal states. This paper defines the structure of the model, and studies the three basic problems of the model, including probability calculation, path backtracking and parameters estimation. By exploiting the idea that the sequences of states on rows or columns of the model can be seen as states of a one-dimensional discrete 1 × 2 order hidden Markov model, several algorithms solving the three questions are theoretically derived. Simulation results further demonstrate the performance of the algorithms. Compared with the two-dimensional discrete hidden Markov model, there are more statistical characteristics in the structure of the proposed model, therefore the proposed model theoretically can more accurately describe some practical problems.
Bayesian analysis for reversible Markov chains
Diaconis, P.; Rolles, S.W.W.
2006-01-01
We introduce a natural conjugate prior for the transition matrix of a reversible Markov chain. This allows estimation and testing. The prior arises from random walk with reinforcement in the same way the Dirichlet prior arises from Pólya’s urn. We give closed form normalizing constants, a simple
Markov Chain-Based Acute Effect Estimation of Air Pollution on Elder Asthma Hospitalization
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Li Luo
2017-01-01
Full Text Available Background. Asthma caused substantial economic and health care burden and is susceptible to air pollution. Particularly, when it comes to elder asthma patient (older than 65, the phenomenon is more significant. The aim of this study is to investigate the Markov-based acute effects of air pollution on elder asthma hospitalizations, in forms of transition probabilities. Methods. A retrospective, population-based study design was used to assess temporal patterns in hospitalizations for asthma in a region of Sichuan province, China. Approximately 12 million residents were covered during this period. Relative risk analysis and Markov chain model were employed on daily hospitalization state estimation. Results. Among PM2.5, PM10, NO2, and SO2, only SO2 was significant. When air pollution is severe, the transition probability from a low-admission state (previous day to high-admission state (next day is 35.46%, while it is 20.08% when air pollution is mild. In particular, for female-cold subgroup, the counterparts are 30.06% and 0.01%, respectively. Conclusions. SO2 was a significant risk factor for elder asthma hospitalization. When air pollution worsened, the transition probabilities from each state to high admission states increase dramatically. This phenomenon appeared more evidently, especially in female-cold subgroup (which is in cold season for female admissions. Based on our work, admission amount forecast, asthma intervention, and corresponding healthcare allocation can be done.
Singer, Philipp; Helic, Denis; Taraghi, Behnam; Strohmaier, Markus
2014-01-01
One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.
Directory of Open Access Journals (Sweden)
Philipp Singer
Full Text Available One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.
Inhomogeneous Markov Models for Describing Driving Patterns
DEFF Research Database (Denmark)
Iversen, Emil Banning; Møller, Jan K.; Morales, Juan Miguel
2017-01-01
. Specifically, an inhomogeneous Markov model that captures the diurnal variation in the use of a vehicle is presented. The model is defined by the time-varying probabilities of starting and ending a trip, and is justified due to the uncertainty associated with the use of the vehicle. The model is fitted to data...... collected from the actual utilization of a vehicle. Inhomogeneous Markov models imply a large number of parameters. The number of parameters in the proposed model is reduced using B-splines....
Inhomogeneous Markov Models for Describing Driving Patterns
DEFF Research Database (Denmark)
Iversen, Jan Emil Banning; Møller, Jan Kloppenborg; Morales González, Juan Miguel
. Specically, an inhomogeneous Markov model that captures the diurnal variation in the use of a vehicle is presented. The model is dened by the time-varying probabilities of starting and ending a trip and is justied due to the uncertainty associated with the use of the vehicle. The model is tted to data...... collected from the actual utilization of a vehicle. Inhomogeneous Markov models imply a large number of parameters. The number of parameters in the proposed model is reduced using B-splines....
Energy Technology Data Exchange (ETDEWEB)
Campos, J.; Martin, A.
1984-07-01
In this work transition probabilities between Ievels of n < 11 for K and for the known of K+ are calculated. Two computer programs based on the Coulomb approximation and the most suitable coupling schemes has been used. Lifetimes of all these levels are also calculated. (Author)
Genetic Algorithms Principles Towards Hidden Markov Model
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Nabil M. Hewahi
2011-10-01
Full Text Available In this paper we propose a general approach based on Genetic Algorithms (GAs to evolve Hidden Markov Models (HMM. The problem appears when experts assign probability values for HMM, they use only some limited inputs. The assigned probability values might not be accurate to serve in other cases related to the same domain. We introduce an approach based on GAs to find
out the suitable probability values for the HMM to be mostly correct in more cases than what have been used to assign the probability values.
On almost-periodic points of a topological Markov chain
International Nuclear Information System (INIS)
Bogatyi, Semeon A; Redkozubov, Vadim V
2012-01-01
We prove that a transitive topological Markov chain has almost-periodic points of all D-periods. Moreover, every D-period is realized by continuously many distinct minimal sets. We give a simple constructive proof of the result which asserts that any transitive topological Markov chain has periodic points of almost all periods, and study the structure of the finite set of positive integers that are not periods.
Markov-switching model for nonstationary runoff conditioned on El Nino information
DEFF Research Database (Denmark)
Gelati, Emiliano; Madsen, H.; Rosbjerg, Dan
2010-01-01
We define a Markov-modulated autoregressive model with exogenous input (MARX) to generate runoff scenarios using climatic information. Runoff parameterization is assumed to be conditioned on a hidden climate state following a Markov chain, where state transition probabilities are functions...... of the climatic input. MARX allows stochastic modeling of nonstationary runoff, as runoff anomalies are described by a mixture of autoregressive models with exogenous input, each one corresponding to a climate state. We apply MARX to inflow time series of the Daule Peripa reservoir (Ecuador). El Nino Southern...... Oscillation (ENSO) information is used to condition runoff parameterization. Among the investigated ENSO indexes, the NINO 1+2 sea surface temperature anomalies and the trans-Nino index perform best as predictors. In the perspective of reservoir optimization at various time scales, MARX produces realistic...
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Weimin Chen
2014-01-01
Full Text Available The standard approach to studying financial industrial agglomeration is to construct measures of the degree of agglomeration within financial industry. But such measures often fail to exploit the convergence or divergence of financial agglomeration. In this paper, we apply Markov chain approach to diagnose the convergence of financial agglomeration in China based on the location quotient coefficients across the provincial regions over 1993–2011. The estimation of Markov transition probability matrix offers more detailed insights into the mechanics of financial agglomeration evolution process in China during the research period. The results show that the spatial evolution of financial agglomeration changes faster in the period of 2003–2011 than that in the period of 1993–2002. Furthermore, there exists a very uneven financial development patterns, but there is regional convergence for financial agglomeration in China.
Hidden Markov models for zero-inflated Poisson counts with an application to substance use.
DeSantis, Stacia M; Bandyopadhyay, Dipankar
2011-06-30
Paradigms for substance abuse cue-reactivity research involve pharmacological or stressful stimulation designed to elicit stress and craving responses in cocaine-dependent subjects. It is unclear as to whether stress induced from participation in such studies increases drug-seeking behavior. We propose a 2-state Hidden Markov model to model the number of cocaine abuses per week before and after participation in a stress-and cue-reactivity study. The hypothesized latent state corresponds to 'high' or 'low' use. To account for a preponderance of zeros, we assume a zero-inflated Poisson model for the count data. Transition probabilities depend on the prior week's state, fixed demographic variables, and time-varying covariates. We adopt a Bayesian approach to model fitting, and use the conditional predictive ordinate statistic to demonstrate that the zero-inflated Poisson hidden Markov model outperforms other models for longitudinal count data. Copyright © 2011 John Wiley & Sons, Ltd.
Fitting timeseries by continuous-time Markov chains: A quadratic programming approach
International Nuclear Information System (INIS)
Crommelin, D.T.; Vanden-Eijnden, E.
2006-01-01
Construction of stochastic models that describe the effective dynamics of observables of interest is an useful instrument in various fields of application, such as physics, climate science, and finance. We present a new technique for the construction of such models. From the timeseries of an observable, we construct a discrete-in-time Markov chain and calculate the eigenspectrum of its transition probability (or stochastic) matrix. As a next step we aim to find the generator of a continuous-time Markov chain whose eigenspectrum resembles the observed eigenspectrum as closely as possible, using an appropriate norm. The generator is found by solving a minimization problem: the norm is chosen such that the object function is quadratic and convex, so that the minimization problem can be solved using quadratic programming techniques. The technique is illustrated on various toy problems as well as on datasets stemming from simulations of molecular dynamics and of atmospheric flows
janssen, Anja; Segers, Johan
2013-01-01
The extremes of a univariate Markov chain with regularly varying stationary marginal distribution and asymptotically linear behavior are known to exhibit a multiplicative random walk structure called the tail chain. In this paper we extend this fact to Markov chains with multivariate regularly varying marginal distributions in Rd. We analyze both the forward and the backward tail process and show that they mutually determine each other through a kind of adjoint relation. In ...
International Nuclear Information System (INIS)
Younmyoung Lee; Kunjai Lee
1995-01-01
A model using continuous time Markov process for nuclide transport of decay chain of arbitrary length in the fractured rock medium has been developed. Considering the fracture in the rock matrix as a finite number of compartments, the transition probability for nuclide from the transition intensity between and out of the compartments is represented utilizing Chapman-Kolmogorov equation, with which the expectation and the variance of nuclide distribution for the fractured rock medium could be obtained. A comparison between continuous time Markov process model and available analytical solutions for the nuclide transport of three decay chains without rock matrix diffusion has been made showing comparatively good agreement. Fittings with experimental breakthrough curves obtained with nonsorbing materials such as NaLS and uranine in the artificial fractured rock are also made. (author)
The sticking probability for H-2 on some transition metals at a hydrogen pressure of 1 bar
DEFF Research Database (Denmark)
Johansson, Martin; Lytken, Ole; Chorkendorff, Ib
2008-01-01
The sticking probability for hydrogen on films of Co, Ni, Cu, Ru, Rh, Pd, Ir, and Pt supported on graphite has been measured at a hydrogen pressure of 1 bar in the temperature range 40–200 °C. The sticking probability is found to increase in the order Ni, Co, Ir, Pd, Pt, Rh, and Ru at temperature...
Progression of liver cirrhosis to HCC: an application of hidden Markov model
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Serio Gabriella
2011-04-01
Full Text Available Abstract Background Health service databases of administrative type can be a useful tool for the study of progression of a disease, but the data reported in such sources could be affected by misclassifications of some patients' real disease states at the time. Aim of this work was to estimate the transition probabilities through the different degenerative phases of liver cirrhosis using health service databases. Methods We employed a hidden Markov model to determine the transition probabilities between two states, and of misclassification. The covariates inserted in the model were sex, age, the presence of comorbidities correlated with alcohol abuse, the presence of diagnosis codes indicating hepatitis C virus infection, and the Charlson Index. The analysis was conducted in patients presumed to have suffered the onset of cirrhosis in 2000, observing the disease evolution and, if applicable, death up to the end of the year 2006. Results The incidence of hepatocellular carcinoma (HCC in cirrhotic patients was 1.5% per year. The probability of developing HCC is higher in males (OR = 2.217 and patients over 65 (OR = 1.547; over 65-year-olds have a greater probability of death both while still suffering from cirrhosis (OR = 2.379 and if they have developed HCC (OR = 1.410. A more severe casemix affects the transition from HCC to death (OR = 1.714. The probability of misclassifying subjects with HCC as exclusively affected by liver cirrhosis is 14.08%. Conclusions The hidden Markov model allowing for misclassification is well suited to analyses of health service databases, since it is able to capture bias due to the fact that the quality and accuracy of the available information are not always optimal. The probability of evolution of a cirrhotic subject to HCC depends on sex and age class, while hepatitis C virus infection and comorbidities correlated with alcohol abuse do not seem to have an influence.
Applied probability and stochastic processes
Sumita, Ushio
1999-01-01
Applied Probability and Stochastic Processes is an edited work written in honor of Julien Keilson. This volume has attracted a host of scholars in applied probability, who have made major contributions to the field, and have written survey and state-of-the-art papers on a variety of applied probability topics, including, but not limited to: perturbation method, time reversible Markov chains, Poisson processes, Brownian techniques, Bayesian probability, optimal quality control, Markov decision processes, random matrices, queueing theory and a variety of applications of stochastic processes. The book has a mixture of theoretical, algorithmic, and application chapters providing examples of the cutting-edge work that Professor Keilson has done or influenced over the course of his highly-productive and energetic career in applied probability and stochastic processes. The book will be of interest to academic researchers, students, and industrial practitioners who seek to use the mathematics of applied probability i...
Description of quantum-mechanical motion by using the formalism of non-Markov stochastic process
International Nuclear Information System (INIS)
Skorobogatov, G.A.; Svertilov, S.I.
1999-01-01
The principle possibilities of mathematical modeling of quantum mechanical motion by the theory of a real stochastic processes is considered. The set of equations corresponding to the simplest case of a two-level system undergoing transitions under the influence of electromagnetic field are obtained. It is shown that quantum-mechanical processes are purely discrete processes of non-Markovian type. They are continuous processes in the space of probability amplitudes and posses the properties of quantum Markovity. The formulation of quantum mechanics in terms of the theory of stochastic processes is necessary for its generalization on small space-time intervals [ru
Rate Reduction for State-labelled Markov Chains with Upper Time-bounded CSL Requirements
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Bharath Siva Kumar Tati
2016-07-01
Full Text Available This paper presents algorithms for identifying and reducing a dedicated set of controllable transition rates of a state-labelled continuous-time Markov chain model. The purpose of the reduction is to make states to satisfy a given requirement, specified as a CSL upper time-bounded Until formula. We distinguish two different cases, depending on the type of probability bound. A natural partitioning of the state space allows us to develop possible solutions, leading to simple algorithms for both cases.
Multi-state Markov model for disability: A case of Malaysia Social Security (SOCSO)
Samsuddin, Shamshimah; Ismail, Noriszura
2016-06-01
Studies of SOCSO's contributor outcomes like disability are usually restricted to a single outcome. In this respect, the study has focused on the approach of multi-state Markov model for estimating the transition probabilities among SOCSO's contributor in Malaysia between states: work, temporary disability, permanent disability and death at yearly intervals on age, gender, year and disability category; ignoring duration and past disability experience which is not consider of how or when someone arrived in that category. These outcomes represent different states which depend on health status among the workers.
Introduction to the numerical solutions of Markov chains
Stewart, Williams J
1994-01-01
A cornerstone of applied probability, Markov chains can be used to help model how plants grow, chemicals react, and atoms diffuse - and applications are increasingly being found in such areas as engineering, computer science, economics, and education. To apply the techniques to real problems, however, it is necessary to understand how Markov chains can be solved numerically. In this book, the first to offer a systematic and detailed treatment of the numerical solution of Markov chains, William Stewart provides scientists on many levels with the power to put this theory to use in the actual world, where it has applications in areas as diverse as engineering, economics, and education. His efforts make for essential reading in a rapidly growing field. Here, Stewart explores all aspects of numerically computing solutions of Markov chains, especially when the state is huge. He provides extensive background to both discrete-time and continuous-time Markov chains and examines many different numerical computing metho...
Renewal characterization of Markov modulated Poisson processes
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Marcel F. Neuts
1989-01-01
Full Text Available A Markov Modulated Poisson Process (MMPP M(t defined on a Markov chain J(t is a pure jump process where jumps of M(t occur according to a Poisson process with intensity λi whenever the Markov chain J(t is in state i. M(t is called strongly renewal (SR if M(t is a renewal process for an arbitrary initial probability vector of J(t with full support on P={i:λi>0}. M(t is called weakly renewal (WR if there exists an initial probability vector of J(t such that the resulting MMPP is a renewal process. The purpose of this paper is to develop general characterization theorems for the class SR and some sufficiency theorems for the class WR in terms of the first passage times of the bivariate Markov chain [J(t,M(t]. Relevance to the lumpability of J(t is also studied.
El Yazid Boudaren, Mohamed; Monfrini, Emmanuel; Pieczynski, Wojciech; Aïssani, Amar
2014-11-01
Hidden Markov chains have been shown to be inadequate for data modeling under some complex conditions. In this work, we address the problem of statistical modeling of phenomena involving two heterogeneous system states. Such phenomena may arise in biology or communications, among other fields. Namely, we consider that a sequence of meaningful words is to be searched within a whole observation that also contains arbitrary one-by-one symbols. Moreover, a word may be interrupted at some site to be carried on later. Applying plain hidden Markov chains to such data, while ignoring their specificity, yields unsatisfactory results. The Phasic triplet Markov chain, proposed in this paper, overcomes this difficulty by means of an auxiliary underlying process in accordance with the triplet Markov chains theory. Related Bayesian restoration techniques and parameters estimation procedures according to the new model are then described. Finally, to assess the performance of the proposed model against the conventional hidden Markov chain model, experiments are conducted on synthetic and real data.
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Lammers Jan-Willem J
2007-07-01
Full Text Available Abstract Background In order to accurately distinguish gaps of varying length in drug treatment for chronic conditions from discontinuation without resuming therapy, short-term observation does not suffice. Thus, the use of inhalation corticosteroids (ICS in the long-term, during a ten-year period is investigated. To describe medication use as a continuum, taking into account the timeliness and consistency of refilling, a Markov model is proposed. Methods Patients, that filled at least one prescription in 1993, were selected from the PHARMO medical record linkage system (RLS containing >95% prescription dispensings per patient originating from community pharmacy records of 6 medium-sized cities in the Netherlands. The probabilities of continuous use, the refilling of at least one ICS prescription in each year of follow-up, and medication free periods were assessed by Markov analysis. Stratified analysis according to new use was performed. Results The transition probabilities of the refilling of at least one ICS prescription in the subsequent year of follow-up, were assessed for each year of follow-up and for the total study period. The change of transition probabilities in time was evaluated, e.g. the probability of continuing ICS use of starters in the first two years (51% of follow-up increased to more than 70% in the following years. The probabilities of different patterns of medication use were assessed: continuous use (7.7%, cumulative medication gaps (1–8 years 69.1% and discontinuing (23.2% during ten-year follow-up for new users. New users had lower probability of continuous use (7.7% and more variability in ICS refill patterns than previous users (56%. Conclusion In addition to well-established methods in epidemiology to ascertain compliance and persistence, a Markov model could be useful to further specify the variety of possible patterns of medication use within the continuum of adherence. This Markov model describes variation in
A methodology for stochastic analysis of share prices as Markov chains with finite states.
Mettle, Felix Okoe; Quaye, Enoch Nii Boi; Laryea, Ravenhill Adjetey
2014-01-01
Price volatilities make stock investments risky, leaving investors in critical position when uncertain decision is made. To improve investor evaluation confidence on exchange markets, while not using time series methodology, we specify equity price change as a stochastic process assumed to possess Markov dependency with respective state transition probabilities matrices following the identified state pace (i.e. decrease, stable or increase). We established that identified states communicate, and that the chains are aperiodic and ergodic thus possessing limiting distributions. We developed a methodology for determining expected mean return time for stock price increases and also establish criteria for improving investment decision based on highest transition probabilities, lowest mean return time and highest limiting distributions. We further developed an R algorithm for running the methodology introduced. The established methodology is applied to selected equities from Ghana Stock Exchange weekly trading data.
A stochastic Markov chain model to describe lung cancer growth and metastasis.
Directory of Open Access Journals (Sweden)
Paul K Newton
Full Text Available A stochastic Markov chain model for metastatic progression is developed for primary lung cancer based on a network construction of metastatic sites with dynamics modeled as an ensemble of random walkers on the network. We calculate a transition matrix, with entries (transition probabilities interpreted as random variables, and use it to construct a circular bi-directional network of primary and metastatic locations based on postmortem tissue analysis of 3827 autopsies on untreated patients documenting all primary tumor locations and metastatic sites from this population. The resulting 50 potential metastatic sites are connected by directed edges with distributed weightings, where the site connections and weightings are obtained by calculating the entries of an ensemble of transition matrices so that the steady-state distribution obtained from the long-time limit of the Markov chain dynamical system corresponds to the ensemble metastatic distribution obtained from the autopsy data set. We condition our search for a transition matrix on an initial distribution of metastatic tumors obtained from the data set. Through an iterative numerical search procedure, we adjust the entries of a sequence of approximations until a transition matrix with the correct steady-state is found (up to a numerical threshold. Since this constrained linear optimization problem is underdetermined, we characterize the statistical variance of the ensemble of transition matrices calculated using the means and variances of their singular value distributions as a diagnostic tool. We interpret the ensemble averaged transition probabilities as (approximately normally distributed random variables. The model allows us to simulate and quantify disease progression pathways and timescales of progression from the lung position to other sites and we highlight several key findings based on the model.
A stochastic Markov chain model to describe lung cancer growth and metastasis.
Newton, Paul K; Mason, Jeremy; Bethel, Kelly; Bazhenova, Lyudmila A; Nieva, Jorge; Kuhn, Peter
2012-01-01
A stochastic Markov chain model for metastatic progression is developed for primary lung cancer based on a network construction of metastatic sites with dynamics modeled as an ensemble of random walkers on the network. We calculate a transition matrix, with entries (transition probabilities) interpreted as random variables, and use it to construct a circular bi-directional network of primary and metastatic locations based on postmortem tissue analysis of 3827 autopsies on untreated patients documenting all primary tumor locations and metastatic sites from this population. The resulting 50 potential metastatic sites are connected by directed edges with distributed weightings, where the site connections and weightings are obtained by calculating the entries of an ensemble of transition matrices so that the steady-state distribution obtained from the long-time limit of the Markov chain dynamical system corresponds to the ensemble metastatic distribution obtained from the autopsy data set. We condition our search for a transition matrix on an initial distribution of metastatic tumors obtained from the data set. Through an iterative numerical search procedure, we adjust the entries of a sequence of approximations until a transition matrix with the correct steady-state is found (up to a numerical threshold). Since this constrained linear optimization problem is underdetermined, we characterize the statistical variance of the ensemble of transition matrices calculated using the means and variances of their singular value distributions as a diagnostic tool. We interpret the ensemble averaged transition probabilities as (approximately) normally distributed random variables. The model allows us to simulate and quantify disease progression pathways and timescales of progression from the lung position to other sites and we highlight several key findings based on the model.
Modeling nonhomogeneous Markov processes via time transformation.
Hubbard, R A; Inoue, L Y T; Fann, J R
2008-09-01
Longitudinal studies are a powerful tool for characterizing the course of chronic disease. These studies are usually carried out with subjects observed at periodic visits giving rise to panel data. Under this observation scheme the exact times of disease state transitions and sequence of disease states visited are unknown and Markov process models are often used to describe disease progression. Most applications of Markov process models rely on the assumption of time homogeneity, that is, that the transition rates are constant over time. This assumption is not satisfied when transition rates depend on time from the process origin. However, limited statistical tools are available for dealing with nonhomogeneity. We propose models in which the time scale of a nonhomogeneous Markov process is transformed to an operational time scale on which the process is homogeneous. We develop a method for jointly estimating the time transformation and the transition intensity matrix for the time transformed homogeneous process. We assess maximum likelihood estimation using the Fisher scoring algorithm via simulation studies and compare performance of our method to homogeneous and piecewise homogeneous models. We apply our methodology to a study of delirium progression in a cohort of stem cell transplantation recipients and show that our method identifies temporal trends in delirium incidence and recovery.
Confluence reduction for Markov automata
Timmer, Mark; Katoen, Joost P.; van de Pol, Jaco; Stoelinga, Mariëlle Ida Antoinette
2016-01-01
Markov automata are a novel formalism for specifying systems exhibiting nondeterminism, probabilistic choices and Markovian rates. As expected, the state space explosion threatens the analysability of these models. We therefore introduce confluence reduction for Markov automata, a powerful reduction
Modeling Uncertainty of Directed Movement via Markov Chains
Directory of Open Access Journals (Sweden)
YIN Zhangcai
2015-10-01
Full Text Available Probabilistic time geography (PTG is suggested as an extension of (classical time geography, in order to present the uncertainty of an agent located at the accessible position by probability. This may provide a quantitative basis for most likely finding an agent at a location. In recent years, PTG based on normal distribution or Brown bridge has been proposed, its variance, however, is irrelevant with the agent's speed or divergent with the increase of the speed; so they are difficult to take into account application pertinence and stability. In this paper, a new method is proposed to model PTG based on Markov chain. Firstly, a bidirectional conditions Markov chain is modeled, the limit of which, when the moving speed is large enough, can be regarded as the Brown bridge, thus has the characteristics of digital stability. Then, the directed movement is mapped to Markov chains. The essential part is to build step length, the state space and transfer matrix of Markov chain according to the space and time position of directional movement, movement speed information, to make sure the Markov chain related to the movement speed. Finally, calculating continuously the probability distribution of the directed movement at any time by the Markov chains, it can be get the possibility of an agent located at the accessible position. Experimental results show that, the variance based on Markov chains not only is related to speed, but also is tending towards stability with increasing the agent's maximum speed.
Markov chains and semi-Markov models in time-to-event analysis.
Abner, Erin L; Charnigo, Richard J; Kryscio, Richard J
2013-10-25
A variety of statistical methods are available to investigators for analysis of time-to-event data, often referred to as survival analysis. Kaplan-Meier estimation and Cox proportional hazards regression are commonly employed tools but are not appropriate for all studies, particularly in the presence of competing risks and when multiple or recurrent outcomes are of interest. Markov chain models can accommodate censored data, competing risks (informative censoring), multiple outcomes, recurrent outcomes, frailty, and non-constant survival probabilities. Markov chain models, though often overlooked by investigators in time-to-event analysis, have long been used in clinical studies and have widespread application in other fields.
Markov processes from K. Ito's perspective (AM-155)
Stroock, Daniel W
2003-01-01
Kiyosi Itô''s greatest contribution to probability theory may be his introduction of stochastic differential equations to explain the Kolmogorov-Feller theory of Markov processes. Starting with the geometric ideas that guided him, this book gives an account of Itô''s program. The modern theory of Markov processes was initiated by A. N. Kolmogorov. However, Kolmogorov''s approach was too analytic to reveal the probabilistic foundations on which it rests. In particular, it hides the central role played by the simplest Markov processes: those with independent, identically distributed incremen
From Brownian Dynamics to Markov Chain: An Ion Channel Example
Chen, Wan
2014-02-27
A discrete rate theory for multi-ion channels is presented, in which the continuous dynamics of ion diffusion is reduced to transitions between Markovian discrete states. In an open channel, the ion permeation process involves three types of events: an ion entering the channel, an ion escaping from the channel, or an ion hopping between different energy minima in the channel. The continuous dynamics leads to a hierarchy of Fokker-Planck equations, indexed by channel occupancy. From these the mean escape times and splitting probabilities (denoting from which side an ion has escaped) can be calculated. By equating these with the corresponding expressions from the Markov model, one can determine the Markovian transition rates. The theory is illustrated with a two-ion one-well channel. The stationary probability of states is compared with that from both Brownian dynamics simulation and the hierarchical Fokker-Planck equations. The conductivity of the channel is also studied, and the optimal geometry maximizing ion flux is computed. © 2014 Society for Industrial and Applied Mathematics.
Parameterizing the Spatial Markov Model from Breakthrough Curve Data Alone
Sherman, T.; Bolster, D.; Fakhari, A.; Miller, S.; Singha, K.
2017-12-01
The spatial Markov model (SMM) uses a correlated random walk and has been shown to effectively capture anomalous transport in porous media systems; in the SMM, particles' future trajectories are correlated to their current velocity. It is common practice to use a priori Lagrangian velocity statistics obtained from high resolution simulations to determine a distribution of transition probabilities (correlation) between velocity classes that govern predicted transport behavior; however, this approach is computationally cumbersome. Here, we introduce a methodology to quantify velocity correlation from Breakthrough (BTC) curve data alone; discretizing two measured BTCs into a set of arrival times and reverse engineering the rules of the SMM allows for prediction of velocity correlation, thereby enabling parameterization of the SMM in studies where Lagrangian velocity statistics are not available. The introduced methodology is applied to estimate velocity correlation from BTCs measured in high resolution simulations, thus allowing for a comparison of estimated parameters with known simulated values. Results show 1) estimated transition probabilities agree with simulated values and 2) using the SMM with estimated parameterization accurately predicts BTCs downstream. Additionally, we include uncertainty measurements by calculating lower and upper estimates of velocity correlation, which allow for prediction of a range of BTCs. The simulated BTCs fall in the range of predicted BTCs. This research proposes a novel method to parameterize the SMM from BTC data alone, thereby reducing the SMM's computational costs and widening its applicability.
Oscillator strengths and transition probabilities from the Breit–Pauli R-matrix method: Ne IV
Energy Technology Data Exchange (ETDEWEB)
Nahar, Sultana N., E-mail: nahar@astronomy.ohio-state.edu
2014-09-15
The atomic parameters–oscillator strengths, line strengths, radiative decay rates (A), and lifetimes–for fine structure transitions of electric dipole (E1) type for the astrophysically abundant ion Ne IV are presented. The results include 868 fine structure levels with n≤ 10, l≤ 9, and 1/2≤J≤ 19/2 of even and odd parities, and the corresponding 83,767 E1 transitions. The calculations were carried out using the relativistic Breit–Pauli R-matrix method in the close coupling approximation. The transitions have been identified spectroscopically using an algorithm based on quantum defect analysis and other criteria. The calculated energies agree with the 103 observed and identified energies to within 3% or better for most of the levels. Some larger differences are also noted. The A-values show good to fair agreement with the very limited number of available transitions in the table compiled by NIST, but show very good agreement with the latest published multi-configuration Hartree–Fock calculations. The present transitions should be useful for diagnostics as well as for precise and complete spectral modeling in the soft X-ray to infra-red regions of astrophysical and laboratory plasmas. -- Highlights: •The first application of BPRM method for accurate E1 transitions in Ne IV is reported. •Amount of atomic data (n going up to 10) is complete for most practical applications. •The calculated energies are in very good agreement with most observed levels. •Very good agreement of A-values and lifetimes with other relativistic calculations. •The results should provide precise nebular abundances, chemical evolution etc.
Second Order Optimality in Markov Decision Chains
Czech Academy of Sciences Publication Activity Database
Sladký, Karel
2017-01-01
Roč. 53, č. 6 (2017), s. 1086-1099 ISSN 0023-5954 R&D Projects: GA ČR GA15-10331S Institutional support: RVO:67985556 Keywords : Markov decision chains * second order optimality * optimalilty conditions for transient, discounted and average models * policy and value iterations Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 0.379, year: 2016 http://library.utia.cas.cz/separaty/2017/E/sladky-0485146.pdf
Process Algebra and Markov Chains
Brinksma, Hendrik; Hermanns, H.; Brinksma, Hendrik; Hermanns, H.; Katoen, Joost P.
This paper surveys and relates the basic concepts of process algebra and the modelling of continuous time Markov chains. It provides basic introductions to both fields, where we also study the Markov chains from an algebraic perspective, viz. that of Markov chain algebra. We then proceed to study
Process algebra and Markov chains
Brinksma, E.; Hermanns, H.; Brinksma, E.; Hermanns, H.; Katoen, J.P.
2001-01-01
This paper surveys and relates the basic concepts of process algebra and the modelling of continuous time Markov chains. It provides basic introductions to both fields, where we also study the Markov chains from an algebraic perspective, viz. that of Markov chain algebra. We then proceed to study
From complex spatial dynamics to simple Markov chain models: do predators and prey leave footprints?
DEFF Research Database (Denmark)
Nachman, Gøsta Støger; Borregaard, Michael Krabbe
2010-01-01
to another, are then depicted in a state transition diagram, constituting the "footprints" of the underlying population dynamics. We investigate to what extent changes in the population processes modeled in the complex simulation (i.e. the predator's functional response and the dispersal rates of both......In this paper we present a concept for using presence-absence data to recover information on the population dynamics of predator-prey systems. We use a highly complex and spatially explicit simulation model of a predator-prey mite system to generate simple presence-absence data: the number...... of transition probabilities on state variables, and combine this information in a Markov chain transition matrix model. Finally, we use this extended model to predict the long-term dynamics of the system and to reveal its asymptotic steady state properties....
Indian Academy of Sciences (India)
be obtained as a limiting value of a sample path of a suitable ... makes a mathematical model of chance and deals with the problem by .... Is the Markov chain aperiodic? It is! Here is how you can see it. Suppose that after you do the cut, you hold the top half in your right hand, and the bottom half in your left. Then there.
Composable Markov Building Blocks
Evers, S.; Fokkinga, M.M.; Apers, Peter M.G.; Prade, H.; Subrahmanian, V.S.
2007-01-01
In situations where disjunct parts of the same process are described by their own first-order Markov models and only one model applies at a time (activity in one model coincides with non-activity in the other models), these models can be joined together into one. Under certain conditions, nearly all
Composable Markov Building Blocks
Evers, S.; Fokkinga, M.M.; Apers, Peter M.G.
2007-01-01
In situations where disjunct parts of the same process are described by their own first-order Markov models, these models can be joined together under the constraint that there can only be one activity at a time, i.e. the activities of one model coincide with non-activity in the other models. Under
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 7; Issue 3. Markov Chain Monte Carlo - Examples. Arnab Chakraborty. General Article Volume 7 Issue 3 March 2002 pp 25-34. Fulltext. Click here to view fulltext PDF. Permanent link: https://www.ias.ac.in/article/fulltext/reso/007/03/0025-0034. Keywords.
Probability model of solid to liquid-like transition of a fluid suspension after a shear flow onset
Czech Academy of Sciences Publication Activity Database
Nouar, C.; Říha, Pavel
2008-01-01
Roč. 34, č. 5 (2008), s. 477-483 ISSN 0301-9322 R&D Projects: GA AV ČR IAA200600803 Institutional research plan: CEZ:AV0Z20600510 Keywords : laminar suspension flow * liquid-liquid interface * probability model Subject RIV: BK - Fluid Dynamics Impact factor: 1.497, year: 2008
Wali, Arvin R; Brandel, Michael G; Santiago-Dieppa, David R; Rennert, Robert C; Steinberg, Jeffrey A; Hirshman, Brian R; Murphy, James D; Khalessi, Alexander A
2018-05-01
OBJECTIVE Markov modeling is a clinical research technique that allows competing medical strategies to be mathematically assessed in order to identify the optimal allocation of health care resources. The authors present a review of the recently published neurosurgical literature that employs Markov modeling and provide a conceptual framework with which to evaluate, critique, and apply the findings generated from health economics research. METHODS The PubMed online database was searched to identify neurosurgical literature published from January 2010 to December 2017 that had utilized Markov modeling for neurosurgical cost-effectiveness studies. Included articles were then assessed with regard to year of publication, subspecialty of neurosurgery, decision analytical techniques utilized, and source information for model inputs. RESULTS A total of 55 articles utilizing Markov models were identified across a broad range of neurosurgical subspecialties. Sixty-five percent of the papers were published within the past 3 years alone. The majority of models derived health transition probabilities, health utilities, and cost information from previously published studies or publicly available information. Only 62% of the studies incorporated indirect costs. Ninety-three percent of the studies performed a 1-way or 2-way sensitivity analysis, and 67% performed a probabilistic sensitivity analysis. A review of the conceptual framework of Markov modeling and an explanation of the different terminology and methodology are provided. CONCLUSIONS As neurosurgeons continue to innovate and identify novel treatment strategies for patients, Markov modeling will allow for better characterization of the impact of these interventions on a patient and societal level. The aim of this work is to equip the neurosurgical readership with the tools to better understand, critique, and apply findings produced from cost-effectiveness research.
Elements of the theory of Markov processes and their applications
Bharucha-Reid, A T
2010-01-01
This graduate-level text and reference in probability, with numerous applications to several fields of science, presents nonmeasure-theoretic introduction to theory of Markov processes. The work also covers mathematical models based on the theory, employed in various applied fields. Prerequisites are a knowledge of elementary probability theory, mathematical statistics, and analysis. Appendixes. Bibliographies. 1960 edition.
On Characterisation of Markov Processes Via Martingale Problems
Indian Academy of Sciences (India)
This extension is used to improve on a criterion for a probability measure to be invariant for the semigroup associated with the Markov process. We also give examples of martingale problems that are well-posed in the class of solutions which are continuous in probability but for which no r.c.l.l. solution exists.
Grimmett, Geoffrey
2014-01-01
Probability is an area of mathematics of tremendous contemporary importance across all aspects of human endeavour. This book is a compact account of the basic features of probability and random processes at the level of first and second year mathematics undergraduates and Masters' students in cognate fields. It is suitable for a first course in probability, plus a follow-up course in random processes including Markov chains. A special feature is the authors' attention to rigorous mathematics: not everything is rigorous, but the need for rigour is explained at difficult junctures. The text is enriched by simple exercises, together with problems (with very brief hints) many of which are taken from final examinations at Cambridge and Oxford. The first eight chapters form a course in basic probability, being an account of events, random variables, and distributions - discrete and continuous random variables are treated separately - together with simple versions of the law of large numbers and the central limit th...
NonMarkov Ito Processes with 1- state memory
McCauley, Joseph L.
2010-08-01
A Markov process, by definition, cannot depend on any previous state other than the last observed state. An Ito process implies the Fokker-Planck and Kolmogorov backward time partial differential eqns. for transition densities, which in turn imply the Chapman-Kolmogorov eqn., but without requiring the Markov condition. We present a class of Ito process superficially resembling Markov processes, but with 1-state memory. In finance, such processes would obey the efficient market hypothesis up through the level of pair correlations. These stochastic processes have been mislabeled in recent literature as 'nonlinear Markov processes'. Inspired by Doob and Feller, who pointed out that the ChapmanKolmogorov eqn. is not restricted to Markov processes, we exhibit a Gaussian Ito transition density with 1-state memory in the drift coefficient that satisfies both of Kolmogorov's partial differential eqns. and also the Chapman-Kolmogorov eqn. In addition, we show that three of the examples from McKean's seminal 1966 paper are also nonMarkov Ito processes. Last, we show that the transition density of the generalized Black-Scholes type partial differential eqn. describes a martingale, and satisfies the ChapmanKolmogorov eqn. This leads to the shortest-known proof that the Green function of the Black-Scholes eqn. with variable diffusion coefficient provides the so-called martingale measure of option pricing.
International Nuclear Information System (INIS)
Ertaş, Mehmet; Keskin, Mustafa
2015-01-01
By using the path probability method (PPM) with point distribution, we study the dynamic phase transitions (DPTs) in the Blume–Emery–Griffiths (BEG) model under an oscillating external magnetic field. The phases in the model are obtained by solving the dynamic equations for the average order parameters and a disordered phase, ordered phase and four mixed phases are found. We also investigate the thermal behavior of the dynamic order parameters to analyze the nature dynamic transitions as well as to obtain the DPT temperatures. The dynamic phase diagrams are presented in three different planes in which exhibit the dynamic tricritical point, double critical end point, critical end point, quadrupole point, triple point as well as the reentrant behavior, strongly depending on the values of the system parameters. We compare and discuss the dynamic phase diagrams with dynamic phase diagrams that were obtained within the Glauber-type stochastic dynamics based on the mean-field theory. - Highlights: • Dynamic magnetic behavior of the Blume–Emery–Griffiths system is investigated by using the path probability method. • The time variations of average magnetizations are studied to find the phases. • The temperature dependence of the dynamic magnetizations is investigated to obtain the dynamic phase transition points. • We compare and discuss the dynamic phase diagrams with dynamic phase diagrams that were obtained within the Glauber-type stochastic dynamics based on the mean-field theory
Energy Technology Data Exchange (ETDEWEB)
Ertaş, Mehmet, E-mail: mehmetertas@erciyes.edu.tr; Keskin, Mustafa
2015-03-01
By using the path probability method (PPM) with point distribution, we study the dynamic phase transitions (DPTs) in the Blume–Emery–Griffiths (BEG) model under an oscillating external magnetic field. The phases in the model are obtained by solving the dynamic equations for the average order parameters and a disordered phase, ordered phase and four mixed phases are found. We also investigate the thermal behavior of the dynamic order parameters to analyze the nature dynamic transitions as well as to obtain the DPT temperatures. The dynamic phase diagrams are presented in three different planes in which exhibit the dynamic tricritical point, double critical end point, critical end point, quadrupole point, triple point as well as the reentrant behavior, strongly depending on the values of the system parameters. We compare and discuss the dynamic phase diagrams with dynamic phase diagrams that were obtained within the Glauber-type stochastic dynamics based on the mean-field theory. - Highlights: • Dynamic magnetic behavior of the Blume–Emery–Griffiths system is investigated by using the path probability method. • The time variations of average magnetizations are studied to find the phases. • The temperature dependence of the dynamic magnetizations is investigated to obtain the dynamic phase transition points. • We compare and discuss the dynamic phase diagrams with dynamic phase diagrams that were obtained within the Glauber-type stochastic dynamics based on the mean-field theory.
The How and Why of Interactive Markov Chains
Hermanns, H.; Katoen, Joost P.; de Boer, F.S; Bonsangue, S.H.; Leuschel, M
2010-01-01
This paper reviews the model of interactive Markov chains (IMCs, for short), an extension of labelled transition systems with exponentially delayed transitions. We show that IMCs are closed under parallel composition and hiding, and show how IMCs can be compositionally aggregated prior to analysis
DETERMINANTS OF FOREIGN DIRECT INVESTMENT IN NIGERIA: A MARKOV REGIME-SWITCHING APPROACH
Directory of Open Access Journals (Sweden)
Akinlo A. Enisan
2017-04-01
Full Text Available Several studies have analyzed the movement of foreign direct investment in Nigeria using linear approach. In contrast with all existing studies in Nigeria, this paper runs several non linear FDI equations where the main determinants of FDI are determined using Markov- Regime Switching Model (MSMs. The approach enables us to observe structural changes, where exist, in FDI equations through time. Asides, where FDI regression equation is truly nonlinear, MSMs fit data better than the linear models. The paper adopts maximum likelihood methodology of Markov-Regime Model (MSM to identify possible structural changes in level and/or trends and possible changes in parameters of independent variables through the transition probabilities. The results show that FDI process in Nigeria is governed by two different regimes and a shift from one regime to another regime depends on transition probabilities. The results show that the main determinants of FDI are GDP growth, macro instability, financial development, exchange rate, inflation and discount rate. This implies liberalization that stems inflation and enhance the value of domestic currency will attract more FDI into the country.
Mielke, Steven L.; Truhlar, Donald G.; Schwenke, David W.
1991-01-01
Improved techniques and well-optimized basis sets are presented for application of the outgoing wave variational principle to calculate converged quantum mechanical reaction probabilities. They are illustrated with calculations for the reactions D + H2 yields HD + H with total angular momentum J = 3 and F + H2 yields HF + H with J = 0 and 3. The optimization involves the choice of distortion potential, the grid for calculating half-integrated Green's functions, the placement, width, and number of primitive distributed Gaussians, and the computationally most efficient partition between dynamically adapted and primitive basis functions. Benchmark calculations with 224-1064 channels are presented.
Lismawati, Eka; Respatiwulan; Widyaningsih, Purnami
2017-06-01
The SIS epidemic model describes the pattern of disease spread with characteristics that recovered individuals can be infected more than once. The number of susceptible and infected individuals every time follows the discrete time Markov process. It can be represented by the discrete time Markov chains (DTMC) SIS. The DTMC SIS epidemic model can be developed for two pathogens in two patches. The aims of this paper are to reconstruct and to apply the DTMC SIS epidemic model with two pathogens in two patches. The model was presented as transition probabilities. The application of the model obtain that the number of susceptible individuals decreases while the number of infected individuals increases for each pathogen in each patch.
International Nuclear Information System (INIS)
Bystrenko, A.V.; Okhrimenko, I.P.
1993-01-01
The E 2-transition probabilities between the discrete and continuous spectrum states in 20 Ne are investigated using the two-channel version (making allowance for the connection between cluster and quadrupole channels) of the consistent microscopic approach,an algebraic version of the resonating-group method. The correctness of the approximation of the continuous spectrum by the discrete states, which is usual in collective models, the quadrupole sum rule and the giant quadrupole resonance phenomenon are considered. (author). 2 tab., 12 figs
Distinguishing Hidden Markov Chains
Kiefer, Stefan; Sistla, A. Prasad
2015-01-01
Hidden Markov Chains (HMCs) are commonly used mathematical models of probabilistic systems. They are employed in various fields such as speech recognition, signal processing, and biological sequence analysis. We consider the problem of distinguishing two given HMCs based on an observation sequence that one of the HMCs generates. More precisely, given two HMCs and an observation sequence, a distinguishing algorithm is expected to identify the HMC that generates the observation sequence. Two HM...
Fannes, Mark; Wouters, Jeroen
2012-01-01
We study a quantum process that can be considered as a quantum analogue for the classical Markov process. We specifically construct a version of these processes for free Fermions. For such free Fermionic processes we calculate the entropy density. This can be done either directly using Szeg\\"o's theorem for asymptotic densities of functions of Toeplitz matrices, or through an extension of said theorem to rates of functions, which we present in this article.
Ciampi, Antonio; Dyachenko, Alina; Cole, Martin; McCusker, Jane
2011-12-01
The study of mental disorders in the elderly presents substantial challenges due to population heterogeneity, coexistence of different mental disorders, and diagnostic uncertainty. While reliable tools have been developed to collect relevant data, new approaches to study design and analysis are needed. We focus on a new analytic approach. Our framework is based on latent class analysis and hidden Markov chains. From repeated measurements of a multivariate disease index, we extract the notion of underlying state of a patient at a time point. The course of the disorder is then a sequence of transitions among states. States and transitions are not observable; however, the probability of being in a state at a time point, and the transition probabilities from one state to another over time can be estimated. Data from 444 patients with and without diagnosis of delirium and dementia were available from a previous study. The Delirium Index was measured at diagnosis, and at 2 and 6 months from diagnosis. Four latent classes were identified: fairly healthy, moderately ill, clearly sick, and very sick. Dementia and delirium could not be separated on the basis of these data alone. Indeed, as the probability of delirium increased, so did the probability of decline of mental functions. Eight most probable courses were identified, including good and poor stable courses, and courses exhibiting various patterns of improvement. Latent class analysis and hidden Markov chains offer a promising tool for studying mental disorders in the elderly. Its use may show its full potential as new data become available.
Effects of tour boats on dolphin activity examined with sensitivity analysis of Markov chains.
Dans, Silvana Laura; Degrati, Mariana; Pedraza, Susana Noemí; Crespo, Enrique Alberto
2012-08-01
In Patagonia, Argentina, watching dolphins, especially dusky dolphins (Lagenorhynchus obscurus), is a new tourist activity. Feeding time decreases and time to return to feeding after feeding is abandoned and time it takes a group of dolphins to feed increase in the presence of boats. Such effects on feeding behavior may exert energetic costs on dolphins and thus reduce an individual's survival and reproductive capacity or maybe associated with shifts in distribution. We sought to predict which behavioral changes modify the activity pattern of dolphins the most. We modeled behavioral sequences of dusky dolphins with Markov chains. We calculated transition probabilities from one activity to another and arranged them in a stochastic matrix model. The proportion of time dolphins dedicated to a given activity (activity budget) and the time it took a dolphin to resume that activity after it had been abandoned (recurrence time) were calculated. We used a sensitivity analysis of Markov chains to calculate the sensitivity of the time budget and the activity-resumption time to changes in behavioral transition probabilities. Feeding-time budget was most sensitive to changes in the probability of dolphins switching from traveling to feeding behavior and of maintaining feeding behavior. Thus, an increase in these probabilities would be associated with the largest reduction in the time dedicated to feeding. A reduction in the probability of changing from traveling to feeding would also be associated with the largest increases in the time it takes dolphins to resume feeding. To approach dolphins when they are traveling would not affect behavior less because presence of the boat may keep dolphins from returning to feeding. Our results may help operators of dolphin-watching vessels minimize negative effects on dolphins. ©2012 Society for Conservation Biology.
Approximate quantum Markov chains
Sutter, David
2018-01-01
This book is an introduction to quantum Markov chains and explains how this concept is connected to the question of how well a lost quantum mechanical system can be recovered from a correlated subsystem. To achieve this goal, we strengthen the data-processing inequality such that it reveals a statement about the reconstruction of lost information. The main difficulty in order to understand the behavior of quantum Markov chains arises from the fact that quantum mechanical operators do not commute in general. As a result we start by explaining two techniques of how to deal with non-commuting matrices: the spectral pinching method and complex interpolation theory. Once the reader is familiar with these techniques a novel inequality is presented that extends the celebrated Golden-Thompson inequality to arbitrarily many matrices. This inequality is the key ingredient in understanding approximate quantum Markov chains and it answers a question from matrix analysis that was open since 1973, i.e., if Lieb's triple ma...
Probability, statistics, and computational science.
Beerenwinkel, Niko; Siebourg, Juliane
2012-01-01
In this chapter, we review basic concepts from probability theory and computational statistics that are fundamental to evolutionary genomics. We provide a very basic introduction to statistical modeling and discuss general principles, including maximum likelihood and Bayesian inference. Markov chains, hidden Markov models, and Bayesian network models are introduced in more detail as they occur frequently and in many variations in genomics applications. In particular, we discuss efficient inference algorithms and methods for learning these models from partially observed data. Several simple examples are given throughout the text, some of which point to models that are discussed in more detail in subsequent chapters.
Bridge Deterioration Prediction Model Based On Hybrid Markov-System Dynamic
Directory of Open Access Journals (Sweden)
Widodo Soetjipto Jojok
2017-01-01
Full Text Available Instantaneous bridge failure tends to increase in Indonesia. To mitigate this condition, Indonesia’s Bridge Management System (I-BMS has been applied to continuously monitor the condition of bridges. However, I-BMS only implements visual inspection for maintenance priority of the bridge structure component instead of bridge structure system. This paper proposes a new bridge failure prediction model based on hybrid Markov-System Dynamic (MSD. System dynamic is used to represent the correlation among bridge structure components while Markov chain is used to calculate temporal probability of the bridge failure. Around 235 data of bridges in Indonesia were collected from Directorate of Bridge the Ministry of Public Works and Housing for calculating transition probability of the model. To validate the model, a medium span concrete bridge was used as a case study. The result shows that the proposed model can accurately predict the bridge condition. Besides predicting the probability of the bridge failure, this model can also be used as an early warning system for bridge monitoring activity.
International Nuclear Information System (INIS)
Migdalek, J.
1984-01-01
The lowest 4fsup(n)6s-4fsup(n)6p transitions are studied for the Eu(II) (n=7), Tb(II) (n=9), and Ho(II) (n=11) spectra, where the J 1 J coupling is an acceptable approximation. The relativistic radial integrals, required to evaluate the oscillator strengths and transition probabilities, are calculated with the model-potential method, which includes also core-polarization effects. The similarities observed in oscillator strengths for transitions with given ΔJ but different J values are discussed and explained. The computed oscillator strengths are compared with those obtained with the Coulomb approximation and it is found that the latter are only 11-12% lower. The core polarization influence on oscillator strengths is also investigated and the 19-21% decrease in oscillator strengths due to this effect is predicted. This result may, however, be overestimated because of some deficiencies in our procedure. (author)
Switching Markov chains for a holistic modeling of SIS unavailability
International Nuclear Information System (INIS)
Mechri, Walid; Simon, Christophe; BenOthman, Kamel
2015-01-01
This paper proposes a holistic approach to model the Safety Instrumented Systems (SIS). The model is based on Switching Markov Chain and integrates several parameters like Common Cause Failure, Imperfect Proof testing, partial proof testing, etc. The basic concepts of Switching Markov Chain applied to reliability analysis are introduced and a model to compute the unavailability for a case study is presented. The proposed Switching Markov Chain allows us to assess the effect of each parameter on the SIS performance. The proposed method ensures the relevance of the results. - Highlights: • A holistic approach to model the unavailability safety systems using Switching Markov chains. • The model integrates several parameters like probability of failure due to the test, the probability of not detecting a failure in a test. • The basic concepts of the Switching Markov Chains are introduced and applied to compute the unavailability for safety systems. • The proposed Switching Markov Chain allows assessing the effect of each parameter on the chemical reactor performance
Detection of bursts in extracellular spike trains using hidden semi-Markov point process models.
Tokdar, Surya; Xi, Peiyi; Kelly, Ryan C; Kass, Robert E
2010-08-01
Neurons in vitro and in vivo have epochs of bursting or "up state" activity during which firing rates are dramatically elevated. Various methods of detecting bursts in extracellular spike trains have appeared in the literature, the most widely used apparently being Poisson Surprise (PS). A natural description of the phenomenon assumes (1) there are two hidden states, which we label "burst" and "non-burst," (2) the neuron evolves stochastically, switching at random between these two states, and (3) within each state the spike train follows a time-homogeneous point process. If in (2) the transitions from non-burst to burst and burst to non-burst states are memoryless, this becomes a hidden Markov model (HMM). For HMMs, the state transitions follow exponential distributions, and are highly irregular. Because observed bursting may in some cases be fairly regular-exhibiting inter-burst intervals with small variation-we relaxed this assumption. When more general probability distributions are used to describe the state transitions the two-state point process model becomes a hidden semi-Markov model (HSMM). We developed an efficient Bayesian computational scheme to fit HSMMs to spike train data. Numerical simulations indicate the method can perform well, sometimes yielding very different results than those based on PS.
A relation between non-Markov and Markov processes
International Nuclear Information System (INIS)
Hara, H.
1980-01-01
With the aid of a transformation technique, it is shown that some memory effects in the non-Markov processes can be eliminated. In other words, some non-Markov processes are rewritten in a form obtained by the random walk process; the Markov process. To this end, two model processes which have some memory or correlation in the random walk process are introduced. An explanation of the memory in the processes is given. (orig.)
Perturbation approach to scaled type Markov renewal processes with infinite mean
Pajor-Gyulai, Zsolt; Szász, Domokos
2010-01-01
Scaled type Markov renewal processes generalize classical renewal processes: renewal times come from a one parameter family of probability laws and the sequence of the parameters is the trajectory of an ergodic Markov chain. Our primary interest here is the asymptotic distribution of the Markovian parameter at time t \\to \\infty. The limit, of course, depends on the stationary distribution of the Markov chain. The results, however, are essentially different depending on whether the expectation...
The spectral method and ergodic theorems for general Markov chains
International Nuclear Information System (INIS)
Nagaev, S V
2015-01-01
We study the ergodic properties of Markov chains with an arbitrary state space and prove a geometric ergodic theorem. The method of the proof is new: it may be described as an operator method. Our main result is an ergodic theorem for Harris-Markov chains in the case when the return time to some fixed set has finite expectation. Our conditions for the transition function are more general than those used by Athreya-Ney and Nummelin. Unlike them, we impose restrictions not on the original transition function but on the transition function of an embedded Markov chain constructed from the return times to the fixed set mentioned above. The proof uses the spectral theory of linear operators on a Banach space
Al-Ma'shumah, Fathimah; Permana, Dony; Sidarto, Kuntjoro Adji
2015-12-01
Customer Lifetime Value is an important and useful concept in marketing. One of its benefits is to help a company for budgeting marketing expenditure for customer acquisition and customer retention. Many mathematical models have been introduced to calculate CLV considering the customer retention/migration classification scheme. A fairly new class of these models which will be described in this paper uses Markov Chain Models (MCM). This class of models has the major advantage for its flexibility to be modified to several different cases/classification schemes. In this model, the probabilities of customer retention and acquisition play an important role. From Pfeifer and Carraway, 2000, the final formula of CLV obtained from MCM usually contains nonlinear form of the transition probability matrix. This nonlinearity makes the inverse problem of CLV difficult to solve. This paper aims to solve this inverse problem, yielding the approximate transition probabilities for the customers, by applying metaheuristic optimization algorithm developed by Yang, 2013, Flower Pollination Algorithm. The major interpretation of obtaining the transition probabilities are to set goals for marketing teams in keeping the relative frequencies of customer acquisition and customer retention.
Classification of customer lifetime value models using Markov chain
Permana, Dony; Pasaribu, Udjianna S.; Indratno, Sapto W.; Suprayogi
2017-10-01
A firm’s potential reward in future time from a customer can be determined by customer lifetime value (CLV). There are some mathematic methods to calculate it. One method is using Markov chain stochastic model. Here, a customer is assumed through some states. Transition inter the states follow Markovian properties. If we are given some states for a customer and the relationships inter states, then we can make some Markov models to describe the properties of the customer. As Markov models, CLV is defined as a vector contains CLV for a customer in the first state. In this paper we make a classification of Markov Models to calculate CLV. Start from two states of customer model, we make develop in many states models. The development a model is based on weaknesses in previous model. Some last models can be expected to describe how real characters of customers in a firm.
Density Control of Multi-Agent Systems with Safety Constraints: A Markov Chain Approach
Demirer, Nazli
systems with a single agent and systems with large number of agents due to the probabilistic nature, where the probability distribution of each agent's state evolves according to a finite-state and discrete-time Markov chain (MC). Hence, designing proper decision control policies requires numerically tractable solution methods for the synthesis of Markov chains. The synthesis problem has the form of a Linear Matrix Inequality Problem (LMI), with LMI formulation of the constraints. To this end, we propose convex necessary and sufficient conditions for safety constraints in Markov chains, which is a novel result in the Markov chain literature. In addition to LMI-based, offline, Markov matrix synthesis method, we also propose a QP-based, online, method to compute a time-varying Markov matrix based on the real-time density feedback. Both problems are convex optimization problems that can be solved in a reliable and tractable way, utilizing existing tools in the literature. A Low Earth Orbit (LEO) swarm simulations are presented to validate the effectiveness of the proposed algorithms. Another problem tackled as a part of this research is the generalization of the density control problem to autonomous mobile agents with two control modes: ON and OFF. Here, each mode consists of a (possibly overlapping) finite set of actions, that is, there exist a set of actions for the ON mode and another set for the OFF mode. We give formulation for a new Markov chain synthesis problem, with additional measurements for the state transitions, where a policy is designed to ensure desired safety and convergence properties for the underlying Markov chain.
Mallak, Saed
1996-01-01
Ankara : Department of Mathematics and Institute of Engineering and Sciences of Bilkent University, 1996. Thesis (Master's) -- Bilkent University, 1996. Includes bibliographical references leaves leaf 29 In thi.s work, we studierl the Ergodicilv of Non-Stationary .Markov chains. We gave several e.xainples with different cases. We proved that given a sec[uence of Markov chains such that the limit of this sec|uence is an Ergodic Markov chain, then the limit of the combination ...
Water exchange traded funds: A study on idiosyncratic risk using Markov switching analysis
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Gurudeo Anand Tularam
2016-12-01
Full Text Available We investigate the relationship between idiosyncratic risk and return among four water exchange traded funds—PowerShares Water Resources Portfolio, Power Shares Global Water, First Trust ISE Water Index Fund, and Guggenheim S&P Global Water Index ETF using the Markov switching model for the period 2007–2015. The generated transition probabilities in this paper show that there is a high and low probability of switching between Regimes 1 and 3, respectively. Moreover, we find that the idiosyncratic risk for most of the exchange traded funds move from low volatility (Regime 2 to very low volatility (Regime 1 and 3. Our study also identify that the beta coefficients are positive and entire values are less than 1. Thus, it seems that water investment has a lower systematic risk and a positive effect on the water exchange traded index funds returns during different regimes.
Study of behavior and determination of customer lifetime value(CLV) using Markov chain model
Permana, Dony; Indratno, Sapto Wahyu; Pasaribu, Udjianna S.
2014-03-01
Customer Lifetime Value or CLV is a restriction on interactive marketing to help a company in arranging financial for the marketing of new customer acquisition and customer retention. Additionally CLV can be able to segment customers for financial arrangements. Stochastic models for the fairly new CLV used a Markov chain. In this model customer retention probability and new customer acquisition probability play an important role. This model is originally introduced by Pfeifer and Carraway in 2000 [1]. They introduced several CLV models, one of them only involves customer and former customer. In this paper we expand the model by adding the assumption of the transition from former customer to customer. In the proposed model, the CLV value is higher than the CLV value obtained by Pfeifer and Caraway model. But our model still requires a longer convergence time.
Study of behavior and determination of customer lifetime value(CLV) using Markov chain model
Energy Technology Data Exchange (ETDEWEB)
Permana, Dony, E-mail: donypermana@students.itb.ac.id [Statistics Research Division, Faculty of Mathematics and Natural Science, Bandung Institute of Technology, Indonesia and Statistics Study Program, Faculty of Mathematics and Natural Sciences, Padang State University (Indonesia); Indratno, Sapto Wahyu; Pasaribu, Udjianna S. [Statistics Research Division, Faculty of Mathematics and Natural Science, Bandung Institute of Technology (Indonesia)
2014-03-24
Customer Lifetime Value or CLV is a restriction on interactive marketing to help a company in arranging financial for the marketing of new customer acquisition and customer retention. Additionally CLV can be able to segment customers for financial arrangements. Stochastic models for the fairly new CLV used a Markov chain. In this model customer retention probability and new customer acquisition probability play an important role. This model is originally introduced by Pfeifer and Carraway in 2000 [1]. They introduced several CLV models, one of them only involves customer and former customer. In this paper we expand the model by adding the assumption of the transition from former customer to customer. In the proposed model, the CLV value is higher than the CLV value obtained by Pfeifer and Caraway model. But our model still requires a longer convergence time.
Study of behavior and determination of customer lifetime value(CLV) using Markov chain model
International Nuclear Information System (INIS)
Permana, Dony; Indratno, Sapto Wahyu; Pasaribu, Udjianna S.
2014-01-01
Customer Lifetime Value or CLV is a restriction on interactive marketing to help a company in arranging financial for the marketing of new customer acquisition and customer retention. Additionally CLV can be able to segment customers for financial arrangements. Stochastic models for the fairly new CLV used a Markov chain. In this model customer retention probability and new customer acquisition probability play an important role. This model is originally introduced by Pfeifer and Carraway in 2000 [1]. They introduced several CLV models, one of them only involves customer and former customer. In this paper we expand the model by adding the assumption of the transition from former customer to customer. In the proposed model, the CLV value is higher than the CLV value obtained by Pfeifer and Caraway model. But our model still requires a longer convergence time
Estimation and uncertainty of reversible Markov models.
Trendelkamp-Schroer, Benjamin; Wu, Hao; Paul, Fabian; Noé, Frank
2015-11-07
Reversibility is a key concept in Markov models and master-equation models of molecular kinetics. The analysis and interpretation of the transition matrix encoding the kinetic properties of the model rely heavily on the reversibility property. The estimation of a reversible transition matrix from simulation data is, therefore, crucial to the successful application of the previously developed theory. In this work, we discuss methods for the maximum likelihood estimation of transition matrices from finite simulation data and present a new algorithm for the estimation if reversibility with respect to a given stationary vector is desired. We also develop new methods for the Bayesian posterior inference of reversible transition matrices with and without given stationary vector taking into account the need for a suitable prior distribution preserving the meta-stable features of the observed process during posterior inference. All algorithms here are implemented in the PyEMMA software--http://pyemma.org--as of version 2.0.
Radford, Isolde H; Fersht, Alan R; Settanni, Giovanni
2011-06-09
Atomistic molecular dynamics simulations of the TZ1 beta-hairpin peptide have been carried out using an implicit model for the solvent. The trajectories have been analyzed using a Markov state model defined on the projections along two significant observables and a kinetic network approach. The Markov state model allowed for an unbiased identification of the metastable states of the system, and provided the basis for commitment probability calculations performed on the kinetic network. The kinetic network analysis served to extract the main transition state for folding of the peptide and to validate the results from the Markov state analysis. The combination of the two techniques allowed for a consistent and concise characterization of the dynamics of the peptide. The slowest relaxation process identified is the exchange between variably folded and denatured species, and the second slowest process is the exchange between two different subsets of the denatured state which could not be otherwise identified by simple inspection of the projected trajectory. The third slowest process is the exchange between a fully native and a partially folded intermediate state characterized by a native turn with a proximal backbone H-bond, and frayed side-chain packing and termini. The transition state for the main folding reaction is similar to the intermediate state, although a more native like side-chain packing is observed.
Hiligsmann, Mickaël; Ethgen, Olivier; Bruyère, Olivier; Richy, Florent; Gathon, Henry-Jean; Reginster, Jean-Yves
2009-01-01
Markov models are increasingly used in economic evaluations of treatments for osteoporosis. Most of the existing evaluations are cohort-based Markov models missing comprehensive memory management and versatility. In this article, we describe and validate an original Markov microsimulation model to accurately assess the cost-effectiveness of prevention and treatment of osteoporosis. We developed a Markov microsimulation model with a lifetime horizon and a direct health-care cost perspective. The patient history was recorded and was used in calculations of transition probabilities, utilities, and costs. To test the internal consistency of the model, we carried out an example calculation for alendronate therapy. Then, external consistency was investigated by comparing absolute lifetime risk of fracture estimates with epidemiologic data. For women at age 70 years, with a twofold increase in the fracture risk of the average population, the costs per quality-adjusted life-year gained for alendronate therapy versus no treatment were estimated at €9105 and €15,325, respectively, under full and realistic adherence assumptions. All the sensitivity analyses in terms of model parameters and modeling assumptions were coherent with expected conclusions and absolute lifetime risk of fracture estimates were within the range of previous estimates, which confirmed both internal and external consistency of the model. Microsimulation models present some major advantages over cohort-based models, increasing the reliability of the results and being largely compatible with the existing state of the art, evidence-based literature. The developed model appears to be a valid model for use in economic evaluations in osteoporosis.
Thermal properties and optical transition probabilities of Tm3 + doped TeO2-WO3 glass.
Cenk, S; Demirata, B; Oveçoglu, M L; Ozen, G
2001-10-01
Glasses with the composition of (1 - x)TeO2 + (x)WO3, where x = 0.15, 0.25 and 0.3 were prepared and, their thermal and absorption measurements were carried out. Differential thermal analysis (DTA) curves taken in the 23-600 degrees C temperature range with a heating rate of 10 degrees C/min reveal a change in the value of the glass transition temperature, Tg, while crystallization was not observed for the glasses containing a WO3 content of more than 15 mol%. All the glasses were found to be moisture-resistant. The absorption bands corresponding to the absorption of the 1G4, 3F2, 3F3 and 3F4, 3H5 and 3H4 levels from the 3H6 ground level of the Tm3+ ion were observed in the optical absorption spectra. Integrated absorption cross-sections of each band except that of 3H5 level was found to vary with the glass composition. Judd-Ofelt analysis was carried out for the samples doped with 1.0 mol% Tm2O3. The omega2 parameter shows the strongest dependence on the host composition and it increases with the increasing WO3 amount. The value of omega4 increases rather slowly while the value of omega6 is practically independent of the composition. The strong dependence of the parameter omega2 indicates that this parameter is related to the structural change and the symmetry of the local environment of the Tm3+ ions in this glass.
DEFF Research Database (Denmark)
Kohlenbach, Ulrich Wilhelm
2002-01-01
We show that the so-called weak Markov's principle (WMP) which states that every pseudo-positive real number is positive is underivable in E-HA + AC. Since allows one to formalize (atl eastl arge parts of) Bishop's constructive mathematics, this makes it unlikely that WMP can be proved within...... the framework of Bishop-style mathematics (which has been open for about 20 years). The underivability even holds if the ine.ective schema of full comprehension (in all types) for negated formulas (in particular for -free formulas) is added, which allows one to derive the law of excluded middle...
Dağlarli, Evren; Temeltaş, Hakan
2007-04-01
This paper presents artificial emotional system based autonomous robot control architecture. Hidden Markov model developed as mathematical background for stochastic emotional and behavior transitions. Motivation module of architecture considered as behavioral gain effect generator for achieving multi-objective robot tasks. According to emotional and behavioral state transition probabilities, artificial emotions determine sequences of behaviors. Also motivational gain effects of proposed architecture can be observed on the executing behaviors during simulation.
Zhang, Yue; Berhane, Kiros
2014-01-01
Questionnaire-based health status outcomes are often prone to misclassification. When studying the effect of risk factors on such outcomes, ignoring any potential misclassification may lead to biased effect estimates. Analytical challenges posed by these misclassified outcomes are further complicated when simultaneously exploring factors for both the misclassification and health processes in a multi-level setting. To address these challenges, we propose a fully Bayesian Mixed Hidden Markov Model (BMHMM) for handling differential misclassification in categorical outcomes in a multi-level setting. The BMHMM generalizes the traditional Hidden Markov Model (HMM) by introducing random effects into three sets of HMM parameters for joint estimation of the prevalence, transition and misclassification probabilities. This formulation not only allows joint estimation of all three sets of parameters, but also accounts for cluster level heterogeneity based on a multi-level model structure. Using this novel approach, both the true health status prevalence and the transition probabilities between the health states during follow-up are modeled as functions of covariates. The observed, possibly misclassified, health states are related to the true, but unobserved, health states and covariates. Results from simulation studies are presented to validate the estimation procedure, to show the computational efficiency due to the Bayesian approach and also to illustrate the gains from the proposed method compared to existing methods that ignore outcome misclassification and cluster level heterogeneity. We apply the proposed method to examine the risk factors for both asthma transition and misclassification in the Southern California Children's Health Study (CHS). PMID:24254432
An introduction to probability and stochastic processes
Melsa, James L
2013-01-01
Geared toward college seniors and first-year graduate students, this text is designed for a one-semester course in probability and stochastic processes. Topics covered in detail include probability theory, random variables and their functions, stochastic processes, linear system response to stochastic processes, Gaussian and Markov processes, and stochastic differential equations. 1973 edition.
The spectral method and the central limit theorem for general Markov chains
Nagaev, S. V.
2017-12-01
We consider Markov chains with an arbitrary phase space and develop a modification of the spectral method that enables us to prove the central limit theorem (CLT) for non-uniformly ergodic Markov chains. The conditions imposed on the transition function are more general than those by Athreya-Ney and Nummelin. Our proof of the CLT is purely analytical.
STATISTICAL ANALYSIS OF NOTATIONAL AFL DATA USING CONTINUOUS TIME MARKOV CHAINS
Directory of Open Access Journals (Sweden)
Denny Meyer
2006-12-01
Full Text Available Animal biologists commonly use continuous time Markov chain models to describe patterns of animal behaviour. In this paper we consider the use of these models for describing AFL football. In particular we test the assumptions for continuous time Markov chain models (CTMCs, with time, distance and speed values associated with each transition. Using a simple event categorisation it is found that a semi-Markov chain model is appropriate for this data. This validates the use of Markov Chains for future studies in which the outcomes of AFL matches are simulated
Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain
Dai, Yonghui; Han, Dongmei; Dai, Weihui
2014-01-01
The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market. PMID:24782659
Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain
Directory of Open Access Journals (Sweden)
Yonghui Dai
2014-01-01
Full Text Available The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market.
Neyman, Markov processes and survival analysis.
Yang, Grace
2013-07-01
J. Neyman used stochastic processes extensively in his applied work. One example is the Fix and Neyman (F-N) competing risks model (1951) that uses finite homogeneous Markov processes to analyse clinical trials with breast cancer patients. We revisit the F-N model, and compare it with the Kaplan-Meier (K-M) formulation for right censored data. The comparison offers a way to generalize the K-M formulation to include risks of recovery and relapses in the calculation of a patient's survival probability. The generalization is to extend the F-N model to a nonhomogeneous Markov process. Closed-form solutions of the survival probability are available in special cases of the nonhomogeneous processes, like the popular multiple decrement model (including the K-M model) and Chiang's staging model, but these models do not consider recovery and relapses while the F-N model does. An analysis of sero-epidemiology current status data with recurrent events is illustrated. Fix and Neyman used Neyman's RBAN (regular best asymptotic normal) estimates for the risks, and provided a numerical example showing the importance of considering both the survival probability and the length of time of a patient living a normal life in the evaluation of clinical trials. The said extension would result in a complicated model and it is unlikely to find analytical closed-form solutions for survival analysis. With ever increasing computing power, numerical methods offer a viable way of investigating the problem.
Directory of Open Access Journals (Sweden)
M. V. Serzhantova
2016-05-01
Full Text Available Subject of Research. We analyze the problems of finite Markov chains apparatus application for simulating a human operator activity in the quasi-static functional environment. It is shown that the functional environment stochastic nature is generated by a factor of interval character of human operator properties. Method. The problem is solved in the class of regular (recurrent finite Markov chains with three states of the human operator: with a favorable, median and unfavorable combination of the values of mathematical model parameters of the human operator in a quasi-static functional environment. The finite Markov chain is designed taking into account the factors of human operator tiredness and interval character of parameters of the model representation of his properties. The device is based on the usage of mathematical approximation of the standard curve of the human operator activity performance during work shift. The standard curve of the human operator activity performance is based on the extensive research experience of functional activity of the human operator with the help of photos of the day, his action timing and ergonomic generalizations. Main Results. The apparatus of regular finite Markov chains gave the possibility to evaluate correctly the human operator activity performance in a quasi-static functional environment with the use of the main information component of these chains as a vector of final probabilities. In addition, we managed to build an algorithmic basis for estimating the stationary time (time study for transit of human operator from arbitrary initial functional state into a state corresponding to a vector of final probabilities for a used chain after it reaches the final state based on the analysis of the eigenvalues spectrum of the matrix of transition probabilities for a regular (recurrent finite Markov chain. Practical Relevance. Obtained theoretical results are confirmed by illustrative examples, which
Comparison of the kinetics of different Markov models for ligand binding under varying conditions
International Nuclear Information System (INIS)
Martini, Johannes W. R.; Habeck, Michael
2015-01-01
We recently derived a Markov model for macromolecular ligand binding dynamics from few physical assumptions and showed that its stationary distribution is the grand canonical ensemble [J. W. R. Martini, M. Habeck, and M. Schlather, J. Math. Chem. 52, 665 (2014)]. The transition probabilities of the proposed Markov process define a particular Glauber dynamics and have some similarity to the Metropolis-Hastings algorithm. Here, we illustrate that this model is the stochastic analog of (pseudo) rate equations and the corresponding system of differential equations. Moreover, it can be viewed as a limiting case of general stochastic simulations of chemical kinetics. Thus, the model links stochastic and deterministic approaches as well as kinetics and equilibrium described by the grand canonical ensemble. We demonstrate that the family of transition matrices of our model, parameterized by temperature and ligand activity, generates ligand binding kinetics that respond to changes in these parameters in a qualitatively similar way as experimentally observed kinetics. In contrast, neither the Metropolis-Hastings algorithm nor the Glauber heat bath reflects changes in the external conditions correctly. Both converge rapidly to the stationary distribution, which is advantageous when the major interest is in the equilibrium state, but fail to describe the kinetics of ligand binding realistically. To simulate cellular processes that involve the reversible stochastic binding of multiple factors, our pseudo rate equation model should therefore be preferred to the Metropolis-Hastings algorithm and the Glauber heat bath, if the stationary distribution is not of only interest
Comparison of the kinetics of different Markov models for ligand binding under varying conditions
Energy Technology Data Exchange (ETDEWEB)
Martini, Johannes W. R., E-mail: jmartin2@gwdg.de [Max Planck Institute for Developmental Biology, Tübingen (Germany); Felix Bernstein Institute for Mathematical Statistics in the Biosciences, University of Göttingen, Göttingen (Germany); Habeck, Michael, E-mail: mhabeck@gwdg.de [Felix Bernstein Institute for Mathematical Statistics in the Biosciences, University of Göttingen, Göttingen (Germany); Max Planck Institute for Biophysical Chemistry, Göttingen (Germany)
2015-03-07
We recently derived a Markov model for macromolecular ligand binding dynamics from few physical assumptions and showed that its stationary distribution is the grand canonical ensemble [J. W. R. Martini, M. Habeck, and M. Schlather, J. Math. Chem. 52, 665 (2014)]. The transition probabilities of the proposed Markov process define a particular Glauber dynamics and have some similarity to the Metropolis-Hastings algorithm. Here, we illustrate that this model is the stochastic analog of (pseudo) rate equations and the corresponding system of differential equations. Moreover, it can be viewed as a limiting case of general stochastic simulations of chemical kinetics. Thus, the model links stochastic and deterministic approaches as well as kinetics and equilibrium described by the grand canonical ensemble. We demonstrate that the family of transition matrices of our model, parameterized by temperature and ligand activity, generates ligand binding kinetics that respond to changes in these parameters in a qualitatively similar way as experimentally observed kinetics. In contrast, neither the Metropolis-Hastings algorithm nor the Glauber heat bath reflects changes in the external conditions correctly. Both converge rapidly to the stationary distribution, which is advantageous when the major interest is in the equilibrium state, but fail to describe the kinetics of ligand binding realistically. To simulate cellular processes that involve the reversible stochastic binding of multiple factors, our pseudo rate equation model should therefore be preferred to the Metropolis-Hastings algorithm and the Glauber heat bath, if the stationary distribution is not of only interest.
Merica, H
2011-01-01
Little attention has gone into linking to its neuronal substrates the dynamic structure of non-rapid-eye-movement (NREM) sleep, defined as the pattern of time-course power in all frequency bands across an entire episode. Using the spectral power time-courses in the sleep electroencephalogram (EEG), we showed in the typical first episode, several moves towards-and-away from deep sleep, each having an identical pattern linking the major frequency bands beta, sigma and delta. The neuronal transition probability model (NTP) - in fitting the data well - successfully explained the pattern as resulting from stochastic transitions of the firing-rates of the thalamically-projecting brainstem-activating neurons, alternating between two steady dynamic-states (towards-and-away from deep sleep) each initiated by a so-far unidentified flip-flop. The aims here are to identify this flip-flop and to demonstrate that the model fits well all NREM episodes, not just the first. Using published data on suprachiasmatic nucleus (SCN...
International Nuclear Information System (INIS)
Spear, R.H.
1988-11-01
Adopted values for the excitation energy, E x( 3 1 - ), of the first 3 - state of the even-even nuclei are tabulated. Values of the reduced electric-octupole transition probability, B(E3;O 1 + → 3 1 - ), from the ground state to this state, as determined from Coulomb excitation, lifetime measurements, inelastic electron scattering, deformation parameters β 3 obtained from angular distributions of inelastically scattered nucleons and light ions, and other miscellaneous procedures are listed in separate Tables. Adopted values for B(E3; O 1 + → 3 1 - ) are presented in Table VII, together with the E3 transition strengths, in Weisskopf units, and the product E x( 3 1 - ) x B(E3; O 1 + → 3 1 - - ) expressed as a percentage of the energy-weighted E3 sum-rule strength. An evaluation is made of the reliability of B(E3; O 1 + → 3 1 - ) values deduced from deformation parameters β 3 . The literature has been covered to March 1988
Directory of Open Access Journals (Sweden)
Sean S Downey
Full Text Available Analysis of the proportion of immature skeletons recovered from European prehistoric cemeteries has shown that the transition to agriculture after 9000 BP triggered a long-term increase in human fertility. Here we compare the largest analysis of European cemeteries to date with an independent line of evidence, the summed calibrated date probability distribution of radiocarbon dates (SCDPD from archaeological sites. Our cemetery reanalysis confirms increased growth rates after the introduction of agriculture; the radiocarbon analysis also shows this pattern, and a significant correlation between both lines of evidence confirms the demographic validity of SCDPDs. We analyze the areal extent of Neolithic enclosures and demographic data from ethnographically known farming and foraging societies and we estimate differences in population levels at individual sites. We find little effect on the overall shape and precision of the SCDPD and we observe a small increase in the correlation with the cemetery trends. The SCDPD analysis supports the hypothesis that the transition to agriculture dramatically increased demographic growth, but it was followed within centuries by a general pattern of collapse even after accounting for higher settlement densities during the Neolithic. The study supports the unique contribution of SCDPDs as a valid demographic proxy for the demographic patterns associated with early agriculture.
Markov Chain Analysis of Musical Dice Games
Volchenkov, D.; Dawin, J. R.
2012-07-01
A system for using dice to compose music randomly is known as the musical dice game. The discrete time MIDI models of 804 pieces of classical music written by 29 composers have been encoded into the transition matrices and studied by Markov chains. Contrary to human languages, entropy dominates over redundancy, in the musical dice games based on the compositions of classical music. The maximum complexity is achieved on the blocks consisting of just a few notes (8 notes, for the musical dice games generated over Bach's compositions). First passage times to notes can be used to resolve tonality and feature a composer.
Berman-Konsowa principle for reversible Markov jump processes
Hollander, den W.Th.F.; Jansen, S.
2013-01-01
In this paper we prove a version of the Berman-Konsowa principle for reversible Markov jump processes on Polish spaces. The Berman-Konsowa principle provides a variational formula for the capacity of a pair of disjoint measurable sets. There are two versions, one involving a class of probability
Efficient Modelling and Generation of Markov Automata (extended version)
Timmer, Mark; Katoen, Joost P.; van de Pol, Jan Cornelis; Stoelinga, Mariëlle Ida Antoinette
2012-01-01
This paper introduces a framework for the efficient modelling and generation of Markov automata. It consists of (1) the data-rich process-algebraic language MAPA, allowing concise modelling of systems with nondeterminism, probability and Markovian timing; (2) a restricted form of the language, the
A Markov decision process for managing habitat for Florida scrub-jays
Johnson, Fred A.; Breininger, David R.; Duncan, Brean W.; Nichols, James D.; Runge, Michael C.; Williams, B. Ken
2011-01-01
Florida scrub-jays Aphelocoma coerulescens are listed as threatened under the Endangered Species Act due to loss and degradation of scrub habitat. This study concerned the development of an optimal strategy for the restoration and management of scrub habitat at Merritt Island National Wildlife Refuge, which contains one of the few remaining large populations of scrub-jays in Florida. There are documented differences in the reproductive and survival rates of scrubjays among discrete classes of scrub height (Markov models to estimate annual transition probabilities among the four scrub-height classes under three possible management actions: scrub restoration (mechanical cutting followed by burning), a prescribed burn, or no intervention. A strategy prescribing the optimal management action for management units exhibiting different proportions of scrub-height classes was derived using dynamic programming. Scrub restoration was the optimal management action only in units dominated by mixed and tall scrub, and burning tended to be the optimal action for intermediate levels of short scrub. The optimal action was to do nothing when the amount of short scrub was greater than 30%, because short scrub mostly transitions to optimal height scrub (i.e., that state with the highest demographic success of scrub-jays) in the absence of intervention. Monte Carlo simulation of the optimal policy suggested that some form of management would be required every year. We note, however, that estimates of scrub-height transition probabilities were subject to several sources of uncertainty, and so we explored the management implications of alternative sets of transition probabilities. Generally, our analysis demonstrated the difficulty of managing for a species that requires midsuccessional habitat, and suggests that innovative management tools may be needed to help ensure the persistence of scrub-jays at Merritt Island National Wildlife Refuge. The development of a tailored monitoring
[Succession caused by beaver (Castor fiber L.) life activity: II. A refined Markov model].
Logofet; Evstigneev, O I; Aleinikov, A A; Morozova, A O
2015-01-01
The refined Markov model of cyclic zoogenic successions caused by beaver (Castor fiber L.) life activity represents a discrete chain of the following six states: flooded forest, swamped forest, pond, grassy swamp, shrubby swamp, and wet forest, which correspond to certain stages of succession. Those stages are defined, and a conceptual scheme of probable transitions between them for one time step is constructed from the knowledge of beaver behaviour in small river floodplains of "Bryanskii Les" Reserve. We calibrated the corresponding matrix of transition probabilities according to the optimization principle: minimizing differences between the model outcome and reality; the model generates a distribution of relative areas corresponding to the stages of succession, that has to be compared to those gained from case studies in the Reserve during 2002-2006. The time step is chosen to equal 2 years, and the first-step data in the sum of differences are given various weights, w (between 0 and 1). The value of w = 0.2 is selected due to its optimality and for some additional reasons. By the formulae of finite homogeneous Markov chain theory, we obtained the main results of the calibrated model, namely, a steady-state distribution of stage areas, indexes of cyclicity, and the mean durations (M(j)) of succession stages. The results of calibration give an objective quantitative nature to the expert knowledge of the course of succession and get a proper interpretation. The 2010 data, which are not involved in the calibration procedure, enabled assessing the quality of prediction by the homogeneous model in short-term (from the 2006 situation): the error of model area distribution relative to the distribution observed in 2010 falls into the range of 9-17%, the best prognosis being given by the least optimal matrices (rejected values of w). This indicates a formally heterogeneous nature of succession processes in time. Thus, the refined version of the homogeneous Markov chain
Milestoning with transition memory
Hawk, Alexander T.; Makarov, Dmitrii E.
2011-12-01
Milestoning is a method used to calculate the kinetics and thermodynamics of molecular processes occurring on time scales that are not accessible to brute force molecular dynamics (MD). In milestoning, the conformation space of the system is sectioned by hypersurfaces (milestones), an ensemble of trajectories is initialized on each milestone, and MD simulations are performed to calculate transitions between milestones. The transition probabilities and transition time distributions are then used to model the dynamics of the system with a Markov renewal process, wherein a long trajectory of the system is approximated as a succession of independent transitions between milestones. This approximation is justified if the transition probabilities and transition times are statistically independent. In practice, this amounts to a requirement that milestones are spaced such that trajectories lose position and velocity memory between subsequent transitions. Unfortunately, limiting the number of milestones limits both the resolution at which a system's properties can be analyzed, and the computational speedup achieved by the method. We propose a generalized milestoning procedure, milestoning with transition memory (MTM), which accounts for memory of previous transitions made by the system. When a reaction coordinate is used to define the milestones, the MTM procedure can be carried out at no significant additional expense as compared to conventional milestoning. To test MTM, we have applied its version that allows for the memory of the previous step to the toy model of a polymer chain undergoing Langevin dynamics in solution. We have computed the mean first passage time for the chain to attain a cyclic conformation and found that the number of milestones that can be used, without incurring significant errors in the first passage time is at least 8 times that permitted by conventional milestoning. We further demonstrate that, unlike conventional milestoning, MTM permits
A basic course in probability theory
Bhattacharya, Rabi
2016-01-01
This text develops the necessary background in probability theory underlying diverse treatments of stochastic processes and their wide-ranging applications. In this second edition, the text has been reorganized for didactic purposes, new exercises have been added and basic theory has been expanded. General Markov dependent sequences and their convergence to equilibrium is the subject of an entirely new chapter. The introduction of conditional expectation and conditional probability very early in the text maintains the pedagogic innovation of the first edition; conditional expectation is illustrated in detail in the context of an expanded treatment of martingales, the Markov property, and the strong Markov property. Weak convergence of probabilities on metric spaces and Brownian motion are two topics to highlight. A selection of large deviation and/or concentration inequalities ranging from those of Chebyshev, Cramer–Chernoff, Bahadur–Rao, to Hoeffding have been added, with illustrative comparisons of thei...
The Consensus String Problem and the Complexity of Comparing Hidden Markov Models
DEFF Research Database (Denmark)
Lyngsø, Rune Bang; Pedersen, Christian Nørgaard Storm
2002-01-01
The basic theory of hidden Markov models was developed and applied to problems in speech recognition in the late 1960s, and has since then been applied to numerous problems, e.g. biological sequence analysis. Most applications of hidden Markov models are based on efficient algorithms for computing...... the probability of generating a given string, or computing the most likely path generating a given string. In this paper we consider the problem of computing the most likely string, or consensus string, generated by a given model, and its implications on the complexity of comparing hidden Markov models. We show...... that computing the consensus string, and approximating its probability within any constant factor, is NP-hard, and that the same holds for the closely related labeling problem for class hidden Markov models. Furthermore, we establish the NP-hardness of comparing two hidden Markov models under the L∞- and L1...
On the Total Variation Distance of Semi-Markov Chains
DEFF Research Database (Denmark)
Bacci, Giorgio; Bacci, Giovanni; Larsen, Kim Guldstrand
2015-01-01
Semi-Markov chains (SMCs) are continuous-time probabilistic transition systems where the residence time on states is governed by generic distributions on the positive real line. This paper shows the tight relation between the total variation distance on SMCs and their model checking problem over...
Markov queue game with virtual reality strategies | Nwobi-Okoye ...
African Journals Online (AJOL)
A non cooperative markov game with several unique characteristics was introduced. Some of these characteristics include: the existence of a single phase multi server queuing model and markovian transition matrix/matrices for each game, introduction of virtual situations (virtual reality) or dummies to improve the chances ...
Prediction of beta-turns in proteins using the first-order Markov models.
Lin, Thy-Hou; Wang, Ging-Ming; Wang, Yen-Tseng
2002-01-01
We present a method based on the first-order Markov models for predicting simple beta-turns and loops containing multiple turns in proteins. Sequences of 338 proteins in a database are divided using the published turn criteria into the following three regions, namely, the turn, the boundary, and the nonturn ones. A transition probability matrix is constructed for either the turn or the nonturn region using the weighted transition probabilities computed for dipeptides identified from each region. There are two such matrices constructed for the boundary region since the transition probabilities for dipeptides immediately preceding or following a turn are different. The window used for scanning a protein sequence from amino (N-) to carboxyl (C-) terminal is a hexapeptide since the transition probability computed for a turn tetrapeptide is capped at both the N- and C- termini with a boundary transition probability indexed respectively from the two boundary transition matrices. A sum of the averaged product of the transition probabilities of all the hexapeptides involving each residue is computed. This is then weighted with a probability computed from assuming that all the hexapeptides are from the nonturn region to give the final prediction quantity. Both simple beta-turns and loops containing multiple turns in a protein are then identified by the rising of the prediction quantity computed. The performance of the prediction scheme or the percentage (%) of correct prediction is evaluated through computation of Matthews correlation coefficients for each protein predicted. It is found that the prediction method is capable of giving prediction results with better correlation between the percent of correct prediction and the Matthews correlation coefficients for a group of test proteins as compared with those predicted using some secondary structural prediction methods. The prediction accuracy for about 40% of proteins in the database or 50% of proteins in the test set is
Relative transition probabilities for krypton.
Miller, M. H.; Roig, R. A.; Bengtson, R. D.
1972-01-01
First experimental line strength data for the visible Kr II lines and for several of the more prominent Kr I lines are given. The spectroscopic light source used is the thermal plasma behind the reflected shock wave in a gas-driven shock tube. A 3/4-m spectrograph and a 1-m spectrograph were employed simultaneously to provide redundant photometry. The data are compared with other measurements and with theoretical calculations.
Regeneration and general Markov chains
Directory of Open Access Journals (Sweden)
Vladimir V. Kalashnikov
1994-01-01
Full Text Available Ergodicity, continuity, finite approximations and rare visits of general Markov chains are investigated. The obtained results permit further quantitative analysis of characteristics, such as, rates of convergence, continuity (measured as a distance between perturbed and non-perturbed characteristics, deviations between Markov chains, accuracy of approximations and bounds on the distribution function of the first visit time to a chosen subset, etc. The underlying techniques use the embedding of the general Markov chain into a wide sense regenerative process with the help of splitting construction.
Markov chains theory and applications
Sericola, Bruno
2013-01-01
Markov chains are a fundamental class of stochastic processes. They are widely used to solve problems in a large number of domains such as operational research, computer science, communication networks and manufacturing systems. The success of Markov chains is mainly due to their simplicity of use, the large number of available theoretical results and the quality of algorithms developed for the numerical evaluation of many metrics of interest.The author presents the theory of both discrete-time and continuous-time homogeneous Markov chains. He carefully examines the explosion phenomenon, the
Quadratic Variation by Markov Chains
DEFF Research Database (Denmark)
Hansen, Peter Reinhard; Horel, Guillaume
We introduce a novel estimator of the quadratic variation that is based on the the- ory of Markov chains. The estimator is motivated by some general results concerning filtering contaminated semimartingales. Specifically, we show that filtering can in prin- ciple remove the effects of market...... microstructure noise in a general framework where little is assumed about the noise. For the practical implementation, we adopt the dis- crete Markov chain model that is well suited for the analysis of financial high-frequency prices. The Markov chain framework facilitates simple expressions and elegant analyti...
Hidden Markov processes theory and applications to biology
Vidyasagar, M
2014-01-01
This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. The book starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are t
Approximating Markov Chains: What and why
International Nuclear Information System (INIS)
Pincus, S.
1996-01-01
Much of the current study of dynamical systems is focused on geometry (e.g., chaos and bifurcations) and ergodic theory. Yet dynamical systems were originally motivated by an attempt to open-quote open-quote solve,close-quote close-quote or at least understand, a discrete-time analogue of differential equations. As such, numerical, analytical solution techniques for dynamical systems would seem desirable. We discuss an approach that provides such techniques, the approximation of dynamical systems by suitable finite state Markov Chains. Steady state distributions for these Markov Chains, a straightforward calculation, will converge to the true dynamical system steady state distribution, with appropriate limit theorems indicated. Thus (i) approximation by a computable, linear map holds the promise of vastly faster steady state solutions for nonlinear, multidimensional differential equations; (ii) the solution procedure is unaffected by the presence or absence of a probability density function for the attractor, entirely skirting singularity, fractal/multifractal, and renormalization considerations. The theoretical machinery underpinning this development also implies that under very general conditions, steady state measures are weakly continuous with control parameter evolution. This means that even though a system may change periodicity, or become chaotic in its limiting behavior, such statistical parameters as the mean, standard deviation, and tail probabilities change continuously, not abruptly with system evolution. copyright 1996 American Institute of Physics
Confluence reduction for Markov automata
Timmer, Mark; van de Pol, Jan Cornelis; Stoelinga, Mariëlle Ida Antoinette
Markov automata are a novel formalism for specifying systems exhibiting nondeterminism, probabilistic choices and Markovian rates. Recently, the process algebra MAPA was introduced to efficiently model such systems. As always, the state space explosion threatens the analysability of the models
Confluence Reduction for Markov Automata
Timmer, Mark; van de Pol, Jan Cornelis; Stoelinga, Mariëlle Ida Antoinette; Braberman, Victor; Fribourg, Laurent
Markov automata are a novel formalism for specifying systems exhibiting nondeterminism, probabilistic choices and Markovian rates. Recently, the process algebra MAPA was introduced to efficiently model such systems. As always, the state space explosion threatens the analysability of the models
Parameterizing the Spatial Markov Model From Breakthrough Curve Data Alone
Sherman, Thomas; Fakhari, Abbas; Miller, Savannah; Singha, Kamini; Bolster, Diogo
2017-12-01
The spatial Markov model (SMM) is an upscaled Lagrangian model that effectively captures anomalous transport across a diverse range of hydrologic systems. The distinct feature of the SMM relative to other random walk models is that successive steps are correlated. To date, with some notable exceptions, the model has primarily been applied to data from high-resolution numerical simulations and correlation effects have been measured from simulated particle trajectories. In real systems such knowledge is practically unattainable and the best one might hope for is breakthrough curves (BTCs) at successive downstream locations. We introduce a novel methodology to quantify velocity correlation from BTC data alone. By discretizing two measured BTCs into a set of arrival times and developing an inverse model, we estimate velocity correlation, thereby enabling parameterization of the SMM in studies where detailed Lagrangian velocity statistics are unavailable. The proposed methodology is applied to two synthetic numerical problems, where we measure all details and thus test the veracity of the approach by comparison of estimated parameters with known simulated values. Our results suggest that our estimated transition probabilities agree with simulated values and using the SMM with this estimated parameterization accurately predicts BTCs downstream. Our methodology naturally allows for estimates of uncertainty by calculating lower and upper bounds of velocity correlation, enabling prediction of a range of BTCs. The measured BTCs fall within the range of predicted BTCs. This novel method to parameterize the SMM from BTC data alone is quite parsimonious, thereby widening the SMM's practical applicability.
Optical character recognition of handwritten Arabic using hidden Markov models
Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.; Olama, Mohammed M.
2011-04-01
The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language is initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.
Bayesian inference for Markov jump processes with informative observations.
Golightly, Andrew; Wilkinson, Darren J
2015-04-01
In this paper we consider the problem of parameter inference for Markov jump process (MJP) representations of stochastic kinetic models. Since transition probabilities are intractable for most processes of interest yet forward simulation is straightforward, Bayesian inference typically proceeds through computationally intensive methods such as (particle) MCMC. Such methods ostensibly require the ability to simulate trajectories from the conditioned jump process. When observations are highly informative, use of the forward simulator is likely to be inefficient and may even preclude an exact (simulation based) analysis. We therefore propose three methods for improving the efficiency of simulating conditioned jump processes. A conditioned hazard is derived based on an approximation to the jump process, and used to generate end-point conditioned trajectories for use inside an importance sampling algorithm. We also adapt a recently proposed sequential Monte Carlo scheme to our problem. Essentially, trajectories are reweighted at a set of intermediate time points, with more weight assigned to trajectories that are consistent with the next observation. We consider two implementations of this approach, based on two continuous approximations of the MJP. We compare these constructs for a simple tractable jump process before using them to perform inference for a Lotka-Volterra system. The best performing construct is used to infer the parameters governing a simple model of motility regulation in Bacillus subtilis.
Simulating reservoir lithologies by an actively conditioned Markov chain model
Feng, Runhai; Luthi, Stefan M.; Gisolf, Dries
2018-06-01
The coupled Markov chain model can be used to simulate reservoir lithologies between wells, by conditioning them on the observed data in the cored wells. However, with this method, only the state at the same depth as the current cell is going to be used for conditioning, which may be a problem if the geological layers are dipping. This will cause the simulated lithological layers to be broken or to become discontinuous across the reservoir. In order to address this problem, an actively conditioned process is proposed here, in which a tolerance angle is predefined. The states contained in the region constrained by the tolerance angle will be employed for conditioning in the horizontal chain first, after which a coupling concept with the vertical chain is implemented. In order to use the same horizontal transition matrix for different future states, the tolerance angle has to be small. This allows the method to work in reservoirs without complex structures caused by depositional processes or tectonic deformations. Directional artefacts in the modeling process are avoided through a careful choice of the simulation path. The tolerance angle and dipping direction of the strata can be obtained from a correlation between wells, or from seismic data, which are available in most hydrocarbon reservoirs, either by interpretation or by inversion that can also assist the construction of a horizontal probability matrix.
International Nuclear Information System (INIS)
Lee, Youn Myoung
1995-02-01
As a newly approaching model, a stochastic model using continuous time Markov process for nuclide decay chain transport of arbitrary length in the fractured porous rock medium has been proposed, by which the need for solving a set of partial differential equations corresponding to various sets of side conditions can be avoided. Once the single planar fracture in the rock matrix is represented by a series of finite number of compartments having region wise constant parameter values in them, the medium is continuous in view of various processes associated with nuclide transport but discrete in medium space and such geologic system is assumed to have Markov property, since the Markov process requires that only the present value of the time dependent random variable be known to determine the future value of random variable, nuclide transport in the medium can then be modeled as a continuous time Markov process. Processes that are involved in nuclide transport are advective transport due to groundwater flow, diffusion into the rock matrix, adsorption onto the wall of the fracture and within the pores in the rock matrix, and radioactive decay chain. The transition probabilities for nuclide from the transition intensities between and out of the compartments are represented utilizing Chapman-Kolmogorov equation, through which the expectation and the variance of nuclide distribution for each compartment or the fractured rock medium can be obtained. Some comparisons between Markov process model developed in this work and available analytical solutions for one-dimensional layered porous medium, fractured medium with rock matrix diffusion, and porous medium considering three member nuclide decay chain without rock matrix diffusion have been made showing comparatively good agreement for all cases. To verify the model developed in this work another comparative study was also made by fitting the experimental data obtained with NaLS and uranine running in the artificial fractured
Introduction to probability with Mathematica
Hastings, Kevin J
2009-01-01
Discrete ProbabilityThe Cast of Characters Properties of Probability Simulation Random SamplingConditional ProbabilityIndependenceDiscrete DistributionsDiscrete Random Variables, Distributions, and ExpectationsBernoulli and Binomial Random VariablesGeometric and Negative Binomial Random Variables Poisson DistributionJoint, Marginal, and Conditional Distributions More on ExpectationContinuous ProbabilityFrom the Finite to the (Very) Infinite Continuous Random Variables and DistributionsContinuous ExpectationContinuous DistributionsThe Normal Distribution Bivariate Normal DistributionNew Random Variables from OldOrder Statistics Gamma DistributionsChi-Square, Student's t, and F-DistributionsTransformations of Normal Random VariablesAsymptotic TheoryStrong and Weak Laws of Large Numbers Central Limit TheoremStochastic Processes and ApplicationsMarkov ChainsPoisson Processes QueuesBrownian MotionFinancial MathematicsAppendixIntroduction to Mathematica Glossary of Mathematica Commands for Probability Short Answers...
Florescu, Ionut
2013-01-01
THE COMPLETE COLLECTION NECESSARY FOR A CONCRETE UNDERSTANDING OF PROBABILITY Written in a clear, accessible, and comprehensive manner, the Handbook of Probability presents the fundamentals of probability with an emphasis on the balance of theory, application, and methodology. Utilizing basic examples throughout, the handbook expertly transitions between concepts and practice to allow readers an inclusive introduction to the field of probability. The book provides a useful format with self-contained chapters, allowing the reader easy and quick reference. Each chapter includes an introductio
SHARP ENTRYWISE PERTURBATION BOUNDS FOR MARKOV CHAINS.
Thiede, Erik; VAN Koten, Brian; Weare, Jonathan
For many Markov chains of practical interest, the invariant distribution is extremely sensitive to perturbations of some entries of the transition matrix, but insensitive to others; we give an example of such a chain, motivated by a problem in computational statistical physics. We have derived perturbation bounds on the relative error of the invariant distribution that reveal these variations in sensitivity. Our bounds are sharp, we do not impose any structural assumptions on the transition matrix or on the perturbation, and computing the bounds has the same complexity as computing the invariant distribution or computing other bounds in the literature. Moreover, our bounds have a simple interpretation in terms of hitting times, which can be used to draw intuitive but rigorous conclusions about the sensitivity of a chain to various types of perturbations.
Prinz, Jan-Hendrik; Chodera, John D; Pande, Vijay S; Swope, William C; Smith, Jeremy C; Noé, Frank
2011-06-28
Parallel tempering (PT) molecular dynamics simulations have been extensively investigated as a means of efficient sampling of the configurations of biomolecular systems. Recent work has demonstrated how the short physical trajectories generated in PT simulations of biomolecules can be used to construct the Markov models describing biomolecular dynamics at each simulated temperature. While this approach describes the temperature-dependent kinetics, it does not make optimal use of all available PT data, instead estimating the rates at a given temperature using only data from that temperature. This can be problematic, as some relevant transitions or states may not be sufficiently sampled at the temperature of interest, but might be readily sampled at nearby temperatures. Further, the comparison of temperature-dependent properties can suffer from the false assumption that data collected from different temperatures are uncorrelated. We propose here a strategy in which, by a simple modification of the PT protocol, the harvested trajectories can be reweighted, permitting data from all temperatures to contribute to the estimated kinetic model. The method reduces the statistical uncertainty in the kinetic model relative to the single temperature approach and provides estimates of transition probabilities even for transitions not observed at the temperature of interest. Further, the method allows the kinetics to be estimated at temperatures other than those at which simulations were run. We illustrate this method by applying it to the generation of a Markov model of the conformational dynamics of the solvated terminally blocked alanine peptide.
Honest Importance Sampling with Multiple Markov Chains.
Tan, Aixin; Doss, Hani; Hobert, James P
2015-01-01
Importance sampling is a classical Monte Carlo technique in which a random sample from one probability density, π 1 , is used to estimate an expectation with respect to another, π . The importance sampling estimator is strongly consistent and, as long as two simple moment conditions are satisfied, it obeys a central limit theorem (CLT). Moreover, there is a simple consistent estimator for the asymptotic variance in the CLT, which makes for routine computation of standard errors. Importance sampling can also be used in the Markov chain Monte Carlo (MCMC) context. Indeed, if the random sample from π 1 is replaced by a Harris ergodic Markov chain with invariant density π 1 , then the resulting estimator remains strongly consistent. There is a price to be paid however, as the computation of standard errors becomes more complicated. First, the two simple moment conditions that guarantee a CLT in the iid case are not enough in the MCMC context. Second, even when a CLT does hold, the asymptotic variance has a complex form and is difficult to estimate consistently. In this paper, we explain how to use regenerative simulation to overcome these problems. Actually, we consider a more general set up, where we assume that Markov chain samples from several probability densities, π 1 , …, π k , are available. We construct multiple-chain importance sampling estimators for which we obtain a CLT based on regeneration. We show that if the Markov chains converge to their respective target distributions at a geometric rate, then under moment conditions similar to those required in the iid case, the MCMC-based importance sampling estimator obeys a CLT. Furthermore, because the CLT is based on a regenerative process, there is a simple consistent estimator of the asymptotic variance. We illustrate the method with two applications in Bayesian sensitivity analysis. The first concerns one-way random effects models under different priors. The second involves Bayesian variable
International Nuclear Information System (INIS)
Alonso-Medina, A.
2010-01-01
Transition probabilities for 30 spectral lines, arising from the 5d 10 6s{8s, 7p, 5f, 5g} electronic configurations of Pb III (20 measured for the first time), have been experimentally determined from measurements of emission line intensities in a plasma lead induced by ablation with a Nd:YAG laser. The line intensities were obtained with the target placed in molecular argon at 6 Torr, recorded at a 400 ns delay from the laser pulse, which provides appropriate measurement conditions, and analysed between 200 and 700 nm. They are measured when the plasma reaches local thermodynamic equilibrium (LTE). The plasma under study had an electron temperature (T) of 21,400 K and an electron number density (N e ) of 7x10 16 cm -3 . The influence of self-absorption has been estimated for every line, and plasma homogeneity has been checked. The values obtained were compared with previous experimental values and theoretical estimates where possible.
Energy Technology Data Exchange (ETDEWEB)
Pattnaik, S. [Taratarini College, Purusottampur, Ganjam, Odisha (India); Nayak, R. C. [Department of Physics, Berhampur University, Brahmapur-760007 (India)
2014-04-15
We obtain here a new relation for the reduced electric quadrupole transition probability B(E2)↑ of a given nucleus in terms of its derivatives with respect to neutron and proton numbers based on a similar local energy relation in the Infinite Nuclear Matter (INM) model of atomic nuclei, which is essentially built on the foundation of the Hugenholtz–Van Hove (HVH) theorem of many-body theory. Obviously, such a relation in the form of a differential equation is expected to be more powerful than the usual algebraic difference equations. Although the relation for B(E2)↑ has been perceived simply on the basis of a corresponding differential equation for the local energy in the INM model, its theoretical foundation otherwise has been clearly demonstrated. We further exploit the differential equation in using the very definitions of the derivatives to obtain two different recursion relations for B(E2)↑, connecting in each case three neighboring even–even nuclei from lower to higher mass numbers and vice versa. We demonstrate their numerical validity using available data throughout the nuclear chart and also explore their possible utility in predicting B(E2)↑ values. (author)
International Nuclear Information System (INIS)
Pattnaik, S.; Nayak, R.C.
2014-01-01
We obtain here a new relation for the reduced electric quadrupole transition probability B(E2)↑ of a given nucleus in terms of its derivatives with respect to neutron and proton numbers based on a similar local energy relation in the Infinite Nuclear Matter (INM) model of atomic nuclei, which is essentially built on the foundation of the Hugenholtz–Van Hove (HVH) theorem of many-body theory. Obviously, such a relation in the form of a differential equation is expected to be more powerful than the usual algebraic difference equations. Although the relation for B(E2)↑ has been perceived simply on the basis of a corresponding differential equation for the local energy in the INM model, its theoretical foundation otherwise has been clearly demonstrated. We further exploit the differential equation in using the very definitions of the derivatives to obtain two different recursion relations for B(E2)↑, connecting in each case three neighboring even–even nuclei from lower to higher mass numbers and vice versa. We demonstrate their numerical validity using available data throughout the nuclear chart and also explore their possible utility in predicting B(E2)↑ values. (author)
Analysis of the trajectory surface hopping method from the Markov state model perspective
International Nuclear Information System (INIS)
Akimov, Alexey V.; Wang, Linjun; Prezhdo, Oleg V.; Trivedi, Dhara
2015-01-01
We analyze the applicability of the seminal fewest switches surface hopping (FSSH) method of Tully to modeling quantum transitions between electronic states that are not coupled directly, in the processes such as Auger recombination. We address the known deficiency of the method to describe such transitions by introducing an alternative definition for the surface hopping probabilities, as derived from the Markov state model perspective. We show that the resulting transition probabilities simplify to the quantum state populations derived from the time-dependent Schrödinger equation, reducing to the rapidly switching surface hopping approach of Tully and Preston. The resulting surface hopping scheme is simple and appeals to the fundamentals of quantum mechanics. The computational approach is similar to the FSSH method of Tully, yet it leads to a notably different performance. We demonstrate that the method is particularly accurate when applied to superexchange modeling. We further show improved accuracy of the method, when applied to one of the standard test problems. Finally, we adapt the derived scheme to atomistic simulation, combine it with the time-domain density functional theory, and show that it provides the Auger energy transfer timescales which are in good agreement with experiment, significantly improving upon other considered techniques. (author)
An Application of Graph Theory in Markov Chains Reliability Analysis
Directory of Open Access Journals (Sweden)
Pavel Skalny
2014-01-01
Full Text Available The paper presents reliability analysis which was realized for an industrial company. The aim of the paper is to present the usage of discrete time Markov chains and the flow in network approach. Discrete Markov chains a well-known method of stochastic modelling describes the issue. The method is suitable for many systems occurring in practice where we can easily distinguish various amount of states. Markov chains are used to describe transitions between the states of the process. The industrial process is described as a graph network. The maximal flow in the network corresponds to the production. The Ford-Fulkerson algorithm is used to quantify the production for each state. The combination of both methods are utilized to quantify the expected value of the amount of manufactured products for the given time period.
Qin, Z.; Zhao, J. M.; Liu, L. H.
2017-11-01
Accurate radiative transition probabilities of diatomic electronic systems are required to calculate the discrete radiation of plasmas. However, most of the published transition probabilities are obtained using older spectroscopic constants and electronic transition moment functions (ETMFs), some of which deviates greatly from experimental data. Fortunately, a lot of new spectroscopic constants that include more anharmonic correction terms than the earlier ones have been published over the past few years. In this work, the Einstein coefficients, Franck-Condon factors and absorption band oscillator strengths are calculated for important diatomic radiative transition processes of N2-O2, CO2-N2 and H2 plasmas produced in entering into the atmosphere of Earth, Mars and Jupiter. The most up-to-date spectroscopic constants are selected to reconstruct the potential energy curves by the Rydberg-Klein-Rees (RKR) method. Then the vibrational wave functions are calculated through the resolution of the radial Schrödinger equation for such potential energy curves. These results, together with the latest "ab-initio" ETMFs derived from the literature are used to compute the square of electronic-vibrational transition moments, Einstein coefficients and absorption band oscillator strengths. Moreover, the Franck-Condon factors are determined with the obtained vibrational wave functions. In the supplementary material we present tables of the radiative transition probabilities for 40 band systems of N2, N2+, NO, O2, CO, CO+, CN, C2 and H2 molecules. In addition, the calculated radiative lifetimes are systematically validated by available experimental results.
Utilization of two web-based continuing education courses evaluated by Markov chain model.
Tian, Hao; Lin, Jin-Mann S; Reeves, William C
2012-01-01
To evaluate the web structure of two web-based continuing education courses, identify problems and assess the effects of web site modifications. Markov chain models were built from 2008 web usage data to evaluate the courses' web structure and navigation patterns. The web site was then modified to resolve identified design issues and the improvement in user activity over the subsequent 12 months was quantitatively evaluated. Web navigation paths were collected between 2008 and 2010. The probability of navigating from one web page to another was analyzed. The continuing education courses' sequential structure design was clearly reflected in the resulting actual web usage models, and none of the skip transitions provided was heavily used. The web navigation patterns of the two different continuing education courses were similar. Two possible design flaws were identified and fixed in only one of the two courses. Over the following 12 months, the drop-out rate in the modified course significantly decreased from 41% to 35%, but remained unchanged in the unmodified course. The web improvement effects were further verified via a second-order Markov chain model. The results imply that differences in web content have less impact than web structure design on how learners navigate through continuing education courses. Evaluation of user navigation can help identify web design flaws and guide modifications. This study showed that Markov chain models provide a valuable tool to evaluate web-based education courses. Both the results and techniques in this study would be very useful for public health education and research specialists.
A joint logistic regression and covariate-adjusted continuous-time Markov chain model.
Rubin, Maria Laura; Chan, Wenyaw; Yamal, Jose-Miguel; Robertson, Claudia Sue
2017-12-10
The use of longitudinal measurements to predict a categorical outcome is an increasingly common goal in research studies. Joint models are commonly used to describe two or more models simultaneously by considering the correlated nature of their outcomes and the random error present in the longitudinal measurements. However, there is limited research on joint models with longitudinal predictors and categorical cross-sectional outcomes. Perhaps the most challenging task is how to model the longitudinal predictor process such that it represents the true biological mechanism that dictates the association with the categorical response. We propose a joint logistic regression and Markov chain model to describe a binary cross-sectional response, where the unobserved transition rates of a two-state continuous-time Markov chain are included as covariates. We use the method of maximum likelihood to estimate the parameters of our model. In a simulation study, coverage probabilities of about 95%, standard deviations close to standard errors, and low biases for the parameter values show that our estimation method is adequate. We apply the proposed joint model to a dataset of patients with traumatic brain injury to describe and predict a 6-month outcome based on physiological data collected post-injury and admission characteristics. Our analysis indicates that the information provided by physiological changes over time may help improve prediction of long-term functional status of these severely ill subjects. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
A reward semi-Markov process with memory for wind speed modeling
Petroni, F.; D'Amico, G.; Prattico, F.
2012-04-01
The increasing interest in renewable energy leads scientific research to find a better way to recover most of the available energy. Particularly, the maximum energy recoverable from wind is equal to 59.3% of that available (Betz law) at a specific pitch angle and when the ratio between the wind speed in output and in input is equal to 1/3. The pitch angle is the angle formed between the airfoil of the blade of the wind turbine and the wind direction. Old turbine and a lot of that actually marketed, in fact, have always the same invariant geometry of the airfoil. This causes that wind turbines will work with an efficiency that is lower than 59.3%. New generation wind turbines, instead, have a system to variate the pitch angle by rotating the blades. This system able the wind turbines to recover, at different wind speed, always the maximum energy, working in Betz limit at different speed ratios. A powerful system control of the pitch angle allows the wind turbine to recover better the energy in transient regime. A good stochastic model for wind speed is then needed to help both the optimization of turbine design and to assist the system control to predict the value of the wind speed to positioning the blades quickly and correctly. The possibility to have synthetic data of wind speed is a powerful instrument to assist designer to verify the structures of the wind turbines or to estimate the energy recoverable from a specific site. To generate synthetic data, Markov chains of first or higher order are often used [1,2,3]. In particular in [1] is presented a comparison between a first-order Markov chain and a second-order Markov chain. A similar work, but only for the first-order Markov chain, is conduced by [2], presenting the probability transition matrix and comparing the energy spectral density and autocorrelation of real and synthetic wind speed data. A tentative to modeling and to join speed and direction of wind is presented in [3], by using two models, first
Study of the seismic activity in central Ionian Islands via semi-Markov modelling
Pertsinidou, Christina Elisavet; Tsaklidis, George; Papadimitriou, Eleftheria
2017-06-01
The seismicity of the central Ionian Islands ( M ≥ 5.2, 1911-2014) is studied via a semi-Markov chain which is investigated in terms of the destination probabilities (occurrence probabilities). The interevent times are considered to follow geometric (in which case the semi-Markov model reduces to a Markov model) or Pareto distributions. The study of the destination probabilities is useful for forecasting purposes because they can provide the more probable earthquake magnitude and occurrence time. Using the first half of the data sample for the estimation procedure and the other half for forecasting purposes it is found that the time windows obtained by the destination probabilities include 72.9% of the observed earthquake occurrence times (for all magnitudes) and 71.4% for the larger ( M ≥ 6.0) ones.
Applied probability models with optimization applications
Ross, Sheldon M
1992-01-01
Concise advanced-level introduction to stochastic processes that frequently arise in applied probability. Largely self-contained text covers Poisson process, renewal theory, Markov chains, inventory theory, Brownian motion and continuous time optimization models, much more. Problems and references at chapter ends. ""Excellent introduction."" - Journal of the American Statistical Association. Bibliography. 1970 edition.
Adiabatic condition and the quantum hitting time of Markov chains
International Nuclear Information System (INIS)
Krovi, Hari; Ozols, Maris; Roland, Jeremie
2010-01-01
We present an adiabatic quantum algorithm for the abstract problem of searching marked vertices in a graph, or spatial search. Given a random walk (or Markov chain) P on a graph with a set of unknown marked vertices, one can define a related absorbing walk P ' where outgoing transitions from marked vertices are replaced by self-loops. We build a Hamiltonian H(s) from the interpolated Markov chain P(s)=(1-s)P+sP ' and use it in an adiabatic quantum algorithm to drive an initial superposition over all vertices to a superposition over marked vertices. The adiabatic condition implies that, for any reversible Markov chain and any set of marked vertices, the running time of the adiabatic algorithm is given by the square root of the classical hitting time. This algorithm therefore demonstrates a novel connection between the adiabatic condition and the classical notion of hitting time of a random walk. It also significantly extends the scope of previous quantum algorithms for this problem, which could only obtain a full quadratic speedup for state-transitive reversible Markov chains with a unique marked vertex.
Maximizing Entropy over Markov Processes
DEFF Research Database (Denmark)
Biondi, Fabrizio; Legay, Axel; Nielsen, Bo Friis
2013-01-01
The channel capacity of a deterministic system with confidential data is an upper bound on the amount of bits of data an attacker can learn from the system. We encode all possible attacks to a system using a probabilistic specification, an Interval Markov Chain. Then the channel capacity...... as a reward function, a polynomial algorithm to verify the existence of an system maximizing entropy among those respecting a specification, a procedure for the maximization of reward functions over Interval Markov Chains and its application to synthesize an implementation maximizing entropy. We show how...... to use Interval Markov Chains to model abstractions of deterministic systems with confidential data, and use the above results to compute their channel capacity. These results are a foundation for ongoing work on computing channel capacity for abstractions of programs derived from code....
Maximizing entropy over Markov processes
DEFF Research Database (Denmark)
Biondi, Fabrizio; Legay, Axel; Nielsen, Bo Friis
2014-01-01
The channel capacity of a deterministic system with confidential data is an upper bound on the amount of bits of data an attacker can learn from the system. We encode all possible attacks to a system using a probabilistic specification, an Interval Markov Chain. Then the channel capacity...... as a reward function, a polynomial algorithm to verify the existence of a system maximizing entropy among those respecting a specification, a procedure for the maximization of reward functions over Interval Markov Chains and its application to synthesize an implementation maximizing entropy. We show how...... to use Interval Markov Chains to model abstractions of deterministic systems with confidential data, and use the above results to compute their channel capacity. These results are a foundation for ongoing work on computing channel capacity for abstractions of programs derived from code. © 2014 Elsevier...
Markov Networks in Evolutionary Computation
Shakya, Siddhartha
2012-01-01
Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs). EDAs have arisen as one of the most successful experiences in the application of machine learning methods in optimization, mainly due to their efficiency to solve complex real-world optimization problems and their suitability for theoretical analysis. This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models. All major developments to date in the progressive introduction of Markov networks based EDAs are reviewed in the book. Hot current researc...
Generalized Probability-Probability Plots
Mushkudiani, N.A.; Einmahl, J.H.J.
2004-01-01
We introduce generalized Probability-Probability (P-P) plots in order to study the one-sample goodness-of-fit problem and the two-sample problem, for real valued data.These plots, that are constructed by indexing with the class of closed intervals, globally preserve the properties of classical P-P
Markov chains and mixing times
Levin, David A; Wilmer, Elizabeth L
2009-01-01
This book is an introduction to the modern approach to the theory of Markov chains. The main goal of this approach is to determine the rate of convergence of a Markov chain to the stationary distribution as a function of the size and geometry of the state space. The authors develop the key tools for estimating convergence times, including coupling, strong stationary times, and spectral methods. Whenever possible, probabilistic methods are emphasized. The book includes many examples and provides brief introductions to some central models of statistical mechanics. Also provided are accounts of r
Markov Models for Handwriting Recognition
Plotz, Thomas
2011-01-01
Since their first inception, automatic reading systems have evolved substantially, yet the recognition of handwriting remains an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, no standard procedures for building Markov model-based recognizers have yet been established. This text provides a comprehensive overview of the application of Markov models in the field of handwriting recognition, covering both hidden
Earth Data Analysis Center, University of New Mexico — USFS, State Forestry, BLM, and DOI fire occurrence point locations from 1987 to 2008 were combined and converted into a fire occurrence probability or density grid...
Analyzing the profit-loss sharing contracts with Markov model
Directory of Open Access Journals (Sweden)
Imam Wahyudi
2016-12-01
Full Text Available The purpose of this paper is to examine how to use first order Markov chain to build a reliable monitoring system for the profit-loss sharing based contracts (PLS as the mode of financing contracts in Islamic bank with censored continuous-time observations. The paper adopts the longitudinal analysis with the first order Markov chain framework. Laplace transform was used with homogenous continuous time assumption, from discretized generator matrix, to generate the transition matrix. Various metrics, i.e.: eigenvalue and eigenvector were used to test the first order Markov chain assumption. Cox semi parametric model was used also to analyze the momentum and waiting time effect as non-Markov behavior. The result shows that first order Markov chain is powerful as a monitoring tool for Islamic banks. We find that waiting time negatively affected present rating downgrade (upgrade significantly. Likewise, momentum covariate showed negative effect. Finally, the result confirms that different origin rating have different movement behavior. The paper explores the potential of Markov chain framework as a risk management tool for Islamic banks. It provides valuable insight and integrative model for banks to manage their borrower accounts. This model can be developed to be a powerful early warning system to identify which borrower needs to be monitored intensively. Ultimately, this model could potentially increase the efficiency, productivity and competitiveness of Islamic banks in Indonesia. The analysis used only rating data. Further study should be able to give additional information about the determinant factors of rating movement of the borrowers by incorporating various factors such as contract-related factors, bank-related factors, borrower-related factors and macroeconomic factors.
Learning Markov models for stationary system behaviors
DEFF Research Database (Denmark)
Chen, Yingke; Mao, Hua; Jaeger, Manfred
2012-01-01
to a single long observation sequence, and in these situations existing automatic learning methods cannot be applied. In this paper, we adapt algorithms for learning variable order Markov chains from a single observation sequence of a target system, so that stationary system properties can be verified using......Establishing an accurate model for formal verification of an existing hardware or software system is often a manual process that is both time consuming and resource demanding. In order to ease the model construction phase, methods have recently been proposed for automatically learning accurate...... the learned model. Experiments demonstrate that system properties (formulated as stationary probabilities of LTL formulas) can be reliably identified using the learned model....
Neuroevolution Mechanism for Hidden Markov Model
Directory of Open Access Journals (Sweden)
Nabil M. Hewahi
2011-12-01
Full Text Available Hidden Markov Model (HMM is a statistical model based on probabilities. HMM is becoming one of the major models involved in many applications such as natural language
processing, handwritten recognition, image processing, prediction systems and many more. In this research we are concerned with finding out the best HMM for a certain application domain. We propose a neuroevolution process that is based first on converting the HMM to a neural network, then generating many neural networks at random where each represents a HMM. We proceed by
applying genetic operators to obtain new set of neural networks where each represents HMMs, and updating the population. Finally select the best neural network based on a fitness function.
Recombination Processes and Nonlinear Markov Chains.
Pirogov, Sergey; Rybko, Alexander; Kalinina, Anastasia; Gelfand, Mikhail
2016-09-01
Bacteria are known to exchange genetic information by horizontal gene transfer. Since the frequency of homologous recombination depends on the similarity between the recombining segments, several studies examined whether this could lead to the emergence of subspecies. Most of them simulated fixed-size Wright-Fisher populations, in which the genetic drift should be taken into account. Here, we use nonlinear Markov processes to describe a bacterial population evolving under mutation and recombination. We consider a population structure as a probability measure on the space of genomes. This approach implies the infinite population size limit, and thus, the genetic drift is not assumed. We prove that under these conditions, the emergence of subspecies is impossible.
A Markov Chain Model for Contagion
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Angelos Dassios
2014-11-01
Full Text Available We introduce a bivariate Markov chain counting process with contagion for modelling the clustering arrival of loss claims with delayed settlement for an insurance company. It is a general continuous-time model framework that also has the potential to be applicable to modelling the clustering arrival of events, such as jumps, bankruptcies, crises and catastrophes in finance, insurance and economics with both internal contagion risk and external common risk. Key distributional properties, such as the moments and probability generating functions, for this process are derived. Some special cases with explicit results and numerical examples and the motivation for further actuarial applications are also discussed. The model can be considered a generalisation of the dynamic contagion process introduced by Dassios and Zhao (2011.
Multivariate Markov chain modeling for stock markets
Maskawa, Jun-ichi
2003-06-01
We study a multivariate Markov chain model as a stochastic model of the price changes of portfolios in the framework of the mean field approximation. The time series of price changes are coded into the sequences of up and down spins according to their signs. We start with the discussion for small portfolios consisting of two stock issues. The generalization of our model to arbitrary size of portfolio is constructed by a recurrence relation. The resultant form of the joint probability of the stationary state coincides with Gibbs measure assigned to each configuration of spin glass model. Through the analysis of actual portfolios, it has been shown that the synchronization of the direction of the price changes is well described by the model.
Indian Academy of Sciences (India)
First page Back Continue Last page Overview Graphics. TRANSIT. SYSTEM: DETERMINE 2D-POSITION GLOBALLY BUT INTERMITTENT (POST-FACTO). IMPROVED ACCURACY. PRINCIPLE: POLAR SATELLITES WITH INNOVATIONS OF: GRAVITY-GRADIENT ATTITUDE CONTROL; DRAG COMPENSATION. WORKS ...
Herrera, C.; Nava, F. A.; Lomnitz, C.
2006-08-01
A previous work introduced a new method for seismic hazard evaluation in a system (a geographic area with distinct, but related seismogenic regions) based on modeling the transition probabilities of states (patterns of presence or absence of seismicity, with magnitude greater or equal to a threshold magnitude Mr, in the regions of the system, during a time interval Δt) as a Markov chain. Application of this direct method to the Japan area gave very good results. Given that the most important limitation of the direct method is the relative scarcity of large magnitude events, we decided to explore the possibility that seismicity with magnitude M ≥ Mmr contains information about the future occurrence of earthquakes with M ≥ Mmr > Mmr. This mixed Markov chain method estimates the probabilities of occurrence of a system state for M ≥ MMr on the basis of the observed state for M ≥ Mmr in the previous Δt. Application of the mixed method to the area of Japan gives better hazard estimations than the direct method; in particular for large earthquakes. As part of this study, the problem of performance evaluation of hazard estimation methods is addressed, leading to the use of grading functions.
The Consensus String Problem and the Complexity of Comparing Hidden Markov Models
DEFF Research Database (Denmark)
Lyngsø, Rune Bang; Pedersen, Christian Nørgaard Storm
2002-01-01
The basic theory of hidden Markov models was developed and applied to problems in speech recognition in the late 1960s, and has since then been applied to numerous problems, e.g. biological sequence analysis. Most applications of hidden Markov models are based on efficient algorithms for computing......-norms. We discuss the applicability of the technique used for proving the hardness of comparing two hidden Markov models under the L1-norm to other measures of distance between probability distributions. In particular, we show that it cannot be used for proving NP-hardness of determining the Kullback...
Learning Markov Decision Processes for Model Checking
DEFF Research Database (Denmark)
Mao, Hua; Chen, Yingke; Jaeger, Manfred
2012-01-01
. The proposed learning algorithm is adapted from algorithms for learning deterministic probabilistic finite automata, and extended to include both probabilistic and nondeterministic transitions. The algorithm is empirically analyzed and evaluated by learning system models of slot machines. The evaluation......Constructing an accurate system model for formal model verification can be both resource demanding and time-consuming. To alleviate this shortcoming, algorithms have been proposed for automatically learning system models based on observed system behaviors. In this paper we extend the algorithm...... on learning probabilistic automata to reactive systems, where the observed system behavior is in the form of alternating sequences of inputs and outputs. We propose an algorithm for automatically learning a deterministic labeled Markov decision process model from the observed behavior of a reactive system...
Monte Carlo simulation of Markov unreliability models
International Nuclear Information System (INIS)
Lewis, E.E.; Boehm, F.
1984-01-01
A Monte Carlo method is formulated for the evaluation of the unrealibility of complex systems with known component failure and repair rates. The formulation is in terms of a Markov process allowing dependences between components to be modeled and computational efficiencies to be achieved in the Monte Carlo simulation. Two variance reduction techniques, forced transition and failure biasing, are employed to increase computational efficiency of the random walk procedure. For an example problem these result in improved computational efficiency by more than three orders of magnitudes over analog Monte Carlo. The method is generalized to treat problems with distributed failure and repair rate data, and a batching technique is introduced and shown to result in substantial increases in computational efficiency for an example problem. A method for separating the variance due to the data uncertainty from that due to the finite number of random walks is presented. (orig.)
Directory of Open Access Journals (Sweden)
Meshach Tettey
2017-08-01
Full Text Available Abstract This study develops an objective rainfall pattern assessment through Markov chain analysis using daily rainfall data from 1980 to 2010, a period of 30 years, for five cities or towns along the south eastern coastal belt of Ghana; Cape Coast, Accra, Akuse, Akatsi and Keta. Transition matrices were computed for each town and each month using the conditional probability of rain or no rain on a particular day given that it rained or did not rain on the previous day. The steady state transition matrices and the steady state probability vectors were also computed for each town and each month. It was found that, the rainy or dry season pattern observed using the monthly steady state rainfall vectors tended to reflect the monthly rainfall time series trajectory. Overall, the probability of rain on any day was low to average: Keta 0.227, Akuse 0.382, Accra 0.467, Cape Coast, 0.50 and Akatsi 0.50. In particular, for Accra, the rainy season was observed to be in the months of May to June and September to October. We also determined that the probability of rainfall generally tended to increase from east to west along the south eastern coast of Ghana.
The computation of stationary distributions of Markov chains through perturbations
Directory of Open Access Journals (Sweden)
Jeffery J. Hunter
1991-01-01
Full Text Available An algorithmic procedure for the determination of the stationary distribution of a finite, m-state, irreducible Markov chain, that does not require the use of methods for solving systems of linear equations, is presented. The technique is based upon a succession of m, rank one, perturbations of the trivial doubly stochastic matrix whose known steady state vector is updated at each stage to yield the required stationary probability vector.
Geometry and Dynamics for Markov Chain Monte Carlo
Barp, Alessandro; Briol, Francois-Xavier; Kennedy, Anthony D.; Girolami, Mark
2017-01-01
Markov Chain Monte Carlo methods have revolutionised mathematical computation and enabled statistical inference within many previously intractable models. In this context, Hamiltonian dynamics have been proposed as an efficient way of building chains which can explore probability densities efficiently. The method emerges from physics and geometry and these links have been extensively studied by a series of authors through the last thirty years. However, there is currently a gap between the in...
MARKOV CHAIN MODELING OF PERFORMANCE DEGRADATION OF PHOTOVOLTAIC SYSTEM
E. Suresh Kumar; Asis Sarkar; Dhiren kumar Behera
2012-01-01
Modern probability theory studies chance processes for which theknowledge of previous outcomes influence predictions for future experiments. In principle, when a sequence of chance experiments, all of the past outcomes could influence the predictions for the next experiment. In Markov chain type of chance, the outcome of a given experiment can affect the outcome of the next experiment. The system state changes with time and the state X and time t are two random variables. Each of these variab...
Using multi-state markov models to identify credit card risk
Directory of Open Access Journals (Sweden)
Daniel Evangelista Régis
2016-06-01
Full Text Available Abstract The main interest of this work is to analyze the application of multi-state Markov models to evaluate credit card risk by investigating the characteristics of different state transitions in client-institution relationships over time, thereby generating score models for various purposes. We also used logistic regression models to compare the results with those obtained using multi-state Markov models. The models were applied to an actual database of a Brazilian financial institution. In this application, multi-state Markov models performed better than logistic regression models in predicting default risk, and logistic regression models performed better in predicting cancellation risk.
Katoen, Joost P.; Maneesh Khattri, M.; Zapreev, I.S.; Zapreev, I.S.
2005-01-01
This short tool paper introduces MRMC, a model checker for discrete-time and continuous-time Markov reward models. It supports reward extensions of PCTL and CSL, and allows for the automated verification of properties concerning long-run and instantaneous rewards as well as cumulative rewards. In
Markov Decision Processes in Practice
Boucherie, Richardus J.; van Dijk, N.M.
2017-01-01
It is over 30 years ago since D.J. White started his series of surveys on practical applications of Markov decision processes (MDP), over 20 years after the phenomenal book by Martin Puterman on the theory of MDP, and over 10 years since Eugene A. Feinberg and Adam Shwartz published their Handbook
Quantum Probabilities as Behavioral Probabilities
Directory of Open Access Journals (Sweden)
Vyacheslav I. Yukalov
2017-03-01
Full Text Available We demonstrate that behavioral probabilities of human decision makers share many common features with quantum probabilities. This does not imply that humans are some quantum objects, but just shows that the mathematics of quantum theory is applicable to the description of human decision making. The applicability of quantum rules for describing decision making is connected with the nontrivial process of making decisions in the case of composite prospects under uncertainty. Such a process involves deliberations of a decision maker when making a choice. In addition to the evaluation of the utilities of considered prospects, real decision makers also appreciate their respective attractiveness. Therefore, human choice is not based solely on the utility of prospects, but includes the necessity of resolving the utility-attraction duality. In order to justify that human consciousness really functions similarly to the rules of quantum theory, we develop an approach defining human behavioral probabilities as the probabilities determined by quantum rules. We show that quantum behavioral probabilities of humans do not merely explain qualitatively how human decisions are made, but they predict quantitative values of the behavioral probabilities. Analyzing a large set of empirical data, we find good quantitative agreement between theoretical predictions and observed experimental data.
Probability theory a foundational course
Pakshirajan, R P
2013-01-01
This book shares the dictum of J. L. Doob in treating Probability Theory as a branch of Measure Theory and establishes this relation early. Probability measures in product spaces are introduced right at the start by way of laying the ground work to later claim the existence of stochastic processes with prescribed finite dimensional distributions. Other topics analysed in the book include supports of probability measures, zero-one laws in product measure spaces, Erdos-Kac invariance principle, functional central limit theorem and functional law of the iterated logarithm for independent variables, Skorohod embedding, and the use of analytic functions of a complex variable in the study of geometric ergodicity in Markov chains. This book is offered as a text book for students pursuing graduate programs in Mathematics and or Statistics. The book aims to help the teacher present the theory with ease, and to help the student sustain his interest and joy in learning the subject.
Markov Chain Models for Stochastic Behavior in Resonance Overlap Regions
McCarthy, Morgan; Quillen, Alice
2018-01-01
We aim to predict lifetimes of particles in chaotic zoneswhere resonances overlap. A continuous-time Markov chain model isconstructed using mean motion resonance libration timescales toestimate transition times between resonances. The model is applied todiffusion in the co-rotation region of a planet. For particles begunat low eccentricity, the model is effective for early diffusion, butnot at later time when particles experience close encounters to the planet.
A Bayesian Markov geostatistical model for estimation of hydrogeological properties
International Nuclear Information System (INIS)
Rosen, L.; Gustafson, G.
1996-01-01
A geostatistical methodology based on Markov-chain analysis and Bayesian statistics was developed for probability estimations of hydrogeological and geological properties in the siting process of a nuclear waste repository. The probability estimates have practical use in decision-making on issues such as siting, investigation programs, and construction design. The methodology is nonparametric which makes it possible to handle information that does not exhibit standard statistical distributions, as is often the case for classified information. Data do not need to meet the requirements on additivity and normality as with the geostatistical methods based on regionalized variable theory, e.g., kriging. The methodology also has a formal way for incorporating professional judgments through the use of Bayesian statistics, which allows for updating of prior estimates to posterior probabilities each time new information becomes available. A Bayesian Markov Geostatistical Model (BayMar) software was developed for implementation of the methodology in two and three dimensions. This paper gives (1) a theoretical description of the Bayesian Markov Geostatistical Model; (2) a short description of the BayMar software; and (3) an example of application of the model for estimating the suitability for repository establishment with respect to the three parameters of lithology, hydraulic conductivity, and rock quality designation index (RQD) at 400--500 meters below ground surface in an area around the Aespoe Hard Rock Laboratory in southeastern Sweden
International Nuclear Information System (INIS)
Xiao Gang; Li Zhizhong
2004-01-01
Based on integral equaiton describing the life-history of Markov system, six types of estimators of the current unavailability of Markov system with dependent repair are propounded. Combining with the biased sampling of state transition time of system, six types of Monte Carlo for estimating the current unavailability are given. Two numerical examples are given to deal with the variances and efficiencies of the six types of Monte Carlo methods. (authors)
A sufficiency property arising from the characterization of extremes of Markov chains
Bortot, Paola; Coles, Stuart
2000-01-01
At extreme levels, it is known that for a particular choice of marginal distribution, transitions of a Markov chain behave like a random walk. For a broad class of Markov chains, we give a characterization for the step length density of the limiting random walk, which leads to an interesting sufficiency property. This representation also leads us to propose a new technique for kernel density estimation for this class of models.
DEFF Research Database (Denmark)
Rojas-Nandayapa, Leonardo
Tail probabilities of sums of heavy-tailed random variables are of a major importance in various branches of Applied Probability, such as Risk Theory, Queueing Theory, Financial Management, and are subject to intense research nowadays. To understand their relevance one just needs to think...... analytic expression for the distribution function of a sum of random variables. The presence of heavy-tailed random variables complicates the problem even more. The objective of this dissertation is to provide better approximations by means of sharp asymptotic expressions and Monte Carlo estimators...
Quantum Markov processes and applications in many-body systems
International Nuclear Information System (INIS)
Temme, P. K.
2010-01-01
This thesis is concerned with the investigation of quantum as well as classical Markov processes and their application in the field of strongly correlated many-body systems. A Markov process is a special kind of stochastic process, which is determined by an evolution that is independent of its history and only depends on the current state of the system. The application of Markov processes has a long history in the field of statistical mechanics and classical many-body theory. Not only are Markov processes used to describe the dynamics of stochastic systems, but they predominantly also serve as a practical method that allows for the computation of fundamental properties of complex many-body systems by means of probabilistic algorithms. The aim of this thesis is to investigate the properties of quantum Markov processes, i.e. Markov processes taking place in a quantum mechanical state space, and to gain a better insight into complex many-body systems by means thereof. Moreover, we formulate a novel quantum algorithm which allows for the computation of the thermal and ground states of quantum many-body systems. After a brief introduction to quantum Markov processes we turn to an investigation of their convergence properties. We find bounds on the convergence rate of the quantum process by generalizing geometric bounds found for classical processes. We generalize a distance measure that serves as the basis for our investigations, the chi-square divergence, to non-commuting probability spaces. This divergence allows for a convenient generalization of the detailed balance condition to quantum processes. We then devise the quantum algorithm that can be seen as the natural generalization of the ubiquitous Metropolis algorithm to simulate quantum many-body Hamiltonians. By this we intend to provide further evidence, that a quantum computer can serve as a fully-fledged quantum simulator, which is not only capable of describing the dynamical evolution of quantum systems, but
438 Optimal Number of States in Hidden Markov Models and its ...
African Journals Online (AJOL)
In this paper, Hidden Markov Model is applied to model human movements as to .... emit either discrete information or a continuous data derived from a Probability .... For each hidden state in the test set, the probability = ... by applying the Kullback-Leibler distance (Juang & Rabiner, 1985) which ..... One Size Does Not Fit.
Markov Processes: Exploring the Use of Dynamic Visualizations to Enhance Student Understanding
Pfannkuch, Maxine; Budgett, Stephanie
2016-01-01
Finding ways to enhance introductory students' understanding of probability ideas and theory is a goal of many first-year probability courses. In this article, we explore the potential of a prototype tool for Markov processes using dynamic visualizations to develop in students a deeper understanding of the equilibrium and hitting times…
k– fading channels: a finite state Markov modelling approach
Indian Academy of Sciences (India)
C Priyanka
2018-02-07
Feb 7, 2018 ... 1 Department of Electronics and Communication Engineering, SRM University, ... probability; level crossing rate; state-transition probability; state-time duration. 1. ... statistics of the mobile radio signals are described by dif-.
Probability, Statistics, and Stochastic Processes
Olofsson, Peter
2012-01-01
This book provides a unique and balanced approach to probability, statistics, and stochastic processes. Readers gain a solid foundation in all three fields that serves as a stepping stone to more advanced investigations into each area. The Second Edition features new coverage of analysis of variance (ANOVA), consistency and efficiency of estimators, asymptotic theory for maximum likelihood estimators, empirical distribution function and the Kolmogorov-Smirnov test, general linear models, multiple comparisons, Markov chain Monte Carlo (MCMC), Brownian motion, martingales, and
Shi, Wei; Xia, Jun
2017-02-01
Water quality risk management is a global hot research linkage with the sustainable water resource development. Ammonium nitrogen (NH 3 -N) and permanganate index (COD Mn ) as the focus indicators in Huai River Basin, are selected to reveal their joint transition laws based on Markov theory. The time-varying moments model with either time or land cover index as explanatory variables is applied to build the time-varying marginal distributions of water quality time series. Time-varying copula model, which takes the non-stationarity in the marginal distribution and/or the time variation in dependence structure between water quality series into consideration, is constructed to describe a bivariate frequency analysis for NH 3 -N and COD Mn series at the same monitoring gauge. The larger first-order Markov joint transition probability indicates water quality state Class V w , Class IV and Class III will occur easily in the water body of Bengbu Sluice. Both marginal distribution and copula models are nonstationary, and the explanatory variable time yields better performance than land cover index in describing the non-stationarities in the marginal distributions. In modelling the dependence structure changes, time-varying copula has a better fitting performance than the copula with the constant or the time-trend dependence parameter. The largest synchronous encounter risk probability of NH 3 -N and COD Mn simultaneously reaching Class V is 50.61%, while the asynchronous encounter risk probability is largest when NH 3 -N and COD Mn is inferior to class V and class IV water quality standards, respectively.
Zhao, Zhibiao
2011-06-01
We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.
Operations and support cost modeling using Markov chains
Unal, Resit
1989-01-01
Systems for future missions will be selected with life cycle costs (LCC) as a primary evaluation criterion. This reflects the current realization that only systems which are considered affordable will be built in the future due to the national budget constaints. Such an environment calls for innovative cost modeling techniques which address all of the phases a space system goes through during its life cycle, namely: design and development, fabrication, operations and support; and retirement. A significant portion of the LCC for reusable systems are generated during the operations and support phase (OS). Typically, OS costs can account for 60 to 80 percent of the total LCC. Clearly, OS costs are wholly determined or at least strongly influenced by decisions made during the design and development phases of the project. As a result OS costs need to be considered and estimated early in the conceptual phase. To be effective, an OS cost estimating model needs to account for actual instead of ideal processes by associating cost elements with probabilities. One approach that may be suitable for OS cost modeling is the use of the Markov Chain Process. Markov chains are an important method of probabilistic analysis for operations research analysts but they are rarely used for life cycle cost analysis. This research effort evaluates the use of Markov Chains in LCC analysis by developing OS cost model for a hypothetical reusable space transportation vehicle (HSTV) and suggests further uses of the Markov Chain process as a design-aid tool.
Mapping absorption processes onto a Markov chain, conserving the mean first passage time
International Nuclear Information System (INIS)
Biswas, Katja
2013-01-01
The dynamics of a multidimensional system is projected onto a discrete state master equation using the transition rates W(k → k′; t, t + dt) between a set of states {k} represented by the regions {ζ k } in phase or discrete state space. Depending on the dynamics Γ i (t) of the original process and the choice of ζ k , the discretized process can be Markovian or non-Markovian. For absorption processes, it is shown that irrespective of these properties of the projection, a master equation with time-independent transition rates W-bar (k→k ' ) can be obtained, which conserves the total occupation time of the partitions of the phase or discrete state space of the original process. An expression for the transition probabilities p-bar (k ' |k) is derived based on either time-discrete measurements {t i } with variable time stepping Δ (i+1)i = t i+1 − t i or the theoretical knowledge at continuous times t. This allows computational methods of absorbing Markov chains to be used to obtain the mean first passage time (MFPT) of the system. To illustrate this approach, the procedure is applied to obtain the MFPT for the overdamped Brownian motion of particles subject to a system with dichotomous noise and the escape from an entropic barrier. The high accuracy of the simulation results confirms with the theory. (paper)
Synthesis of the Markov model of the thermochemical degradation of a polymer in solution
Directory of Open Access Journals (Sweden)
V. K. Bityukov
2017-01-01
Full Text Available The paper deals with the problem of mathematical modeling of thermochemical destruction process. The apparatus of Markov's chains is used to synthesize a mathematical model. The authors of the study suggest to consider the destruction process as a random one, where the system state changes, which is characterized by the proportion of macromolecules in each fraction of the molecular- and weight distribution. The intensities of transitions from one state to another characterize the corresponding rates of destruction processes for each fraction of the molecular- and weight distribution. The processes of crosslinking and polymerization in this work were neglected, and it was accepted that there is a probability of transition from any state with a lower order index (corresponding to fractions with higher molecular weights to any state with a higher index (corresponding to fractions with lower molecular weights. Markov's chain with discrete states and continuous time was taken as the mathematical model basis. Interactive graphical simulation environment MathWorksSimulink was used as a simulation environment. Experimental studies of polybutadiene destruction in solution were carried out to evaluate the mathematical model parameters. The GPC (gel-penetration chromatography data of the polybutadiene solution were used as the initial (starting data for estimating the polymer WMD (molecular weight distribution. Mean-square deviation of the calculated data from the experimental data for each fraction and at specified times was minimized for the numerical search of parameter values. The results of comparison of experimental and calculated on mathematical model data showed an error of calculations on the average about 5%, which indicates an acceptable error in estimating of polymer fractions proportions change during the process of destruction for the process under consideration and conditions.
SDI and Markov Chains for Regional Drought Characteristics
Directory of Open Access Journals (Sweden)
Chen-Feng Yeh
2015-08-01
Full Text Available In recent years, global climate change has altered precipitation patterns, causing uneven spatial and temporal distribution of precipitation that gradually induces precipitation polarization phenomena. Taiwan is located in the subtropical climate zone, with distinct wet and dry seasons, which makes the polarization phenomenon more obvious; this has also led to a large difference between river flows during the wet and dry seasons, which is significantly influenced by precipitation, resulting in hydrological drought. Therefore, to effectively address the growing issue of water shortages, it is necessary to explore and assess the drought characteristics of river systems. In this study, the drought characteristics of northern Taiwan were studied using the streamflow drought index (SDI and Markov chains. Analysis results showed that the year 2002 was a turning point for drought severity in both the Lanyang River and Yilan River basins; the severity of rain events in the Lanyang River basin increased after 2002, and the severity of drought events in the Yilan River basin exhibited a gradual upward trend. In the study of drought severity, analysis results from periods of three months (November to January and six months (November to April have shown significant drought characteristics. In addition, analysis of drought occurrence probabilities using the method of Markov chains has shown that the occurrence probabilities of drought events are higher in the Lanyang River basin than in the Yilan River basin; particularly for extreme events, the occurrence probability of an extreme drought event is 20.6% during the dry season (November to April in the Lanyang River basin, and 3.4% in the Yilan River basin. This study shows that for analysis of drought/wet occurrence probabilities, the results obtained for the drought frequency and occurrence probability using short-term data with the method of Markov chains can be used to predict the long-term occurrence
A Markov random field approach for microstructure synthesis
International Nuclear Information System (INIS)
Kumar, A; Nguyen, L; DeGraef, M; Sundararaghavan, V
2016-01-01
We test the notion that many microstructures have an underlying stationary probability distribution. The stationary probability distribution is ubiquitous: we know that different windows taken from a polycrystalline microstructure are generally ‘statistically similar’. To enable computation of such a probability distribution, microstructures are represented in the form of undirected probabilistic graphs called Markov Random Fields (MRFs). In the model, pixels take up integer or vector states and interact with multiple neighbors over a window. Using this lattice structure, algorithms are developed to sample the conditional probability density for the state of each pixel given the known states of its neighboring pixels. The sampling is performed using reference experimental images. 2D microstructures are artificially synthesized using the sampled probabilities. Statistical features such as grain size distribution and autocorrelation functions closely match with those of the experimental images. The mechanical properties of the synthesized microstructures were computed using the finite element method and were also found to match the experimental values. (paper)
On the representability of complete genomes by multiple competing finite-context (Markov models.
Directory of Open Access Journals (Sweden)
Armando J Pinho
Full Text Available A finite-context (Markov model of order k yields the probability distribution of the next symbol in a sequence of symbols, given the recent past up to depth k. Markov modeling has long been applied to DNA sequences, for example to find gene-coding regions. With the first studies came the discovery that DNA sequences are non-stationary: distinct regions require distinct model orders. Since then, Markov and hidden Markov models have been extensively used to describe the gene structure of prokaryotes and eukaryotes. However, to our knowledge, a comprehensive study about the potential of Markov models to describe complete genomes is still lacking. We address this gap in this paper. Our approach relies on (i multiple competing Markov models of different orders (ii careful programming techniques that allow orders as large as sixteen (iii adequate inverted repeat handling (iv probability estimates suited to the wide range of context depths used. To measure how well a model fits the data at a particular position in the sequence we use the negative logarithm of the probability estimate at that position. The measure yields information profiles of the sequence, which are of independent interest. The average over the entire sequence, which amounts to the average number of bits per base needed to describe the sequence, is used as a global performance measure. Our main conclusion is that, from the probabilistic or information theoretic point of view and according to this performance measure, multiple competing Markov models explain entire genomes almost as well or even better than state-of-the-art DNA compression methods, such as XM, which rely on very different statistical models. This is surprising, because Markov models are local (short-range, contrasting with the statistical models underlying other methods, where the extensive data repetitions in DNA sequences is explored, and therefore have a non-local character.
Absolute continuity of the distribution of some Markov geometric series
Institute of Scientific and Technical Information of China (English)
Ai-hua; FAN; Ji-hong; ZHANG
2007-01-01
Let (∈n)≥0 be the Markov chain of two states with respect to the probability measure of the maximal entropy on the subshift space ∑A defined by Fibonacci incident matrix A.We consider the measure μλ of the probability distribution of the random series ∑∞n=0 εnλn (0 ＜λ＜ 1).It is proved that μλ is singular if λ∈ (0,√5-1/2) and that μλ is absolutely continuous for almost all λ∈ (√5-1/2,0.739).
Grinstead, Charles M; Snell, J Laurie
2011-01-01
This book explores four real-world topics through the lens of probability theory. It can be used to supplement a standard text in probability or statistics. Most elementary textbooks present the basic theory and then illustrate the ideas with some neatly packaged examples. Here the authors assume that the reader has seen, or is learning, the basic theory from another book and concentrate in some depth on the following topics: streaks, the stock market, lotteries, and fingerprints. This extended format allows the authors to present multiple approaches to problems and to pursue promising side discussions in ways that would not be possible in a book constrained to cover a fixed set of topics. To keep the main narrative accessible, the authors have placed the more technical mathematical details in appendices. The appendices can be understood by someone who has taken one or two semesters of calculus.
Dorogovtsev, A Ya; Skorokhod, A V; Silvestrov, D S; Skorokhod, A V
1997-01-01
This book of problems is intended for students in pure and applied mathematics. There are problems in traditional areas of probability theory and problems in the theory of stochastic processes, which has wide applications in the theory of automatic control, queuing and reliability theories, and in many other modern science and engineering fields. Answers to most of the problems are given, and the book provides hints and solutions for more complicated problems.
Walewski, Mateusz
2011-01-01
This paper presents the results of an attempt to use the combined results of the dedicated survey performed by CASE in 2007 and Polish LFS data in order to: (a) analyze the development of the shadow employment in Poland in years 2003-2008 and, (b) analyze the transition probabilities in and out of shadow employment. The estimated share of shadow workers in total employment in Poland in yea...
Assessing type I error and power of multistate Markov models for panel data-A simulation study.
Cassarly, Christy; Martin, Renee' H; Chimowitz, Marc; Peña, Edsel A; Ramakrishnan, Viswanathan; Palesch, Yuko Y
2017-01-01
Ordinal outcomes collected at multiple follow-up visits are common in clinical trials. Sometimes, one visit is chosen for the primary analysis and the scale is dichotomized amounting to loss of information. Multistate Markov models describe how a process moves between states over time. Here, simulation studies are performed to investigate the type I error and power characteristics of multistate Markov models for panel data with limited non-adjacent state transitions. The results suggest that the multistate Markov models preserve the type I error and adequate power is achieved with modest sample sizes for panel data with limited non-adjacent state transitions.
Crossing over...Markov meets Mendel.
Mneimneh, Saad
2012-01-01
Chromosomal crossover is a biological mechanism to combine parental traits. It is perhaps the first mechanism ever taught in any introductory biology class. The formulation of crossover, and resulting recombination, came about 100 years after Mendel's famous experiments. To a great extent, this formulation is consistent with the basic genetic findings of Mendel. More importantly, it provides a mathematical insight for his two laws (and corrects them). From a mathematical perspective, and while it retains similarities, genetic recombination guarantees diversity so that we do not rapidly converge to the same being. It is this diversity that made the study of biology possible. In particular, the problem of genetic mapping and linkage-one of the first efforts towards a computational approach to biology-relies heavily on the mathematical foundation of crossover and recombination. Nevertheless, as students we often overlook the mathematics of these phenomena. Emphasizing the mathematical aspect of Mendel's laws through crossover and recombination will prepare the students to make an early realization that biology, in addition to being experimental, IS a computational science. This can serve as a first step towards a broader curricular transformation in teaching biological sciences. I will show that a simple and modern treatment of Mendel's laws using a Markov chain will make this step possible, and it will only require basic college-level probability and calculus. My personal teaching experience confirms that students WANT to know Markov chains because they hear about them from bioinformaticists all the time. This entire exposition is based on three homework problems that I designed for a course in computational biology. A typical reader is, therefore, an instructional staff member or a student in a computational field (e.g., computer science, mathematics, statistics, computational biology, bioinformatics). However, other students may easily follow by omitting the
Crossing over...Markov meets Mendel.
Directory of Open Access Journals (Sweden)
Saad Mneimneh
Full Text Available Chromosomal crossover is a biological mechanism to combine parental traits. It is perhaps the first mechanism ever taught in any introductory biology class. The formulation of crossover, and resulting recombination, came about 100 years after Mendel's famous experiments. To a great extent, this formulation is consistent with the basic genetic findings of Mendel. More importantly, it provides a mathematical insight for his two laws (and corrects them. From a mathematical perspective, and while it retains similarities, genetic recombination guarantees diversity so that we do not rapidly converge to the same being. It is this diversity that made the study of biology possible. In particular, the problem of genetic mapping and linkage-one of the first efforts towards a computational approach to biology-relies heavily on the mathematical foundation of crossover and recombination. Nevertheless, as students we often overlook the mathematics of these phenomena. Emphasizing the mathematical aspect of Mendel's laws through crossover and recombination will prepare the students to make an early realization that biology, in addition to being experimental, IS a computational science. This can serve as a first step towards a broader curricular transformation in teaching biological sciences. I will show that a simple and modern treatment of Mendel's laws using a Markov chain will make this step possible, and it will only require basic college-level probability and calculus. My personal teaching experience confirms that students WANT to know Markov chains because they hear about them from bioinformaticists all the time. This entire exposition is based on three homework problems that I designed for a course in computational biology. A typical reader is, therefore, an instructional staff member or a student in a computational field (e.g., computer science, mathematics, statistics, computational biology, bioinformatics. However, other students may easily follow by
Markov processes characterization and convergence
Ethier, Stewart N
2009-01-01
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists."[A]nyone who works with Markov processes whose state space is uncountably infinite will need this most impressive book as a guide and reference."-American Scientist"There is no question but that space should immediately be reserved for [this] book on the library shelf. Those who aspire to mastery of the contents should also reserve a large number of long winter evenings."-Zentralblatt f?r Mathematik und ihre Grenzgebiete/Mathematics Abstracts"Ethier and Kurtz have produced an excellent treatment of the modern theory of Markov processes that [is] useful both as a reference work and as a graduate textbook."-Journal of Statistical PhysicsMarkov Proce...
DEFF Research Database (Denmark)
Johansson, Martin; Lytken, Ole; Chorkendorff, Ib
2008-01-01
The sticking probability for H-2 on Ni, Co, Cu, Rh, Ru, Pd, it and Pt metal films supported on graphite has been investigated in a gas mixture consisting of 10 ppm carbon monoxide in hydrogen at a total pressure of 1 bar in the temperature range 40-200 degrees C. Carbon monoxide inhibits the stic......The sticking probability for H-2 on Ni, Co, Cu, Rh, Ru, Pd, it and Pt metal films supported on graphite has been investigated in a gas mixture consisting of 10 ppm carbon monoxide in hydrogen at a total pressure of 1 bar in the temperature range 40-200 degrees C. Carbon monoxide inhibits...... the sticking probability significantly for all the metals, even at 200 degrees C. In the presence of 10 ppm CO, the sticking probability increases in the order It, Pt, Ni, Co, Pd, Rh, Ru, whereas for Cu, it is below the detection limit of the measurement, even in pure H2. The sticking probability for H2...
Generating intrinsically disordered protein conformational ensembles from a Markov chain
Cukier, Robert I.
2018-03-01
Intrinsically disordered proteins (IDPs) sample a diverse conformational space. They are important to signaling and regulatory pathways in cells. An entropy penalty must be payed when an IDP becomes ordered upon interaction with another protein or a ligand. Thus, the degree of conformational disorder of an IDP is of interest. We create a dichotomic Markov model that can explore entropic features of an IDP. The Markov condition introduces local (neighbor residues in a protein sequence) rotamer dependences that arise from van der Waals and other chemical constraints. A protein sequence of length N is characterized by its (information) entropy and mutual information, MIMC, the latter providing a measure of the dependence among the random variables describing the rotamer probabilities of the residues that comprise the sequence. For a Markov chain, the MIMC is proportional to the pair mutual information MI which depends on the singlet and pair probabilities of neighbor residue rotamer sampling. All 2N sequence states are generated, along with their probabilities, and contrasted with the probabilities under the assumption of independent residues. An efficient method to generate realizations of the chain is also provided. The chain entropy, MIMC, and state probabilities provide the ingredients to distinguish different scenarios using the terminologies: MoRF (molecular recognition feature), not-MoRF, and not-IDP. A MoRF corresponds to large entropy and large MIMC (strong dependence among the residues' rotamer sampling), a not-MoRF corresponds to large entropy but small MIMC, and not-IDP corresponds to low entropy irrespective of the MIMC. We show that MorFs are most appropriate as descriptors of IDPs. They provide a reasonable number of high-population states that reflect the dependences between neighbor residues, thus classifying them as IDPs, yet without very large entropy that might lead to a too high entropy penalty.
Turner, Sean; Galelli, Stefano; Wilcox, Karen
2015-04-01
Water reservoir systems are often affected by recurring large-scale ocean-atmospheric anomalies, known as teleconnections, that cause prolonged periods of climatological drought. Accurate forecasts of these events -- at lead times in the order of weeks and months -- may enable reservoir operators to take more effective release decisions to improve the performance of their systems. In practice this might mean a more reliable water supply system, a more profitable hydropower plant or a more sustainable environmental release policy. To this end, climate indices, which represent the oscillation of the ocean-atmospheric system, might be gainfully employed within reservoir operating models that adapt the reservoir operation as a function of the climate condition. This study develops a Stochastic Dynamic Programming (SDP) approach that can incorporate climate indices using a Hidden Markov Model. The model simulates the climatic regime as a hidden state following a Markov chain, with the state transitions driven by variation in climatic indices, such as the Southern Oscillation Index. Time series analysis of recorded streamflow data reveals the parameters of separate autoregressive models that describe the inflow to the reservoir under three representative climate states ("normal", "wet", "dry"). These models then define inflow transition probabilities for use in a classic SDP approach. The key advantage of the Hidden Markov Model is that it allows conditioning the operating policy not only on the reservoir storage and the antecedent inflow, but also on the climate condition, thus potentially allowing adaptability to a broader range of climate conditions. In practice, the reservoir operator would effect a water release tailored to a specific climate state based on available teleconnection data and forecasts. The approach is demonstrated on the operation of a realistic, stylised water reservoir with carry-over capacity in South-East Australia. Here teleconnections relating
A Markov decision process for managing habitat for Florida scrub-jays
Johnson, Fred A.; Breininger, David R.; Duncan, Brean W.; Nichols, James D.; Runge, Michael C.; Williams, B. Ken
2011-01-01
Florida scrub-jays Aphelocoma coerulescens are listed as threatened under the Endangered Species Act due to loss and degradation of scrub habitat. This study concerned the development of an optimal strategy for the restoration and management of scrub habitat at Merritt Island National Wildlife Refuge, which contains one of the few remaining large populations of scrub-jays in Florida. There are documented differences in the reproductive and survival rates of scrubjays among discrete classes of scrub height (strategy that would maximize the long-term growth rate of the resident scrub-jay population. We used aerial imagery with multistate Markov models to estimate annual transition probabilities among the four scrub-height classes under three possible management actions: scrub restoration (mechanical cutting followed by burning), a prescribed burn, or no intervention. A strategy prescribing the optimal management action for management units exhibiting different proportions of scrub-height classes was derived using dynamic programming. Scrub restoration was the optimal management action only in units dominated by mixed and tall scrub, and burning tended to be the optimal action for intermediate levels of short scrub. The optimal action was to do nothing when the amount of short scrub was greater than 30%, because short scrub mostly transitions to optimal height scrub (i.e., that state with the highest demographic success of scrub-jays) in the absence of intervention. Monte Carlo simulation of the optimal policy suggested that some form of management would be required every year. We note, however, that estimates of scrub-height transition probabilities were subject to several sources of uncertainty, and so we explored the management implications of alternative sets of transition probabilities. Generally, our analysis demonstrated the difficulty of managing for a species that requires midsuccessional habitat, and suggests that innovative management tools may be needed to
Maximally reliable Markov chains under energy constraints.
Escola, Sean; Eisele, Michael; Miller, Kenneth; Paninski, Liam
2009-07-01
Signal-to-noise ratios in physical systems can be significantly degraded if the outputs of the systems are highly variable. Biological processes for which highly stereotyped signal generations are necessary features appear to have reduced their signal variabilities by employing multiple processing steps. To better understand why this multistep cascade structure might be desirable, we prove that the reliability of a signal generated by a multistate system with no memory (i.e., a Markov chain) is maximal if and only if the system topology is such that the process steps irreversibly through each state, with transition rates chosen such that an equal fraction of the total signal is generated in each state. Furthermore, our result indicates that by increasing the number of states, it is possible to arbitrarily increase the reliability of the system. In a physical system, however, an energy cost is associated with maintaining irreversible transitions, and this cost increases with the number of such transitions (i.e., the number of states). Thus, an infinite-length chain, which would be perfectly reliable, is infeasible. To model the effects of energy demands on the maximally reliable solution, we numerically optimize the topology under two distinct energy functions that penalize either irreversible transitions or incommunicability between states, respectively. In both cases, the solutions are essentially irreversible linear chains, but with upper bounds on the number of states set by the amount of available energy. We therefore conclude that a physical system for which signal reliability is important should employ a linear architecture, with the number of states (and thus the reliability) determined by the intrinsic energy constraints of the system.
Applying Markov Chains for NDVI Time Series Forecasting of Latvian Regions
Directory of Open Access Journals (Sweden)
Stepchenko Arthur
2015-12-01
Full Text Available Time series of earth observation based estimates of vegetation inform about variations in vegetation at the scale of Latvia. A vegetation index is an indicator that describes the amount of chlorophyll (the green mass and shows the relative density and health of vegetation. NDVI index is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. In this paper, we make a one-step-ahead prediction of 7-daily time series of NDVI index using Markov chains. The choice of a Markov chain is due to the fact that a Markov chain is a sequence of random variables where each variable is located in some state. And a Markov chain contains probabilities of moving from one state to other.
Un calcul de Viterbi pour un Modèle de Markov Caché Contraint
DEFF Research Database (Denmark)
Petit, Matthieu; Christiansen, Henning
2009-01-01
A hidden Markov model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with hidden states. This model has been widely used in speech recognition and biological sequence analysis. Viterbi algorithm has been proposed to compute the most probable value....... Several constraint techniques are used to reduce the search of the most probable value of hidden states of a constrained HMM. An implementation based on PRISM, a logic programming language for statistical modeling, is presented....
A scaling analysis of a cat and mouse Markov chain
Litvak, Nelli; Robert, Philippe
2012-01-01
If ($C_n$) a Markov chain on a discrete state space $S$, a Markov chain ($C_n, M_n$) on the product space $S \\times S$, the cat and mouse Markov chain, is constructed. The first coordinate of this Markov chain behaves like the original Markov chain and the second component changes only when both
Criterion of Semi-Markov Dependent Risk Model
Institute of Scientific and Technical Information of China (English)
Xiao Yun MO; Xiang Qun YANG
2014-01-01
A rigorous definition of semi-Markov dependent risk model is given. This model is a generalization of the Markov dependent risk model. A criterion and necessary conditions of semi-Markov dependent risk model are obtained. The results clarify relations between elements among semi-Markov dependent risk model more clear and are applicable for Markov dependent risk model.
Vinyard, David J; Zachary, Chase E; Ananyev, Gennady; Dismukes, G Charles
2013-07-01
Forty-three years ago, Kok and coworkers introduced a phenomenological model describing period-four oscillations in O2 flash yields during photosynthetic water oxidation (WOC), which had been first reported by Joliot and coworkers. The original two-parameter Kok model was subsequently extended in its level of complexity to better simulate diverse data sets, including intact cells and isolated PSII-WOCs, but at the expense of introducing physically unrealistic assumptions necessary to enable numerical solutions. To date, analytical solutions have been found only for symmetric Kok models (inefficiencies are equally probable for all intermediates, called "S-states"). However, it is widely accepted that S-state reaction steps are not identical and some are not reversible (by thermodynamic restraints) thereby causing asymmetric cycles. We have developed a mathematically more rigorous foundation that eliminates unphysical assumptions known to be in conflict with experiments and adopts a new experimental constraint on solutions. This new algorithm termed STEAMM for S-state Transition Eigenvalues of Asymmetric Markov Models enables solutions to models having fewer adjustable parameters and uses automated fitting to experimental data sets, yielding higher accuracy and precision than the classic Kok or extended Kok models. This new tool provides a general mathematical framework for analyzing damped oscillations arising from any cycle period using any appropriate Markov model, regardless of symmetry. We illustrate applications of STEAMM that better describe the intrinsic inefficiencies for photon-to-charge conversion within PSII-WOCs that are responsible for damped period-four and period-two oscillations of flash O2 yields across diverse species, while using simpler Markov models free from unrealistic assumptions. Copyright © 2013 Elsevier B.V. All rights reserved.
spMC: an R-package for 3D lithological reconstructions based on spatial Markov chains
Sartore, Luca; Fabbri, Paolo; Gaetan, Carlo
2016-09-01
The paper presents the spatial Markov Chains (spMC) R-package and a case study of subsoil simulation/prediction located in a plain site of Northeastern Italy. spMC is a quite complete collection of advanced methods for data inspection, besides spMC implements Markov Chain models to estimate experimental transition probabilities of categorical lithological data. Furthermore, simulation methods based on most known prediction methods (as indicator Kriging and CoKriging) were implemented in spMC package. Moreover, other more advanced methods are available for simulations, e.g. path methods and Bayesian procedures, that exploit the maximum entropy. Since the spMC package was developed for intensive geostatistical computations, part of the code is implemented for parallel computations via the OpenMP constructs. A final analysis of this computational efficiency compares the simulation/prediction algorithms by using different numbers of CPU cores, and considering the example data set of the case study included in the package.
A Markov State-based Quantitative Kinetic Model of Sodium Release from the Dopamine Transporter
Razavi, Asghar M.; Khelashvili, George; Weinstein, Harel
2017-01-01
The dopamine transporter (DAT) belongs to the neurotransmitter:sodium symporter (NSS) family of membrane proteins that are responsible for reuptake of neurotransmitters from the synaptic cleft to terminate a neuronal signal and enable subsequent neurotransmitter release from the presynaptic neuron. The release of one sodium ion from the crystallographically determined sodium binding site Na2 had been identified as an initial step in the transport cycle which prepares the transporter for substrate translocation by stabilizing an inward-open conformation. We have constructed Markov State Models (MSMs) from extensive molecular dynamics simulations of human DAT (hDAT) to explore the mechanism of this sodium release. Our results quantify the release process triggered by hydration of the Na2 site that occurs concomitantly with a conformational transition from an outward-facing to an inward-facing state of the transporter. The kinetics of the release process are computed from the MSM, and transition path theory is used to identify the most probable sodium release pathways. An intermediate state is discovered on the sodium release pathway, and the results reveal the importance of various modes of interaction of the N-terminus of hDAT in controlling the pathways of release.
Reliability analysis and prediction of mixed mode load using Markov Chain Model
International Nuclear Information System (INIS)
Nikabdullah, N.; Singh, S. S. K.; Alebrahim, R.; Azizi, M. A.; K, Elwaleed A.; Noorani, M. S. M.
2014-01-01
The aim of this paper is to present the reliability analysis and prediction of mixed mode loading by using a simple two state Markov Chain Model for an automotive crankshaft. The reliability analysis and prediction for any automotive component or structure is important for analyzing and measuring the failure to increase the design life, eliminate or reduce the likelihood of failures and safety risk. The mechanical failures of the crankshaft are due of high bending and torsion stress concentration from high cycle and low rotating bending and torsional stress. The Markov Chain was used to model the two states based on the probability of failure due to bending and torsion stress. In most investigations it revealed that bending stress is much serve than torsional stress, therefore the probability criteria for the bending state would be higher compared to the torsion state. A statistical comparison between the developed Markov Chain Model and field data was done to observe the percentage of error. The reliability analysis and prediction was derived and illustrated from the Markov Chain Model were shown in the Weibull probability and cumulative distribution function, hazard rate and reliability curve and the bathtub curve. It can be concluded that Markov Chain Model has the ability to generate near similar data with minimal percentage of error and for a practical application; the proposed model provides a good accuracy in determining the reliability for the crankshaft under mixed mode loading
Markov Decision Process Measurement Model.
LaMar, Michelle M
2018-03-01
Within-task actions can provide additional information on student competencies but are challenging to model. This paper explores the potential of using a cognitive model for decision making, the Markov decision process, to provide a mapping between within-task actions and latent traits of interest. Psychometric properties of the model are explored, and simulation studies report on parameter recovery within the context of a simple strategy game. The model is then applied to empirical data from an educational game. Estimates from the model are found to correlate more strongly with posttest results than a partial-credit IRT model based on outcome data alone.