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Sample records for continuous-time stochastic multivariable

  1. Stochastic volatility of volatility in continuous time

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole; Veraart, Almut

    This paper introduces the concept of stochastic volatility of volatility in continuous time and, hence, extends standard stochastic volatility (SV) models to allow for an additional source of randomness associated with greater variability in the data. We discuss how stochastic volatility...... of volatility can be defined both non-parametrically, where we link it to the quadratic variation of the stochastic variance process, and parametrically, where we propose two new SV models which allow for stochastic volatility of volatility. In addition, we show that volatility of volatility can be estimated...

  2. Stochastic calculus for uncoupled continuous-time random walks.

    Science.gov (United States)

    Germano, Guido; Politi, Mauro; Scalas, Enrico; Schilling, René L

    2009-06-01

    The continuous-time random walk (CTRW) is a pure-jump stochastic process with several applications not only in physics but also in insurance, finance, and economics. A definition is given for a class of stochastic integrals driven by a CTRW, which includes the Itō and Stratonovich cases. An uncoupled CTRW with zero-mean jumps is a martingale. It is proved that, as a consequence of the martingale transform theorem, if the CTRW is a martingale, the Itō integral is a martingale too. It is shown how the definition of the stochastic integrals can be used to easily compute them by Monte Carlo simulation. The relations between a CTRW, its quadratic variation, its Stratonovich integral, and its Itō integral are highlighted by numerical calculations when the jumps in space of the CTRW have a symmetric Lévy alpha -stable distribution and its waiting times have a one-parameter Mittag-Leffler distribution. Remarkably, these distributions have fat tails and an unbounded quadratic variation. In the diffusive limit of vanishing scale parameters, the probability density of this kind of CTRW satisfies the space-time fractional diffusion equation (FDE) or more in general the fractional Fokker-Planck equation, which generalizes the standard diffusion equation, solved by the probability density of the Wiener process, and thus provides a phenomenologic model of anomalous diffusion. We also provide an analytic expression for the quadratic variation of the stochastic process described by the FDE and check it by Monte Carlo.

  3. Multivariate Discrete First Order Stochastic Dominance

    DEFF Research Database (Denmark)

    Tarp, Finn; Østerdal, Lars Peter

    This paper characterizes the principle of first order stochastic dominance in a multivariate discrete setting. We show that a distribution  f first order stochastic dominates distribution g if and only if  f can be obtained from g by iteratively shifting density from one outcome to another...

  4. Multivariate moment closure techniques for stochastic kinetic models

    International Nuclear Information System (INIS)

    Lakatos, Eszter; Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H.

    2015-01-01

    Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs

  5. Multivariate moment closure techniques for stochastic kinetic models

    Energy Technology Data Exchange (ETDEWEB)

    Lakatos, Eszter, E-mail: e.lakatos13@imperial.ac.uk; Ale, Angelique; Kirk, Paul D. W.; Stumpf, Michael P. H., E-mail: m.stumpf@imperial.ac.uk [Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ (United Kingdom)

    2015-09-07

    Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporally evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.

  6. Continuous-Time Mean-Variance Portfolio Selection: A Stochastic LQ Framework

    International Nuclear Information System (INIS)

    Zhou, X.Y.; Li, D.

    2000-01-01

    This paper is concerned with a continuous-time mean-variance portfolio selection model that is formulated as a bicriteria optimization problem. The objective is to maximize the expected terminal return and minimize the variance of the terminal wealth. By putting weights on the two criteria one obtains a single objective stochastic control problem which is however not in the standard form due to the variance term involved. It is shown that this nonstandard problem can be 'embedded' into a class of auxiliary stochastic linear-quadratic (LQ) problems. The stochastic LQ control model proves to be an appropriate and effective framework to study the mean-variance problem in light of the recent development on general stochastic LQ problems with indefinite control weighting matrices. This gives rise to the efficient frontier in a closed form for the original portfolio selection problem

  7. A stochastic surplus production model in continuous time

    DEFF Research Database (Denmark)

    Pedersen, Martin Wæver; Berg, Casper Willestofte

    2017-01-01

    surplus production model in continuous time (SPiCT), which in addition to stock dynamics also models the dynamics of the fisheries. This enables error in the catch process to be reflected in the uncertainty of estimated model parameters and management quantities. Benefits of the continuous-time state......Surplus production modelling has a long history as a method for managing data-limited fish stocks. Recent advancements have cast surplus production models as state-space models that separate random variability of stock dynamics from error in observed indices of biomass. We present a stochastic......-space model formulation include the ability to provide estimates of exploitable biomass and fishing mortality at any point in time from data sampled at arbitrary and possibly irregular intervals. We show in a simulation that the ability to analyse subannual data can increase the effective sample size...

  8. Model Checking Multivariate State Rewards

    DEFF Research Database (Denmark)

    Nielsen, Bo Friis; Nielson, Flemming; Nielson, Hanne Riis

    2010-01-01

    We consider continuous stochastic logics with state rewards that are interpreted over continuous time Markov chains. We show how results from multivariate phase type distributions can be used to obtain higher-order moments for multivariate state rewards (including covariance). We also generalise...

  9. An introduction to continuous-time stochastic processes theory, models, and applications to finance, biology, and medicine

    CERN Document Server

    Capasso, Vincenzo

    2015-01-01

    This textbook, now in its third edition, offers a rigorous and self-contained introduction to the theory of continuous-time stochastic processes, stochastic integrals, and stochastic differential equations. Expertly balancing theory and applications, the work features concrete examples of modeling real-world problems from biology, medicine, industrial applications, finance, and insurance using stochastic methods. No previous knowledge of stochastic processes is required. Key topics include: * Markov processes * Stochastic differential equations * Arbitrage-free markets and financial derivatives * Insurance risk * Population dynamics, and epidemics * Agent-based models New to the Third Edition: * Infinitely divisible distributions * Random measures * Levy processes * Fractional Brownian motion * Ergodic theory * Karhunen-Loeve expansion * Additional applications * Additional  exercises * Smoluchowski  approximation of  Langevin systems An Introduction to Continuous-Time Stochastic Processes, Third Editio...

  10. Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

    Science.gov (United States)

    zhang, L.

    2011-12-01

    Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be

  11. Continuous-Time Public Good Contribution Under Uncertainty: A Stochastic Control Approach

    International Nuclear Information System (INIS)

    Ferrari, Giorgio; Riedel, Frank; Steg, Jan-Henrik

    2017-01-01

    In this paper we study continuous-time stochastic control problems with both monotone and classical controls motivated by the so-called public good contribution problem. That is the problem of n economic agents aiming to maximize their expected utility allocating initial wealth over a given time period between private consumption and irreversible contributions to increase the level of some public good. We investigate the corresponding social planner problem and the case of strategic interaction between the agents, i.e. the public good contribution game. We show existence and uniqueness of the social planner’s optimal policy, we characterize it by necessary and sufficient stochastic Kuhn–Tucker conditions and we provide its expression in terms of the unique optional solution of a stochastic backward equation. Similar stochastic first order conditions prove to be very useful for studying any Nash equilibria of the public good contribution game. In the symmetric case they allow us to prove (qualitative) uniqueness of the Nash equilibrium, which we again construct as the unique optional solution of a stochastic backward equation. We finally also provide a detailed analysis of the so-called free rider effect.

  12. Continuous-Time Public Good Contribution Under Uncertainty: A Stochastic Control Approach

    Energy Technology Data Exchange (ETDEWEB)

    Ferrari, Giorgio, E-mail: giorgio.ferrari@uni-bielefeld.de; Riedel, Frank, E-mail: frank.riedel@uni-bielefeld.de; Steg, Jan-Henrik, E-mail: jsteg@uni-bielefeld.de [Bielefeld University, Center for Mathematical Economics (Germany)

    2017-06-15

    In this paper we study continuous-time stochastic control problems with both monotone and classical controls motivated by the so-called public good contribution problem. That is the problem of n economic agents aiming to maximize their expected utility allocating initial wealth over a given time period between private consumption and irreversible contributions to increase the level of some public good. We investigate the corresponding social planner problem and the case of strategic interaction between the agents, i.e. the public good contribution game. We show existence and uniqueness of the social planner’s optimal policy, we characterize it by necessary and sufficient stochastic Kuhn–Tucker conditions and we provide its expression in terms of the unique optional solution of a stochastic backward equation. Similar stochastic first order conditions prove to be very useful for studying any Nash equilibria of the public good contribution game. In the symmetric case they allow us to prove (qualitative) uniqueness of the Nash equilibrium, which we again construct as the unique optional solution of a stochastic backward equation. We finally also provide a detailed analysis of the so-called free rider effect.

  13. A Fractionally Integrated Wishart Stochastic Volatility Model

    NARCIS (Netherlands)

    M. Asai (Manabu); M.J. McAleer (Michael)

    2013-01-01

    textabstractThere has recently been growing interest in modeling and estimating alternative continuous time multivariate stochastic volatility models. We propose a continuous time fractionally integrated Wishart stochastic volatility (FIWSV) process. We derive the conditional Laplace transform of

  14. Absolute continuity under time shift of trajectories and related stochastic calculus

    CERN Document Server

    Löbus, Jörg-Uwe

    2017-01-01

    The text is concerned with a class of two-sided stochastic processes of the form X=W+A. Here W is a two-sided Brownian motion with random initial data at time zero and A\\equiv A(W) is a function of W. Elements of the related stochastic calculus are introduced. In particular, the calculus is adjusted to the case when A is a jump process. Absolute continuity of (X,P) under time shift of trajectories is investigated. For example under various conditions on the initial density with respect to the Lebesgue measure, m, and on A with A_0=0 we verify \\frac{P(dX_{\\cdot -t})}{P(dX_\\cdot)}=\\frac{m(X_{-t})}{m(X_0)}\\cdot \\prod_i\\left|\

  15. The multivariate supOU stochastic volatility model

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole; Stelzer, Robert

    Using positive semidefinite supOU (superposition of Ornstein-Uhlenbeck type) processes to describe the volatility, we introduce a multivariate stochastic volatility model for financial data which is capable of modelling long range dependence effects. The finiteness of moments and the second order...... structure of the volatility, the log returns, as well as their "squares" are discussed in detail. Moreover, we give several examples in which long memory effects occur and study how the model as well as the simple Ornstein-Uhlenbeck type stochastic volatility model behave under linear transformations....... In particular, the models are shown to be preserved under invertible linear transformations. Finally, we discuss how (sup)OU stochastic volatility models can be combined with a factor modelling approach....

  16. Perturbation theory for continuous stochastic equations

    International Nuclear Information System (INIS)

    Chechetkin, V.R.; Lutovinov, V.S.

    1987-01-01

    The various general perturbational schemes for continuous stochastic equations are considered. These schemes have many analogous features with the iterational solution of Schwinger equation for S-matrix. The following problems are discussed: continuous stochastic evolution equations for probability distribution functionals, evolution equations for equal time correlators, perturbation theory for Gaussian and Poissonian additive noise, perturbation theory for birth and death processes, stochastic properties of systems with multiplicative noise. The general results are illustrated by diffusion-controlled reactions, fluctuations in closed systems with chemical processes, propagation of waves in random media in parabolic equation approximation, and non-equilibrium phase transitions in systems with Poissonian breeding centers. The rate of irreversible reaction X + X → A (Smoluchowski process) is calculated with the use of general theory based on continuous stochastic equations for birth and death processes. The threshold criterion and range of fluctuational region for synergetic phase transition in system with Poissonian breeding centers are also considered. (author)

  17. Infinite time interval backward stochastic differential equations with continuous coefficients.

    Science.gov (United States)

    Zong, Zhaojun; Hu, Feng

    2016-01-01

    In this paper, we study the existence theorem for [Formula: see text] [Formula: see text] solutions to a class of 1-dimensional infinite time interval backward stochastic differential equations (BSDEs) under the conditions that the coefficients are continuous and have linear growths. We also obtain the existence of a minimal solution. Furthermore, we study the existence and uniqueness theorem for [Formula: see text] [Formula: see text] solutions of infinite time interval BSDEs with non-uniformly Lipschitz coefficients. It should be pointed out that the assumptions of this result is weaker than that of Theorem 3.1 in Zong (Turkish J Math 37:704-718, 2013).

  18. Analytical Assessment for Transient Stability Under Stochastic Continuous Disturbances

    Energy Technology Data Exchange (ETDEWEB)

    Ju, Ping [Hohai Univ., Nanjing (China); Li, Hongyu [Hohai Univ., Nanjing (China); Gan, Chun [The Univ. of Tennessee, Knoxville, TN (United States); Liu, Yong [The Univ. of Tennessee, Knoxville, TN (United States); Yu, Yiping [Hohai Univ., Nanjing (China); Liu, Yilu [Univ. of Tennessee, Knoxville, TN (United States)

    2017-06-28

    Here, with the growing integration of renewable power generation, plug-in electric vehicles, and other sources of uncertainty, increasing stochastic continuous disturbances are brought to power systems. The impact of stochastic continuous disturbances on power system transient stability attracts significant attention. To address this problem, this paper proposes an analytical assessment method for transient stability of multi-machine power systems under stochastic continuous disturbances. In the proposed method, a probability measure of transient stability is presented and analytically solved by stochastic averaging. Compared with the conventional method (Monte Carlo simulation), the proposed method is many orders of magnitude faster, which makes it very attractive in practice when many plans for transient stability must be compared or when transient stability must be analyzed quickly. Also, it is found that the evolution of system energy over time is almost a simple diffusion process by the proposed method, which explains the impact mechanism of stochastic continuous disturbances on transient stability in theory.

  19. A METHODOLOGY FOR THE CHOICE OF THE BEST FITTING CONTINUOUS-TIME STOCHASTIC MODELS OF CRUDE OIL PRICE: THE CASE OF RUSSIA

    Directory of Open Access Journals (Sweden)

    Hamidreza Mostafaei

    2013-01-01

    Full Text Available In this study, it has been attempted to select the best continuous- time stochastic model, in order to describe and forecast the oil price of Russia, by information and statistics about oil price that has been available for oil price in the past. For this purpose, method of The Maximum Likelihood Estimation is implemented for estimation of the parameters of continuous-time stochastic processes. The result of unit root test with a structural break, reveals that time series of the crude oil price is a stationary series. The simulation of continuous-time stochastic processes and the mean square error between the simulated prices and the market ones shows that the Geometric Brownian Motion is the best model for the Russian crude oil price.

  20. Superior memory efficiency of quantum devices for the simulation of continuous-time stochastic processes

    Science.gov (United States)

    Elliott, Thomas J.; Gu, Mile

    2018-03-01

    Continuous-time stochastic processes pervade everyday experience, and the simulation of models of these processes is of great utility. Classical models of systems operating in continuous-time must typically track an unbounded amount of information about past behaviour, even for relatively simple models, enforcing limits on precision due to the finite memory of the machine. However, quantum machines can require less information about the past than even their optimal classical counterparts to simulate the future of discrete-time processes, and we demonstrate that this advantage extends to the continuous-time regime. Moreover, we show that this reduction in the memory requirement can be unboundedly large, allowing for arbitrary precision even with a finite quantum memory. We provide a systematic method for finding superior quantum constructions, and a protocol for analogue simulation of continuous-time renewal processes with a quantum machine.

  1. Stochastic analysis in discrete and continuous settings with normal martingales

    CERN Document Server

    Privault, Nicolas

    2009-01-01

    This volume gives a unified presentation of stochastic analysis for continuous and discontinuous stochastic processes, in both discrete and continuous time. It is mostly self-contained and accessible to graduate students and researchers having already received a basic training in probability. The simultaneous treatment of continuous and jump processes is done in the framework of normal martingales; that includes the Brownian motion and compensated Poisson processes as specific cases. In particular, the basic tools of stochastic analysis (chaos representation, gradient, divergence, integration by parts) are presented in this general setting. Applications are given to functional and deviation inequalities and mathematical finance.

  2. Oil and stock market volatility: A multivariate stochastic volatility perspective

    International Nuclear Information System (INIS)

    Vo, Minh

    2011-01-01

    This paper models the volatility of stock and oil futures markets using the multivariate stochastic volatility structure in an attempt to extract information intertwined in both markets for risk prediction. It offers four major findings. First, the stock and oil futures prices are inter-related. Their correlation follows a time-varying dynamic process and tends to increase when the markets are more volatile. Second, conditioned on the past information, the volatility in each market is very persistent, i.e., it varies in a predictable manner. Third, there is inter-market dependence in volatility. Innovations that hit either market can affect the volatility in the other market. In other words, conditioned on the persistence and the past volatility in their respective markets, the past volatility of the stock (oil futures) market also has predictive power over the future volatility of the oil futures (stock) market. Finally, the model produces more accurate Value-at-Risk estimates than other benchmarks commonly used in the financial industry. - Research Highlights: → This paper models the volatility of stock and oil futures markets using the multivariate stochastic volatility model. → The correlation between the two markets follows a time-varying dynamic process which tends to increase when the markets are more volatile. → The volatility in each market is very persistent. → Innovations that hit either market can affect the volatility in the other market. → The model produces more accurate Value-at-Risk estimates than other benchmarks commonly used in the financial industry.

  3. Time change

    DEFF Research Database (Denmark)

    Veraart, Almut; Winkel, Matthias

    2010-01-01

    The mathematical operation of time-changing continuous-time stochastic processes can be regarded as a standard method for building financial models. We briefly review the theory on time-changed stochastic processes and relate them to stochastic volatility models in finance. Popular models......, including time-changed Lévy processes, where the time-change process is given by a subordinator or an absolutely continuous time change, are presented. Finally, we discuss the potential and the limitations of using such processes for constructing multivariate financial models....

  4. Multivariable controller for discrete stochastic amplitude-constrained systems

    Directory of Open Access Journals (Sweden)

    Hannu T. Toivonen

    1983-04-01

    Full Text Available A sub-optimal multivariable controller for discrete stochastic amplitude-constrained systems is presented. In the approach the regulator structure is restricted to the class of linear saturated feedback laws. The stationary covariances of the controlled system are evaluated by approximating the stationary probability distribution of the state by a gaussian distribution. An algorithm for minimizing a quadratic loss function is given, and examples are presented to illustrate the performance of the sub-optimal controller.

  5. Continuous multivariate exponential extension

    International Nuclear Information System (INIS)

    Block, H.W.

    1975-01-01

    The Freund-Weinman multivariate exponential extension is generalized to the case of nonidentically distributed marginal distributions. A fatal shock model is given for the resulting distribution. Results in the bivariate case and the concept of constant multivariate hazard rate lead to a continuous distribution related to the multivariate exponential distribution (MVE) of Marshall and Olkin. This distribution is shown to be a special case of the extended Freund-Weinman distribution. A generalization of the bivariate model of Proschan and Sullo leads to a distribution which contains both the extended Freund-Weinman distribution and the MVE

  6. Simulation of multivariate stationary stochastic processes using dimension-reduction representation methods

    Science.gov (United States)

    Liu, Zhangjun; Liu, Zenghui; Peng, Yongbo

    2018-03-01

    In view of the Fourier-Stieltjes integral formula of multivariate stationary stochastic processes, a unified formulation accommodating spectral representation method (SRM) and proper orthogonal decomposition (POD) is deduced. By introducing random functions as constraints correlating the orthogonal random variables involved in the unified formulation, the dimension-reduction spectral representation method (DR-SRM) and the dimension-reduction proper orthogonal decomposition (DR-POD) are addressed. The proposed schemes are capable of representing the multivariate stationary stochastic process with a few elementary random variables, bypassing the challenges of high-dimensional random variables inherent in the conventional Monte Carlo methods. In order to accelerate the numerical simulation, the technique of Fast Fourier Transform (FFT) is integrated with the proposed schemes. For illustrative purposes, the simulation of horizontal wind velocity field along the deck of a large-span bridge is proceeded using the proposed methods containing 2 and 3 elementary random variables. Numerical simulation reveals the usefulness of the dimension-reduction representation methods.

  7. The mass transfer approach to multivariate discrete first order stochastic dominance

    DEFF Research Database (Denmark)

    Østerdal, Lars Peter Raahave

    2010-01-01

    A fundamental result in the theory of stochastic dominance tells that first order dominance between two finite multivariate distributions is equivalent to the property that the one can be obtained from the other by shifting probability mass from one outcome to another that is worse a finite numbe...

  8. Electricity price modeling with stochastic time change

    International Nuclear Information System (INIS)

    Borovkova, Svetlana; Schmeck, Maren Diane

    2017-01-01

    In this paper, we develop a novel approach to electricity price modeling, based on the powerful technique of stochastic time change. This technique allows us to incorporate the characteristic features of electricity prices (such as seasonal volatility, time varying mean reversion and seasonally occurring price spikes) into the model in an elegant and economically justifiable way. The stochastic time change introduces stochastic as well as deterministic (e.g., seasonal) features in the price process' volatility and in the jump component. We specify the base process as a mean reverting jump diffusion and the time change as an absolutely continuous stochastic process with seasonal component. The activity rate of the stochastic time change can be related to the factors that influence supply and demand. Here we use the temperature as a proxy for the demand and hence, as the driving factor of the stochastic time change, and show that this choice leads to realistic price paths. We derive properties of the resulting price process and develop the model calibration procedure. We calibrate the model to the historical EEX power prices and apply it to generating realistic price paths by Monte Carlo simulations. We show that the simulated price process matches the distributional characteristics of the observed electricity prices in periods of both high and low demand. - Highlights: • We develop a novel approach to electricity price modeling, based on the powerful technique of stochastic time change. • We incorporate the characteristic features of electricity prices, such as seasonal volatility and spikes into the model. • We use the temperature as a proxy for the demand and hence, as the driving factor of the stochastic time change • We derive properties of the resulting price process and develop the model calibration procedure. • We calibrate the model to the historical EEX power prices and apply it to generating realistic price paths.

  9. Model checking conditional CSL for continuous-time Markov chains

    DEFF Research Database (Denmark)

    Gao, Yang; Xu, Ming; Zhan, Naijun

    2013-01-01

    In this paper, we consider the model-checking problem of continuous-time Markov chains (CTMCs) with respect to conditional logic. To the end, we extend Continuous Stochastic Logic introduced in Aziz et al. (2000) [1] to Conditional Continuous Stochastic Logic (CCSL) by introducing a conditional...

  10. Novel delay-distribution-dependent stability analysis for continuous-time recurrent neural networks with stochastic delay

    International Nuclear Information System (INIS)

    Wang Shen-Quan; Feng Jian; Zhao Qing

    2012-01-01

    In this paper, the problem of delay-distribution-dependent stability is investigated for continuous-time recurrent neural networks (CRNNs) with stochastic delay. Different from the common assumptions on time delays, it is assumed that the probability distribution of the delay taking values in some intervals is known a priori. By making full use of the information concerning the probability distribution of the delay and by using a tighter bounding technique (the reciprocally convex combination method), less conservative asymptotic mean-square stable sufficient conditions are derived in terms of linear matrix inequalities (LMIs). Two numerical examples show that our results are better than the existing ones. (general)

  11. Computer Aided Continuous Time Stochastic Process Modelling

    DEFF Research Database (Denmark)

    Kristensen, N.R.; Madsen, Henrik; Jørgensen, Sten Bay

    2001-01-01

    A grey-box approach to process modelling that combines deterministic and stochastic modelling is advocated for identification of models for model-based control of batch and semi-batch processes. A computer-aided tool designed for supporting decision-making within the corresponding modelling cycle...

  12. An approach to the drone fleet survivability assessment based on a stochastic continues-time model

    Science.gov (United States)

    Kharchenko, Vyacheslav; Fesenko, Herman; Doukas, Nikos

    2017-09-01

    An approach and the algorithm to the drone fleet survivability assessment based on a stochastic continues-time model are proposed. The input data are the number of the drones, the drone fleet redundancy coefficient, the drone stability and restoration rate, the limit deviation from the norms of the drone fleet recovery, the drone fleet operational availability coefficient, the probability of the drone failure-free operation, time needed for performing the required tasks by the drone fleet. The ways for improving the recoverable drone fleet survivability taking into account amazing factors of system accident are suggested. Dependencies of the drone fleet survivability rate both on the drone stability and the number of the drones are analysed.

  13. Hierarchical Hidden Markov Models for Multivariate Integer-Valued Time-Series

    DEFF Research Database (Denmark)

    Catania, Leopoldo; Di Mari, Roberto

    2018-01-01

    We propose a new flexible dynamic model for multivariate nonnegative integer-valued time-series. Observations are assumed to depend on the realization of two additional unobserved integer-valued stochastic variables which control for the time-and cross-dependence of the data. An Expectation......-Maximization algorithm for maximum likelihood estimation of the model's parameters is derived. We provide conditional and unconditional (cross)-moments implied by the model, as well as the limiting distribution of the series. A Monte Carlo experiment investigates the finite sample properties of our estimation...

  14. The method of separation for evolutionary spectral density estimation of multi-variate and multi-dimensional non-stationary stochastic processes

    KAUST Repository

    Schillinger, Dominik

    2013-07-01

    The method of separation can be used as a non-parametric estimation technique, especially suitable for evolutionary spectral density functions of uniformly modulated and strongly narrow-band stochastic processes. The paper at hand provides a consistent derivation of method of separation based spectrum estimation for the general multi-variate and multi-dimensional case. The validity of the method is demonstrated by benchmark tests with uniformly modulated spectra, for which convergence to the analytical solution is demonstrated. The key advantage of the method of separation is the minimization of spectral dispersion due to optimum time- or space-frequency localization. This is illustrated by the calibration of multi-dimensional and multi-variate geometric imperfection models from strongly narrow-band measurements in I-beams and cylindrical shells. Finally, the application of the method of separation based estimates for the stochastic buckling analysis of the example structures is briefly discussed. © 2013 Elsevier Ltd.

  15. Multivariable biorthogonal continuous--discrete Wilson and Racah polynomials

    International Nuclear Information System (INIS)

    Tratnik, M.V.

    1990-01-01

    Several families of multivariable, biorthogonal, partly continuous and partly discrete, Wilson polynomials are presented. These yield limit cases that are purely continuous in some of the variables and purely discrete in the others, or purely discrete in all the variables. The latter are referred to as the multivariable biorthogonal Racah polynomials. Interesting further limit cases include the multivariable biorthogonal Hahn and dual Hahn polynomials

  16. Stability of continuous-time quantum filters with measurement imperfections

    Science.gov (United States)

    Amini, H.; Pellegrini, C.; Rouchon, P.

    2014-07-01

    The fidelity between the state of a continuously observed quantum system and the state of its associated quantum filter, is shown to be always a submartingale. The observed system is assumed to be governed by a continuous-time Stochastic Master Equation (SME), driven simultaneously by Wiener and Poisson processes and that takes into account incompleteness and errors in measurements. This stability result is the continuous-time counterpart of a similar stability result already established for discrete-time quantum systems and where the measurement imperfections are modelled by a left stochastic matrix.

  17. Space-time-modulated stochastic processes

    Science.gov (United States)

    Giona, Massimiliano

    2017-10-01

    Starting from the physical problem associated with the Lorentzian transformation of a Poisson-Kac process in inertial frames, the concept of space-time-modulated stochastic processes is introduced for processes possessing finite propagation velocity. This class of stochastic processes provides a two-way coupling between the stochastic perturbation acting on a physical observable and the evolution of the physical observable itself, which in turn influences the statistical properties of the stochastic perturbation during its evolution. The definition of space-time-modulated processes requires the introduction of two functions: a nonlinear amplitude modulation, controlling the intensity of the stochastic perturbation, and a time-horizon function, which modulates its statistical properties, providing irreducible feedback between the stochastic perturbation and the physical observable influenced by it. The latter property is the peculiar fingerprint of this class of models that makes them suitable for extension to generic curved-space times. Considering Poisson-Kac processes as prototypical examples of stochastic processes possessing finite propagation velocity, the balance equations for the probability density functions associated with their space-time modulations are derived. Several examples highlighting the peculiarities of space-time-modulated processes are thoroughly analyzed.

  18. Relative entropy and waiting time for continuous-time Markov processes

    NARCIS (Netherlands)

    Chazottes, J.R.; Giardinà, C.; Redig, F.H.J.

    2006-01-01

    For discrete-time stochastic processes, there is a close connection between return (resp. waiting) times and entropy (resp. relative entropy). Such a connection cannot be straightforwardly extended to the continuous-time setting. Contrarily to the discrete-time case one needs a reference measure on

  19. Modeling and Properties of Nonlinear Stochastic Dynamical System of Continuous Culture

    Science.gov (United States)

    Wang, Lei; Feng, Enmin; Ye, Jianxiong; Xiu, Zhilong

    The stochastic counterpart to the deterministic description of continuous fermentation with ordinary differential equation is investigated in the process of glycerol bio-dissimilation to 1,3-propanediol by Klebsiella pneumoniae. We briefly discuss the continuous fermentation process driven by three-dimensional Brownian motion and Lipschitz coefficients, which is suitable for the factual fermentation. Subsequently, we study the existence and uniqueness of solutions for the stochastic system as well as the boundedness of the Two-order Moment and the Markov property of the solution. Finally stochastic simulation is carried out under the Stochastic Euler-Maruyama method.

  20. Stochastic Dynamic AC Optimal Power Flow Based on a Multivariate Short-Term Wind Power Scenario Forecasting Model

    Directory of Open Access Journals (Sweden)

    Wenlei Bai

    2017-12-01

    Full Text Available The deterministic methods generally used to solve DC optimal power flow (OPF do not fully capture the uncertainty information in wind power, and thus their solutions could be suboptimal. However, the stochastic dynamic AC OPF problem can be used to find an optimal solution by fully capturing the uncertainty information of wind power. That uncertainty information of future wind power can be well represented by the short-term future wind power scenarios that are forecasted using the generalized dynamic factor model (GDFM—a novel multivariate statistical wind power forecasting model. Furthermore, the GDFM can accurately represent the spatial and temporal correlations among wind farms through the multivariate stochastic process. Fully capturing the uncertainty information in the spatially and temporally correlated GDFM scenarios can lead to a better AC OPF solution under a high penetration level of wind power. Since the GDFM is a factor analysis based model, the computational time can also be reduced. In order to further reduce the computational time, a modified artificial bee colony (ABC algorithm is used to solve the AC OPF problem based on the GDFM forecasting scenarios. Using the modified ABC algorithm based on the GDFM forecasting scenarios has resulted in better AC OPF’ solutions on an IEEE 118-bus system at every hour for 24 h.

  1. Stochastic nature of series of waiting times

    Science.gov (United States)

    Anvari, Mehrnaz; Aghamohammadi, Cina; Dashti-Naserabadi, H.; Salehi, E.; Behjat, E.; Qorbani, M.; Khazaei Nezhad, M.; Zirak, M.; Hadjihosseini, Ali; Peinke, Joachim; Tabar, M. Reza Rahimi

    2013-06-01

    Although fluctuations in the waiting time series have been studied for a long time, some important issues such as its long-range memory and its stochastic features in the presence of nonstationarity have so far remained unstudied. Here we find that the “waiting times” series for a given increment level have long-range correlations with Hurst exponents belonging to the interval 1/2time distribution. We find that the logarithmic difference of waiting times series has a short-range correlation, and then we study its stochastic nature using the Markovian method and determine the corresponding Kramers-Moyal coefficients. As an example, we analyze the velocity fluctuations in high Reynolds number turbulence and determine the level dependence of Markov time scales, as well as the drift and diffusion coefficients. We show that the waiting time distributions exhibit power law tails, and we were able to model the distribution with a continuous time random walk.

  2. A note on continuous-time stochastic approximation in infinite dimensions

    Czech Academy of Sciences Publication Activity Database

    Seidler, Jan; Žák, F.

    2017-01-01

    Roč. 22, č. 1 (2017), č. článku 36. ISSN 1083-589X R&D Projects: GA ČR(CZ) GA15-08819S Institutional support: RVO:67985556 Keywords : stochastic approximation * stochastic parabolic problems * variational solutions Subject RIV: BA - General Mathematics OBOR OECD: Statistics and probability Impact factor: 0.416, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/seidler-0475647.pdf

  3. Time inconsistency and reputation in monetary policy: a strategic model in continuous time

    OpenAIRE

    Li, Jingyuan; Tian, Guoqiang

    2005-01-01

    This article develops a model to examine the equilibrium behavior of the time inconsistency problem in a continuous time economy with stochastic and endogenized dis- tortion. First, the authors introduce the notion of sequentially rational equilibrium, and show that the time inconsistency problem may be solved with trigger reputation strategies for stochastic setting. The conditions for the existence of sequentially rational equilibrium are provided. Then, the concept of sequen...

  4. Exponential stability for stochastic delayed recurrent neural networks with mixed time-varying delays and impulses: the continuous-time case

    International Nuclear Information System (INIS)

    Karthik Raja, U; Leelamani, A; Raja, R; Samidurai, R

    2013-01-01

    In this paper, the exponential stability for a class of stochastic neural networks with time-varying delays and impulsive effects is considered. By constructing suitable Lyapunov functionals and by using the linear matrix inequality optimization approach, we obtain sufficient delay-dependent criteria to ensure the exponential stability of stochastic neural networks with time-varying delays and impulses. Two numerical examples with simulation results are provided to illustrate the effectiveness of the obtained results over those already existing in the literature. (paper)

  5. Bayesian inference for hybrid discrete-continuous stochastic kinetic models

    International Nuclear Information System (INIS)

    Sherlock, Chris; Golightly, Andrew; Gillespie, Colin S

    2014-01-01

    We consider the problem of efficiently performing simulation and inference for stochastic kinetic models. Whilst it is possible to work directly with the resulting Markov jump process (MJP), computational cost can be prohibitive for networks of realistic size and complexity. In this paper, we consider an inference scheme based on a novel hybrid simulator that classifies reactions as either ‘fast’ or ‘slow’ with fast reactions evolving as a continuous Markov process whilst the remaining slow reaction occurrences are modelled through a MJP with time-dependent hazards. A linear noise approximation (LNA) of fast reaction dynamics is employed and slow reaction events are captured by exploiting the ability to solve the stochastic differential equation driving the LNA. This simulation procedure is used as a proposal mechanism inside a particle MCMC scheme, thus allowing Bayesian inference for the model parameters. We apply the scheme to a simple application and compare the output with an existing hybrid approach and also a scheme for performing inference for the underlying discrete stochastic model. (paper)

  6. Optimal Stochastic Modeling and Control of Flexible Structures

    Science.gov (United States)

    1988-09-01

    1.37] and McLane [1.18] considered multivariable systems and derived their optimal control characteristics. Kleinman, Gorman and Zaborsky considered...Leondes [1.72,1.73] studied various aspects of multivariable linear stochastic, discrete-time systems that are partly deterministic, and partly stochastic...June 1966. 1.8. A.V. Balaknishnan, Applied Functional Analaysis , 2nd ed., New York, N.Y.: Springer-Verlag, 1981 1.9. Peter S. Maybeck, Stochastic

  7. Stochastic simulation of grain growth during continuous casting

    International Nuclear Information System (INIS)

    Ramirez, A.; Carrillo, F.; Gonzalez, J.L.; Lopez, S.

    2006-01-01

    The evolution of microstructure is a very important topic in material science engineering because the solidification conditions of steel billets during continuous casting process affect directly the properties of the final products. In this paper a mathematical model is described in order to simulate the dendritic growth using data of real casting operations; here a combination of deterministic and stochastic methods was used as a function of the solidification time of every node in order to create a reconstruction about the morphology of cast structures

  8. Stochastic simulation of grain growth during continuous casting

    Energy Technology Data Exchange (ETDEWEB)

    Ramirez, A. [Department of Aerounatical Engineering, S.E.P.I., E.S.I.M.E., IPN, Instituto Politecnico Nacional (Unidad Profesional Ticoman), Av. Ticoman 600, Col. Ticoman, C.P.07340 (Mexico)]. E-mail: adalop123@mailbanamex.com; Carrillo, F. [Department of Processing Materials, CICATA-IPN Unidad Altamira Tamps (Mexico); Gonzalez, J.L. [Department of Metallurgy and Materials Engineering, E.S.I.Q.I.E.-IPN (Mexico); Lopez, S. [Department of Molecular Engineering of I.M.P., AP 14-805 (Mexico)

    2006-04-15

    The evolution of microstructure is a very important topic in material science engineering because the solidification conditions of steel billets during continuous casting process affect directly the properties of the final products. In this paper a mathematical model is described in order to simulate the dendritic growth using data of real casting operations; here a combination of deterministic and stochastic methods was used as a function of the solidification time of every node in order to create a reconstruction about the morphology of cast structures.

  9. Stochastic coalescence in finite systems: an algorithm for the numerical solution of the multivariate master equation.

    Science.gov (United States)

    Alfonso, Lester; Zamora, Jose; Cruz, Pedro

    2015-04-01

    The stochastic approach to coagulation considers the coalescence process going in a system of a finite number of particles enclosed in a finite volume. Within this approach, the full description of the system can be obtained from the solution of the multivariate master equation, which models the evolution of the probability distribution of the state vector for the number of particles of a given mass. Unfortunately, due to its complexity, only limited results were obtained for certain type of kernels and monodisperse initial conditions. In this work, a novel numerical algorithm for the solution of the multivariate master equation for stochastic coalescence that works for any type of kernels and initial conditions is introduced. The performance of the method was checked by comparing the numerically calculated particle mass spectrum with analytical solutions obtained for the constant and sum kernels, with an excellent correspondence between the analytical and numerical solutions. In order to increase the speedup of the algorithm, software parallelization techniques with OpenMP standard were used, along with an implementation in order to take advantage of new accelerator technologies. Simulations results show an important speedup of the parallelized algorithms. This study was funded by a grant from Consejo Nacional de Ciencia y Tecnologia de Mexico SEP-CONACYT CB-131879. The authors also thanks LUFAC® Computacion SA de CV for CPU time and all the support provided.

  10. Continuous-Time Mean-Variance Portfolio Selection with Random Horizon

    International Nuclear Information System (INIS)

    Yu, Zhiyong

    2013-01-01

    This paper examines the continuous-time mean-variance optimal portfolio selection problem with random market parameters and random time horizon. Treating this problem as a linearly constrained stochastic linear-quadratic optimal control problem, I explicitly derive the efficient portfolios and efficient frontier in closed forms based on the solutions of two backward stochastic differential equations. Some related issues such as a minimum variance portfolio and a mutual fund theorem are also addressed. All the results are markedly different from those in the problem with deterministic exit time. A key part of my analysis involves proving the global solvability of a stochastic Riccati equation, which is interesting in its own right

  11. Continuous-Time Mean-Variance Portfolio Selection with Random Horizon

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Zhiyong, E-mail: yuzhiyong@sdu.edu.cn [Shandong University, School of Mathematics (China)

    2013-12-15

    This paper examines the continuous-time mean-variance optimal portfolio selection problem with random market parameters and random time horizon. Treating this problem as a linearly constrained stochastic linear-quadratic optimal control problem, I explicitly derive the efficient portfolios and efficient frontier in closed forms based on the solutions of two backward stochastic differential equations. Some related issues such as a minimum variance portfolio and a mutual fund theorem are also addressed. All the results are markedly different from those in the problem with deterministic exit time. A key part of my analysis involves proving the global solvability of a stochastic Riccati equation, which is interesting in its own right.

  12. Continuity of Local Time: An applied perspective

    OpenAIRE

    Ramirez, Jorge M.; Waymire, Edward C.; Thomann, Enrique A.

    2015-01-01

    Continuity of local time for Brownian motion ranks among the most notable mathematical results in the theory of stochastic processes. This article addresses its implications from the point of view of applications. In particular an extension of previous results on an explicit role of continuity of (natural) local time is obtained for applications to recent classes of problems in physics, biology and finance involving discontinuities in a dispersion coefficient. The main theorem and its corolla...

  13. Expectation propagation for continuous time stochastic processes

    International Nuclear Information System (INIS)

    Cseke, Botond; Schnoerr, David; Sanguinetti, Guido; Opper, Manfred

    2016-01-01

    We consider the inverse problem of reconstructing the posterior measure over the trajectories of a diffusion process from discrete time observations and continuous time constraints. We cast the problem in a Bayesian framework and derive approximations to the posterior distributions of single time marginals using variational approximate inference, giving rise to an expectation propagation type algorithm. For non-linear diffusion processes, this is achieved by leveraging moment closure approximations. We then show how the approximation can be extended to a wide class of discrete-state Markov jump processes by making use of the chemical Langevin equation. Our empirical results show that the proposed method is computationally efficient and provides good approximations for these classes of inverse problems. (paper)

  14. Mean Square Exponential Stability of Stochastic Switched System with Interval Time-Varying Delays

    Directory of Open Access Journals (Sweden)

    Manlika Rajchakit

    2012-01-01

    Full Text Available This paper is concerned with mean square exponential stability of switched stochastic system with interval time-varying delays. The time delay is any continuous function belonging to a given interval, but not necessary to be differentiable. By constructing a suitable augmented Lyapunov-Krasovskii functional combined with Leibniz-Newton’s formula, a switching rule for the mean square exponential stability of switched stochastic system with interval time-varying delays and new delay-dependent sufficient conditions for the mean square exponential stability of the switched stochastic system are first established in terms of LMIs. Numerical example is given to show the effectiveness of the obtained result.

  15. On Stochastic Finite-Time Control of Discrete-Time Fuzzy Systems with Packet Dropout

    Directory of Open Access Journals (Sweden)

    Yingqi Zhang

    2012-01-01

    Full Text Available This paper is concerned with the stochastic finite-time stability and stochastic finite-time boundedness problems for one family of fuzzy discrete-time systems over networks with packet dropout, parametric uncertainties, and time-varying norm-bounded disturbance. Firstly, we present the dynamic model description studied, in which the discrete-time fuzzy T-S systems with packet loss can be described by one class of fuzzy Markovian jump systems. Then, the concepts of stochastic finite-time stability and stochastic finite-time boundedness and problem formulation are given. Based on Lyapunov function approach, sufficient conditions on stochastic finite-time stability and stochastic finite-time boundedness are established for the resulting closed-loop fuzzy discrete-time system with Markovian jumps, and state-feedback controllers are designed to ensure stochastic finite-time stability and stochastic finite-time boundedness of the class of fuzzy systems. The stochastic finite-time stability and stochastic finite-time boundedness criteria can be tackled in the form of linear matrix inequalities with a fixed parameter. As an auxiliary result, we also give sufficient conditions on the stochastic stability of the class of fuzzy T-S systems with packet loss. Finally, two illustrative examples are presented to show the validity of the developed methodology.

  16. Analysis of novel stochastic switched SILI epidemic models with continuous and impulsive control

    Science.gov (United States)

    Gao, Shujing; Zhong, Deming; Zhang, Yan

    2018-04-01

    In this paper, we establish two new stochastic switched epidemic models with continuous and impulsive control. The stochastic perturbations are considered for the natural death rate in each equation of the models. Firstly, a stochastic switched SILI model with continuous control schemes is investigated. By using Lyapunov-Razumikhin method, the sufficient conditions for extinction in mean are established. Our result shows that the disease could be die out theoretically if threshold value R is less than one, regardless of whether the disease-free solutions of the corresponding subsystems are stable or unstable. Then, a stochastic switched SILI model with continuous control schemes and pulse vaccination is studied. The threshold value R is derived. The global attractivity of the model is also obtained. At last, numerical simulations are carried out to support our results.

  17. A Two-Stage Maximum Entropy Prior of Location Parameter with a Stochastic Multivariate Interval Constraint and Its Properties

    Directory of Open Access Journals (Sweden)

    Hea-Jung Kim

    2016-05-01

    Full Text Available This paper proposes a two-stage maximum entropy prior to elicit uncertainty regarding a multivariate interval constraint of the location parameter of a scale mixture of normal model. Using Shannon’s entropy, this study demonstrates how the prior, obtained by using two stages of a prior hierarchy, appropriately accounts for the information regarding the stochastic constraint and suggests an objective measure of the degree of belief in the stochastic constraint. The study also verifies that the proposed prior plays the role of bridging the gap between the canonical maximum entropy prior of the parameter with no interval constraint and that with a certain multivariate interval constraint. It is shown that the two-stage maximum entropy prior belongs to the family of rectangle screened normal distributions that is conjugate for samples from a normal distribution. Some properties of the prior density, useful for developing a Bayesian inference of the parameter with the stochastic constraint, are provided. We also propose a hierarchical constrained scale mixture of normal model (HCSMN, which uses the prior density to estimate the constrained location parameter of a scale mixture of normal model and demonstrates the scope of its applicability.

  18. Asymptotic absolute continuity for perturbed time-dependent ...

    Indian Academy of Sciences (India)

    R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

    We study the notion of asymptotic velocity for a class of perturbed time- ... for Mathematical Physics and Stochastics, funded by a grant from the Danish National Research Foun- .... Using (2.4) we can readily continue α(t) to the whole half-axis.

  19. Stochastic nonlinear time series forecasting using time-delay reservoir computers: performance and universality.

    Science.gov (United States)

    Grigoryeva, Lyudmila; Henriques, Julie; Larger, Laurent; Ortega, Juan-Pablo

    2014-07-01

    Reservoir computing is a recently introduced machine learning paradigm that has already shown excellent performances in the processing of empirical data. We study a particular kind of reservoir computers called time-delay reservoirs that are constructed out of the sampling of the solution of a time-delay differential equation and show their good performance in the forecasting of the conditional covariances associated to multivariate discrete-time nonlinear stochastic processes of VEC-GARCH type as well as in the prediction of factual daily market realized volatilities computed with intraday quotes, using as training input daily log-return series of moderate size. We tackle some problems associated to the lack of task-universality for individually operating reservoirs and propose a solution based on the use of parallel arrays of time-delay reservoirs. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. On the continuous selections of solution sets of Lipschitzian quantum stochastic differential inclusions

    International Nuclear Information System (INIS)

    Ayoola, E.O.

    2004-05-01

    We prove that a multifunction associated with the set of solutions of Lipschitzian quantum stochastic differential inclusion (QSDI) admits a selection continuous from some subsets of complex numbers to the space of the matrix elements of adapted weakly absolutely continuous quantum stochastic processes. In particular, we show that the solution set map as well as the reachable set of the QSDI admit some continuous representations. (author)

  1. Application of Stochastic Automata Networks for Creation of Continuous Time Markov Chain Models of Voltage Gating of Gap Junction Channels

    Directory of Open Access Journals (Sweden)

    Mindaugas Snipas

    2015-01-01

    Full Text Available The primary goal of this work was to study advantages of numerical methods used for the creation of continuous time Markov chain models (CTMC of voltage gating of gap junction (GJ channels composed of connexin protein. This task was accomplished by describing gating of GJs using the formalism of the stochastic automata networks (SANs, which allowed for very efficient building and storing of infinitesimal generator of the CTMC that allowed to produce matrices of the models containing a distinct block structure. All of that allowed us to develop efficient numerical methods for a steady-state solution of CTMC models. This allowed us to accelerate CPU time, which is necessary to solve CTMC models, ∼20 times.

  2. Application of Stochastic Automata Networks for Creation of Continuous Time Markov Chain Models of Voltage Gating of Gap Junction Channels

    Science.gov (United States)

    Pranevicius, Henrikas; Pranevicius, Mindaugas; Pranevicius, Osvaldas; Bukauskas, Feliksas F.

    2015-01-01

    The primary goal of this work was to study advantages of numerical methods used for the creation of continuous time Markov chain models (CTMC) of voltage gating of gap junction (GJ) channels composed of connexin protein. This task was accomplished by describing gating of GJs using the formalism of the stochastic automata networks (SANs), which allowed for very efficient building and storing of infinitesimal generator of the CTMC that allowed to produce matrices of the models containing a distinct block structure. All of that allowed us to develop efficient numerical methods for a steady-state solution of CTMC models. This allowed us to accelerate CPU time, which is necessary to solve CTMC models, ∼20 times. PMID:25705700

  3. Ecological prediction with nonlinear multivariate time-frequency functional data models

    Science.gov (United States)

    Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

    2013-01-01

    Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

  4. Continuous strong Markov processes in dimension one a stochastic calculus approach

    CERN Document Server

    Assing, Sigurd

    1998-01-01

    The book presents an in-depth study of arbitrary one-dimensional continuous strong Markov processes using methods of stochastic calculus. Departing from the classical approaches, a unified investigation of regular as well as arbitrary non-regular diffusions is provided. A general construction method for such processes, based on a generalization of the concept of a perfect additive functional, is developed. The intrinsic decomposition of a continuous strong Markov semimartingale is discovered. The book also investigates relations to stochastic differential equations and fundamental examples of irregular diffusions.

  5. GCSRL - A Logic for Stochastic Reward Models with Timed and Untimed Behaviour

    NARCIS (Netherlands)

    Kuntz, Matthias; Haverkort, Boudewijn R.; Cloth, L.

    In this paper we define the logic GCSRL (generalised continuous stochastic reward logic) that provides means to reason about systems that have states which sojourn times are either greater zero, in which case this sojourn time is exponentially distributed (tangible states), or zero (vanishing

  6. Ranking shortest paths in Stochastic time-denpendent networks

    DEFF Research Database (Denmark)

    Nielsen, Lars Relund; Andersen, Kim Allan; Pretolani, Daniele

    A substantial amount of research has been devoted to the shortest path problem in networks where travel times are stochastic or (deterministic and) time-dependent. More recently, a growing interest has been attracted by networks that are both stochastic and time-dependent. In these networks, the ...... present a computational comparison of time-adaptive and a priori route choices, pointing out the effect of travel time and cost distributions. The reported results show that, under realistic distributions, our solution methods are effective.......A substantial amount of research has been devoted to the shortest path problem in networks where travel times are stochastic or (deterministic and) time-dependent. More recently, a growing interest has been attracted by networks that are both stochastic and time-dependent. In these networks...

  7. K shortest paths in stochastic time-dependent networks

    DEFF Research Database (Denmark)

    Nielsen, Lars Relund; Pretolani, Daniele; Andersen, Kim Allan

    2004-01-01

    A substantial amount of research has been devoted to the shortest path problem in networks where travel times are stochastic or (deterministic and) time-dependent. More recently, a growing interest has been attracted by networks that are both stochastic and time-dependent. In these networks, the ...... present a computational comparison of time-adaptive and a priori route choices, pointing out the effect of travel time and cost distributions. The reported results show that, under realistic distributions, our solution methods are effective.......A substantial amount of research has been devoted to the shortest path problem in networks where travel times are stochastic or (deterministic and) time-dependent. More recently, a growing interest has been attracted by networks that are both stochastic and time-dependent. In these networks...

  8. Analytic continuation of quantum Monte Carlo data. Stochastic sampling method

    Energy Technology Data Exchange (ETDEWEB)

    Ghanem, Khaldoon; Koch, Erik [Institute for Advanced Simulation, Forschungszentrum Juelich, 52425 Juelich (Germany)

    2016-07-01

    We apply Bayesian inference to the analytic continuation of quantum Monte Carlo (QMC) data from the imaginary axis to the real axis. Demanding a proper functional Bayesian formulation of any analytic continuation method leads naturally to the stochastic sampling method (StochS) as the Bayesian method with the simplest prior, while it excludes the maximum entropy method and Tikhonov regularization. We present a new efficient algorithm for performing StochS that reduces computational times by orders of magnitude in comparison to earlier StochS methods. We apply the new algorithm to a wide variety of typical test cases: spectral functions and susceptibilities from DMFT and lattice QMC calculations. Results show that StochS performs well and is able to resolve sharp features in the spectrum.

  9. Factors influencing lysis time stochasticity in bacteriophage λ

    Directory of Open Access Journals (Sweden)

    Dennehy John J

    2011-08-01

    Full Text Available Abstract Background Despite identical genotypes and seemingly uniform environments, stochastic gene expression and other dynamic intracellular processes can produce considerable phenotypic diversity within clonal microbes. One trait that provides a good model to explore the molecular basis of stochastic variation is the timing of host lysis by bacteriophage (phage. Results Individual lysis events of thermally-inducible λ lysogens were observed using a temperature-controlled perfusion chamber mounted on an inverted microscope. Both mean lysis time (MLT and its associated standard deviation (SD were estimated. Using the SD as a measure of lysis time stochasticity, we showed that lysogenic cells in controlled environments varied widely in lysis times, and that the level of lysis time stochasticity depended on allelic variation in the holin sequence, late promoter (pR' activity, and host growth rate. In general, the MLT was positively correlated with the SD. Both lower pR' activities and lower host growth rates resulted in larger SDs. Results from premature lysis, induced by adding KCN at different time points after lysogen induction, showed a negative correlation between the timing of KCN addition and lysis time stochasticity. Conclusions Taken together with results published by others, we conclude that a large fraction of λ lysis time stochasticity is the result of random events following the expression and diffusion of the holin protein. Consequently, factors influencing the timing of reaching critical holin concentrations in the cell membrane, such as holin production rate, strongly influence the mean lysis time and the lysis time stochasticity.

  10. Time evolution of one-dimensional gapless models from a domain wall initial state: stochastic Loewner evolution continued?

    International Nuclear Information System (INIS)

    Calabrese, Pasquale; Hagendorf, Christian; Doussal, Pierre Le

    2008-01-01

    We study the time evolution of quantum one-dimensional gapless systems evolving from initial states with a domain wall. We generalize the path integral imaginary time approach that together with boundary conformal field theory allows us to derive the time and space dependence of general correlation functions. The latter are explicitly obtained for the Ising universality class, and the typical behavior of one- and two-point functions is derived for the general case. Possible connections with the stochastic Loewner evolution are discussed and explicit results for one-point time dependent averages are obtained for generic κ for boundary conditions corresponding to stochastic Loewner evolution. We use this set of results to predict the time evolution of the entanglement entropy and obtain the universal constant shift due to the presence of a domain wall in the initial state

  11. H∞ state estimation of stochastic memristor-based neural networks with time-varying delays.

    Science.gov (United States)

    Bao, Haibo; Cao, Jinde; Kurths, Jürgen; Alsaedi, Ahmed; Ahmad, Bashir

    2018-03-01

    This paper addresses the problem of H ∞ state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the H ∞ state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficient conditions are derived to ensure that the estimation error system is asymptotically stable in the mean square with a prescribed H ∞ performance. An explicit expression of the state estimator gain is given in terms of linear matrix inequalities (LMIs). Compared with other results, our results reduce control gain and control cost effectively. Finally, numerical simulations are provided to demonstrate the efficiency of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

  12. Stochastic modeling of neurobiological time series: Power, coherence, Granger causality, and separation of evoked responses from ongoing activity

    Science.gov (United States)

    Chen, Yonghong; Bressler, Steven L.; Knuth, Kevin H.; Truccolo, Wilson A.; Ding, Mingzhou

    2006-06-01

    In this article we consider the stochastic modeling of neurobiological time series from cognitive experiments. Our starting point is the variable-signal-plus-ongoing-activity model. From this model a differentially variable component analysis strategy is developed from a Bayesian perspective to estimate event-related signals on a single trial basis. After subtracting out the event-related signal from recorded single trial time series, the residual ongoing activity is treated as a piecewise stationary stochastic process and analyzed by an adaptive multivariate autoregressive modeling strategy which yields power, coherence, and Granger causality spectra. Results from applying these methods to local field potential recordings from monkeys performing cognitive tasks are presented.

  13. Optimal model-free prediction from multivariate time series

    Science.gov (United States)

    Runge, Jakob; Donner, Reik V.; Kurths, Jürgen

    2015-05-01

    Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal preselection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used suboptimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Niño Southern Oscillation.

  14. Investment timing decisions in a stochastic duopoly model

    Energy Technology Data Exchange (ETDEWEB)

    Marseguerra, Giovanni [Istituto di Econometria e CRANEC, Universita Cattolica del Sacro Cuore di Milan (Italy)]. E-mail: giovanni.marseguerra@unicatt.it; Cortelezzi, Flavia [Dipartimento di Diritto ed Economia delle Persone e delle Imprese, Universita dell' Insubria (Italy)]. E-mail: flavia.cortelezzi@uninsubria.it; Dominioni, Armando [CORE-Catholique de Louvain la Neuve (Belgium)]. E-mail: dominioni@core.ucl.ac.be

    2006-08-15

    We investigate the role of strategic considerations on the optimal timing of investment when firms compete for a new market (e.g., the provision of an innovative product) under demand uncertainty. Within a continuous time model of stochastic oligopoly, we show that strategic considerations are likely to be of limited impact when the new product is radically innovative whilst the fear of a rival's entry may deeply affect firms' decisions whenever innovation is to some extent limited. The welfare analysis shows surprisingly that the desirability of the different market structures considered does not depend on the fixed entry cost.

  15. Investment timing decisions in a stochastic duopoly model

    International Nuclear Information System (INIS)

    Marseguerra, Giovanni; Cortelezzi, Flavia; Dominioni, Armando

    2006-01-01

    We investigate the role of strategic considerations on the optimal timing of investment when firms compete for a new market (e.g., the provision of an innovative product) under demand uncertainty. Within a continuous time model of stochastic oligopoly, we show that strategic considerations are likely to be of limited impact when the new product is radically innovative whilst the fear of a rival's entry may deeply affect firms' decisions whenever innovation is to some extent limited. The welfare analysis shows surprisingly that the desirability of the different market structures considered does not depend on the fixed entry cost

  16. Multivariate supOU processes

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole Eiler; Stelzer, Robert

    Univariate superpositions of Ornstein-Uhlenbeck (OU) type processes, called supOU processes, provide a class of continuous time processes capable of exhibiting long memory behaviour. This paper introduces multivariate supOU processes and gives conditions for their existence and finiteness...... of moments. Moreover, the second order moment structure is explicitly calculated, and examples exhibit the possibility of long range dependence. Our supOU processes are defined via homogeneous and factorisable Lévy bases. We show that the behaviour of supOU processes is particularly nice when the mean...... reversion parameter is restricted to normal matrices and especially to strictly negative definite ones.For finite variation Lévy bases we are able to give conditions for supOU processes to have locally bounded càdlàg paths of finite variation and to show an analogue of the stochastic differential equation...

  17. Multivariate supOU processes

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole Eiler; Stelzer, Robert

    2011-01-01

    Univariate superpositions of Ornstein–Uhlenbeck-type processes (OU), called supOU processes, provide a class of continuous time processes capable of exhibiting long memory behavior. This paper introduces multivariate supOU processes and gives conditions for their existence and finiteness of moments....... Moreover, the second-order moment structure is explicitly calculated, and examples exhibit the possibility of long-range dependence. Our supOU processes are defined via homogeneous and factorizable Lévy bases. We show that the behavior of supOU processes is particularly nice when the mean reversion...... parameter is restricted to normal matrices and especially to strictly negative definite ones. For finite variation Lévy bases we are able to give conditions for supOU processes to have locally bounded càdlàg paths of finite variation and to show an analogue of the stochastic differential equation of OU...

  18. Stochastic ℋ∞ Finite-Time Control of Discrete-Time Systems with Packet Loss

    Directory of Open Access Journals (Sweden)

    Yingqi Zhang

    2012-01-01

    Full Text Available This paper investigates the stochastic finite-time stabilization and ℋ∞ control problem for one family of linear discrete-time systems over networks with packet loss, parametric uncertainties, and time-varying norm-bounded disturbance. Firstly, the dynamic model description studied is given, which, if the packet dropout is assumed to be a discrete-time homogenous Markov process, the class of discrete-time linear systems with packet loss can be regarded as Markovian jump systems. Based on Lyapunov function approach, sufficient conditions are established for the resulting closed-loop discrete-time system with Markovian jumps to be stochastic ℋ∞ finite-time boundedness and then state feedback controllers are designed to guarantee stochastic ℋ∞ finite-time stabilization of the class of stochastic systems. The stochastic ℋ∞ finite-time boundedness criteria can be tackled in the form of linear matrix inequalities with a fixed parameter. As an auxiliary result, we also give sufficient conditions on the robust stochastic stabilization of the class of linear systems with packet loss. Finally, simulation examples are presented to illustrate the validity of the developed scheme.

  19. Quantum dynamical time evolutions as stochastic flows on phase space

    International Nuclear Information System (INIS)

    Combe, P.; Rodriguez, R.; Guerra, F.; Sirigue, M.; Sirigue-Collin, M.

    1984-01-01

    We are mainly interested in describing the time development of the Wigner functions by means of stochastic processes. In the second section we recall the main properties of the Wigner functions as well as those of their Fourier transform. In the next one we derive the evolution equation of these functions for a class of Hamiltonians and we give a probabilistic expression for the solution of these equations by means of a stochastic flow in phase space which reminds of the classical flows. In the last section we remark that the previously defined flow can be extended to the bounded continuous functions on phase space and that this flow conserves the cone generated by the Wigner functions. (orig./HSI)

  20. Incomplete Continuous-time Securities Markets with Stochastic Income Volatility

    DEFF Research Database (Denmark)

    Christensen, Peter Ove; Larsen, Kasper

    2014-01-01

    We derive closed-form solutions for the equilibrium interest rate and market price of risk processes in an incomplete continuous-time market with uncertainty generated by Brownian motions. The economy has a finite number of heterogeneous exponential utility investors, who receive partially...

  1. Incomplete Continuous-Time Securities Markets with Stochastic Income Volatility

    DEFF Research Database (Denmark)

    Christensen, Peter Ove; Larsen, Kasper

    In an incomplete continuous-time securities market governed by Brownian motions, we derive closed-form solutions for the equilibrium risk-free rate and equity premium processes. The economy has a finite number of heterogeneous exponential utility investors, who receive partially unspanned income ...

  2. Brownian motion and stochastic calculus

    CERN Document Server

    Karatzas, Ioannis

    1998-01-01

    This book is designed as a text for graduate courses in stochastic processes. It is written for readers familiar with measure-theoretic probability and discrete-time processes who wish to explore stochastic processes in continuous time. The vehicle chosen for this exposition is Brownian motion, which is presented as the canonical example of both a martingale and a Markov process with continuous paths. In this context, the theory of stochastic integration and stochastic calculus is developed. The power of this calculus is illustrated by results concerning representations of martingales and change of measure on Wiener space, and these in turn permit a presentation of recent advances in financial economics (option pricing and consumption/investment optimization). This book contains a detailed discussion of weak and strong solutions of stochastic differential equations and a study of local time for semimartingales, with special emphasis on the theory of Brownian local time. The text is complemented by a large num...

  3. Calculating Higher-Order Moments of Phylogenetic Stochastic Mapping Summaries in Linear Time

    Science.gov (United States)

    Dhar, Amrit

    2017-01-01

    Abstract Stochastic mapping is a simulation-based method for probabilistically mapping substitution histories onto phylogenies according to continuous-time Markov models of evolution. This technique can be used to infer properties of the evolutionary process on the phylogeny and, unlike parsimony-based mapping, conditions on the observed data to randomly draw substitution mappings that do not necessarily require the minimum number of events on a tree. Most stochastic mapping applications simulate substitution mappings only to estimate the mean and/or variance of two commonly used mapping summaries: the number of particular types of substitutions (labeled substitution counts) and the time spent in a particular group of states (labeled dwelling times) on the tree. Fast, simulation-free algorithms for calculating the mean of stochastic mapping summaries exist. Importantly, these algorithms scale linearly in the number of tips/leaves of the phylogenetic tree. However, to our knowledge, no such algorithm exists for calculating higher-order moments of stochastic mapping summaries. We present one such simulation-free dynamic programming algorithm that calculates prior and posterior mapping variances and scales linearly in the number of phylogeny tips. Our procedure suggests a general framework that can be used to efficiently compute higher-order moments of stochastic mapping summaries without simulations. We demonstrate the usefulness of our algorithm by extending previously developed statistical tests for rate variation across sites and for detecting evolutionarily conserved regions in genomic sequences. PMID:28177780

  4. Importance Sampling for Stochastic Timed Automata

    DEFF Research Database (Denmark)

    Jegourel, Cyrille; Larsen, Kim Guldstrand; Legay, Axel

    2016-01-01

    We present an importance sampling framework that combines symbolic analysis and simulation to estimate the probability of rare reachability properties in stochastic timed automata. By means of symbolic exploration, our framework first identifies states that cannot reach the goal. A state-wise cha......We present an importance sampling framework that combines symbolic analysis and simulation to estimate the probability of rare reachability properties in stochastic timed automata. By means of symbolic exploration, our framework first identifies states that cannot reach the goal. A state...

  5. On Continuous Selection Sets of Non-Lipschitzian Quantum Stochastic Evolution Inclusions

    Directory of Open Access Journals (Sweden)

    Sheila Bishop

    2015-01-01

    Full Text Available We establish existence of a continuous selection of multifunctions associated with quantum stochastic evolution inclusions under a general Lipschitz condition. The coefficients here are multifunctions but not necessarily Lipschitz.

  6. Modeling real-time balancing power demands in wind power systems using stochastic differential equations

    International Nuclear Information System (INIS)

    Olsson, Magnus; Perninge, Magnus; Soeder, Lennart

    2010-01-01

    The inclusion of wind power into power systems has a significant impact on the demand for real-time balancing power due to the stochastic nature of wind power production. The overall aim of this paper is to present probabilistic models of the impact of large-scale integration of wind power on the continuous demand in MW for real-time balancing power. This is important not only for system operators, but also for producers and consumers since they in most systems through various market solutions provide balancing power. Since there can occur situations where the wind power variations cancel out other types of deviations in the system, models on an hourly basis are not sufficient. Therefore the developed model is in continuous time and is based on stochastic differential equations (SDE). The model can be used within an analytical framework or in Monte Carlo simulations. (author)

  7. Time-ordered product expansions for computational stochastic system biology

    International Nuclear Information System (INIS)

    Mjolsness, Eric

    2013-01-01

    The time-ordered product framework of quantum field theory can also be used to understand salient phenomena in stochastic biochemical networks. It is used here to derive Gillespie’s stochastic simulation algorithm (SSA) for chemical reaction networks; consequently, the SSA can be interpreted in terms of Feynman diagrams. It is also used here to derive other, more general simulation and parameter-learning algorithms including simulation algorithms for networks of stochastic reaction-like processes operating on parameterized objects, and also hybrid stochastic reaction/differential equation models in which systems of ordinary differential equations evolve the parameters of objects that can also undergo stochastic reactions. Thus, the time-ordered product expansion can be used systematically to derive simulation and parameter-fitting algorithms for stochastic systems. (paper)

  8. The Application of backward stochastic differential equation with stopping time in hedging American contingent claims

    International Nuclear Information System (INIS)

    Wang Bo; Song Ruili

    2009-01-01

    We consider a more general wealth process with a drift coefficient which is Lipschitz continuous and the portfolio process with convex constraint. We convert the problem of hedging American contingent claims into the problem of minimal solution of backward stochastic differential equation with stopping time. We adopt the penalization method for constructing the minimal solution of stochastic differential equations and obtain the upper hedging price of American contingent claims.

  9. Stochastic differential equation model to Prendiville processes

    International Nuclear Information System (INIS)

    Granita; Bahar, Arifah

    2015-01-01

    The Prendiville process is another variation of the logistic model which assumes linearly decreasing population growth rate. It is a continuous time Markov chain (CTMC) taking integer values in the finite interval. The continuous time Markov chain can be approximated by stochastic differential equation (SDE). This paper discusses the stochastic differential equation of Prendiville process. The work started with the forward Kolmogorov equation in continuous time Markov chain of Prendiville process. Then it was formulated in the form of a central-difference approximation. The approximation was then used in Fokker-Planck equation in relation to the stochastic differential equation of the Prendiville process. The explicit solution of the Prendiville process was obtained from the stochastic differential equation. Therefore, the mean and variance function of the Prendiville process could be easily found from the explicit solution

  10. Stochastic differential equation model to Prendiville processes

    Energy Technology Data Exchange (ETDEWEB)

    Granita, E-mail: granitafc@gmail.com [Dept. of Mathematical Science, Universiti Teknologi Malaysia, 81310, Johor Malaysia (Malaysia); Bahar, Arifah [Dept. of Mathematical Science, Universiti Teknologi Malaysia, 81310, Johor Malaysia (Malaysia); UTM Center for Industrial & Applied Mathematics (UTM-CIAM) (Malaysia)

    2015-10-22

    The Prendiville process is another variation of the logistic model which assumes linearly decreasing population growth rate. It is a continuous time Markov chain (CTMC) taking integer values in the finite interval. The continuous time Markov chain can be approximated by stochastic differential equation (SDE). This paper discusses the stochastic differential equation of Prendiville process. The work started with the forward Kolmogorov equation in continuous time Markov chain of Prendiville process. Then it was formulated in the form of a central-difference approximation. The approximation was then used in Fokker-Planck equation in relation to the stochastic differential equation of the Prendiville process. The explicit solution of the Prendiville process was obtained from the stochastic differential equation. Therefore, the mean and variance function of the Prendiville process could be easily found from the explicit solution.

  11. Multivariate stochastic simulation with subjective multivariate normal distributions

    Science.gov (United States)

    P. J. Ince; J. Buongiorno

    1991-01-01

    In many applications of Monte Carlo simulation in forestry or forest products, it may be known that some variables are correlated. However, for simplicity, in most simulations it has been assumed that random variables are independently distributed. This report describes an alternative Monte Carlo simulation technique for subjectively assesed multivariate normal...

  12. The use of copulas to practical estimation of multivariate stochastic differential equation mixed effects models

    International Nuclear Information System (INIS)

    Rupšys, P.

    2015-01-01

    A system of stochastic differential equations (SDE) with mixed-effects parameters and multivariate normal copula density function were used to develop tree height model for Scots pine trees in Lithuania. A two-step maximum likelihood parameter estimation method is used and computational guidelines are given. After fitting the conditional probability density functions to outside bark diameter at breast height, and total tree height, a bivariate normal copula distribution model was constructed. Predictions from the mixed-effects parameters SDE tree height model calculated during this research were compared to the regression tree height equations. The results are implemented in the symbolic computational language MAPLE

  13. The use of copulas to practical estimation of multivariate stochastic differential equation mixed effects models

    Energy Technology Data Exchange (ETDEWEB)

    Rupšys, P. [Aleksandras Stulginskis University, Studenų g. 11, Akademija, Kaunas district, LT – 53361 Lithuania (Lithuania)

    2015-10-28

    A system of stochastic differential equations (SDE) with mixed-effects parameters and multivariate normal copula density function were used to develop tree height model for Scots pine trees in Lithuania. A two-step maximum likelihood parameter estimation method is used and computational guidelines are given. After fitting the conditional probability density functions to outside bark diameter at breast height, and total tree height, a bivariate normal copula distribution model was constructed. Predictions from the mixed-effects parameters SDE tree height model calculated during this research were compared to the regression tree height equations. The results are implemented in the symbolic computational language MAPLE.

  14. Optimal non-periodic inspection for a multivariate degradation model

    NARCIS (Netherlands)

    Barker, C.T.; Newby, M.J.

    2009-01-01

    We address the problem of determining inspection and maintenance strategy for a system whose state is described by a multivariate stochastic process. We relax and extend the usual approaches. The system state is a multivariate stochastic process, decisions are based on a performance measure defined

  15. Multivariate Time Series Search

    Data.gov (United States)

    National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...

  16. Numerical solution of continuous-time DSGE models under Poisson uncertainty

    DEFF Research Database (Denmark)

    Posch, Olaf; Trimborn, Timo

    We propose a simple and powerful method for determining the transition process in continuous-time DSGE models under Poisson uncertainty numerically. The idea is to transform the system of stochastic differential equations into a system of functional differential equations of the retarded type. We...... classes of models. We illustrate the algorithm simulating both the stochastic neoclassical growth model and the Lucas model under Poisson uncertainty which is motivated by the Barro-Rietz rare disaster hypothesis. We find that, even for non-linear policy functions, the maximum (absolute) error is very...

  17. Long-time correlations in the stochastic regime

    International Nuclear Information System (INIS)

    Karney, C.F.F.

    1982-11-01

    The phase space for Hamiltonians of two degrees of freedom is usually divided into stochastic and integrable components. Even when well into the stochastic regime, integrable orbits may surround small stable regions or islands. The effect of these islands on the correlation function for the stochastic trajectories is examined. Depending on the value of the parameter describing the rotation number for the elliptic fixed point at the center of the island, the long-time correlation function may decay as t -5 or exponentially, but more commonly it decays much more slowly (roughly as t -1 ). As a consequence these small islands may have a profound effect on the properties such as the diffusion coefficient, of the stochastic orbits

  18. A compositional Translation of Stochastic Automata into Timed Automata

    NARCIS (Netherlands)

    d' Argenio, P.R.

    We present a translation from stochastic automata [17, 16] into timed automata with deadlines [37, 13]. The translation preserves traces when the stochastic characteristics, namely the probability measures, are abstracted from the original stochastic automaton. Moreover, we show that the translation

  19. Clustering Multivariate Time Series Using Hidden Markov Models

    Directory of Open Access Journals (Sweden)

    Shima Ghassempour

    2014-03-01

    Full Text Available In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose an approach based on Hidden Markov Models (HMMs, where we first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix. We test our approach on a simulated, but realistic, data set of 1,255 trajectories of individuals of age 45 and over, on a synthetic validation set with known clustering structure, and on a smaller set of 268 trajectories extracted from the longitudinal Health and Retirement Survey. The proposed method can be implemented quite simply using standard packages in R and Matlab and may be a good candidate for solving the difficult problem of clustering multivariate time series with categorical variables using tools that do not require advanced statistic knowledge, and therefore are accessible to a wide range of researchers.

  20. Stochastic space-time and quantum theory

    International Nuclear Information System (INIS)

    Frederick, C.

    1976-01-01

    Much of quantum mechanics may be derived if one adopts a very strong form of Mach's principle such that in the absence of mass, space-time becomes not flat, but stochastic. This is manifested in the metric tensor which is considered to be a collection of stochastic variables. The stochastic-metric assumption is sufficient to generate the spread of the wave packet in empty space. If one further notes that all observations of dynamical variables in the laboratory frame are contravariant components of tensors, and if one assumes that a Lagrangian can be constructed, then one can obtain an explanation of conjugate variables and also a derivation of the uncertainty principle. Finally the superposition of stochastic metrics and the identification of root -g in the four-dimensional invariant volume element root -g dV as the indicator of relative probability yields the phenomenon of interference as will be described for the two-slit experiment

  1. Global behavior analysis for stochastic system of 1,3-PD continuous fermentation

    Science.gov (United States)

    Zhu, Xi; Kliemann, Wolfgang; Li, Chunfa; Feng, Enmin; Xiu, Zhilong

    2017-12-01

    Global behavior for stochastic system of continuous fermentation in glycerol bio-dissimilation to 1,3-propanediol by Klebsiella pneumoniae is analyzed in this paper. This bioprocess cannot avoid the stochastic perturbation caused by internal and external disturbance which reflect on the growth rate. These negative factors can limit and degrade the achievable performance of controlled systems. Based on multiplicity phenomena, the equilibriums and bifurcations of the deterministic system are analyzed. Then, a stochastic model is presented by a bounded Markov diffusion process. In order to analyze the global behavior, we compute the control sets for the associated control system. The probability distributions of relative supports are also computed. The simulation results indicate that how the disturbed biosystem tend to stationary behavior globally.

  2. Prediction of UT1-UTC, LOD and AAM χ3 by combination of least-squares and multivariate stochastic methods

    Science.gov (United States)

    Niedzielski, Tomasz; Kosek, Wiesław

    2008-02-01

    This article presents the application of a multivariate prediction technique for predicting universal time (UT1-UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ 3). The multivariate predictions of LOD and UT1-UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ 3 time-series as the explanatory variable for the computation of LOD or UT1-UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ 3 data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ 3 predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1-UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1-UTC based on LS extrapolation or on LS + AR. In particular, the UT1-UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ 3 time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ 3 reveal the best accuracy for LS + AR.

  3. An introduction to stochastic processes with applications to biology

    CERN Document Server

    Allen, Linda J S

    2010-01-01

    An Introduction to Stochastic Processes with Applications to Biology, Second Edition presents the basic theory of stochastic processes necessary in understanding and applying stochastic methods to biological problems in areas such as population growth and extinction, drug kinetics, two-species competition and predation, the spread of epidemics, and the genetics of inbreeding. Because of their rich structure, the text focuses on discrete and continuous time Markov chains and continuous time and state Markov processes.New to the Second EditionA new chapter on stochastic differential equations th

  4. Quantum trajectories and measurements in continuous time. The diffusive case

    International Nuclear Information System (INIS)

    Barchielli, Alberto; Gregoratti, Matteo

    2009-01-01

    This course-based monograph introduces the reader to the theory of continuous measurements in quantum mechanics and provides some benchmark applications. The approach chosen, quantum trajectory theory, is based on the stochastic Schroedinger and master equations, which determine the evolution of the a-posteriori state of a continuously observed quantum system and give the distribution of the measurement output. The present introduction is restricted to finite-dimensional quantum systems and diffusive outputs. Two appendices introduce the tools of probability theory and quantum measurement theory which are needed for the theoretical developments in the first part of the book. First, the basic equations of quantum trajectory theory are introduced, with all their mathematical properties, starting from the existence and uniqueness of their solutions. This makes the text also suitable for other applications of the same stochastic differential equations in different fields such as simulations of master equations or dynamical reduction theories. In the next step the equivalence between the stochastic approach and the theory of continuous measurements is demonstrated. To conclude the theoretical exposition, the properties of the output of the continuous measurement are analyzed in detail. This is a stochastic process with its own distribution, and the reader will learn how to compute physical quantities such as its moments and its spectrum. In particular this last concept is introduced with clear and explicit reference to the measurement process. The two-level atom is used as the basic prototype to illustrate the theory in a concrete application. Quantum phenomena appearing in the spectrum of the fluorescence light, such as Mollow's triplet structure, squeezing of the fluorescence light, and the linewidth narrowing, are presented. Last but not least, the theory of quantum continuous measurements is the natural starting point to develop a feedback control theory in

  5. Multivariate Time Series Decomposition into Oscillation Components.

    Science.gov (United States)

    Matsuda, Takeru; Komaki, Fumiyasu

    2017-08-01

    Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

  6. Critical spare parts ordering decisions using conditional reliability and stochastic lead time

    International Nuclear Information System (INIS)

    Godoy, David R.; Pascual, Rodrigo; Knights, Peter

    2013-01-01

    Asset-intensive companies face great pressure to reduce operation costs and increase utilization. This scenario often leads to over-stress on critical equipment and its spare parts associated, affecting availability, reliability, and system performance. As these resources impact considerably on financial and operational structures, the opportunity is given by demand for decision-making methods for the management of spare parts processes. We proposed an ordering decision-aid technique which uses a measurement of spare performance, based on the stress–strength interference theory; which we have called Condition-Based Service Level (CBSL). We focus on Condition Managed Critical Spares (CMS), namely, spares which are expensive, highly reliable, with higher lead times, and are not available in store. As a mitigation measure, CMS are under condition monitoring. The aim of the paper is orienting the decision time for CMS ordering or just continuing the operation. The paper presents a graphic technique which considers a rule for decision based on both condition-based reliability function and a stochastic/fixed lead time. For the stochastic lead time case, results show that technique is effective to determine the time when the system operation is reliable and can withstand the lead time variability, satisfying a desired service level. Additionally, for the constant lead time case, the technique helps to define insurance spares. In conclusion, presented ordering decision rule is useful to asset managers for enhancing the operational continuity affected by spare parts

  7. Risk-sensitive control of stochastic hybrid systems on infinite time horizon

    Directory of Open Access Journals (Sweden)

    Runolfsson Thordur

    1999-01-01

    Full Text Available A risk-sensitive optimal control problem is considered for a hybrid system that consists of continuous time diffusion process that depends on a discrete valued mode variable that is modeled as a Markov chain. Optimality conditions are presented and conditions for the existence of optimal controls are derived. It is shown that the optimal risk-sensitive control problem is equivalent to the upper value of an associated stochastic differential game, and insight into the contributions of the noise input and mode variable to the risk sensitivity of the cost functional is given. Furthermore, it is shown that due to the mode variable risk sensitivity, the equivalence relationship that has been observed between risk-sensitive and H ∞ control in the nonhybrid case does not hold for stochastic hybrid systems.

  8. Mean-variance Optimal Reinsurance-investment Strategy in Continuous Time

    OpenAIRE

    Daheng Peng; Fang Zhang

    2017-01-01

    In this paper, Lagrange method is used to solve the continuous-time mean-variance reinsurance-investment problem. Proportional reinsurance, multiple risky assets and risk-free asset are considered synthetically in the optimal strategy for insurers. By solving the backward stochastic differential equation for the Lagrange multiplier, we get the mean-variance optimal reinsurance-investment strategy and its effective frontier in explicit forms.

  9. Index Option Pricing Models with Stochastic Volatility and Stochastic Interest Rates

    NARCIS (Netherlands)

    Jiang, G.J.; van der Sluis, P.J.

    2000-01-01

    This paper specifies a multivariate stochastic volatility (SV) model for the S&P500 index and spot interest rate processes. We first estimate the multivariate SV model via the efficient method of moments (EMM) technique based on observations of underlying state variables, and then investigate the

  10. Bayesian inference for multivariate point processes observed at sparsely distributed times

    DEFF Research Database (Denmark)

    Rasmussen, Jakob Gulddahl; Møller, Jesper; Aukema, B.H.

    We consider statistical and computational aspects of simulation-based Bayesian inference for a multivariate point process which is only observed at sparsely distributed times. For specicity we consider a particular data set which has earlier been analyzed by a discrete time model involving unknown...... normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared to discrete time processes in the setting of the present paper as well as other spatial-temporal situations. Keywords: Bark beetle, conditional intensity, forest entomology, Markov chain Monte Carlo...

  11. Multiparameter Stochastic Dynamics of Ecological Tourism System with Continuous Visitor Education Interventions

    Directory of Open Access Journals (Sweden)

    Dongping Wei

    2015-01-01

    Full Text Available Management of ecological tourism in protected areas faces many challenges, with visitation-related resource degradations and cultural impacts being two of them. To address those issues, several strategies including regulations, site managements, and visitor education programs have been commonly used in China and other countries. This paper presents a multiparameter stochastic differential equation model of an Ecological Tourism System to study how the populations of stakeholders vary in a finite time. The solution of Ordinary Differential Equation of Ecological Tourism System reveals that the system collapses when there is a lack of visitor educational intervention. Hence, the Stochastic Dynamic of Ecological Tourism System is introduced to suppress the explosion of the system. But the simulation results of the Stochastic Dynamic of Ecological Tourism System show that the system is still unstable and chaos in some small time interval. The Multiparameters Stochastic Dynamics of Ecological Tourism System is proposed to improve the performance in this paper. The Multiparameters Stochastic Dynamics of Ecological Tourism System not only suppresses the explosion of the system in a finite time, but also keeps the populations of stakeholders in an acceptable level. In conclusion, the Ecological Tourism System develops steadily and sustainably when land managers employ effective visitor education intervention programs to deal with recreation impacts.

  12. Multivariate performance reliability prediction in real-time

    International Nuclear Information System (INIS)

    Lu, S.; Lu, H.; Kolarik, W.J.

    2001-01-01

    This paper presents a technique for predicting system performance reliability in real-time considering multiple failure modes. The technique includes on-line multivariate monitoring and forecasting of selected performance measures and conditional performance reliability estimates. The performance measures across time are treated as a multivariate time series. A state-space approach is used to model the multivariate time series. Recursive forecasting is performed by adopting Kalman filtering. The predicted mean vectors and covariance matrix of performance measures are used for the assessment of system survival/reliability with respect to the conditional performance reliability. The technique and modeling protocol discussed in this paper provide a means to forecast and evaluate the performance of an individual system in a dynamic environment in real-time. The paper also presents an example to demonstrate the technique

  13. Price discovery in a continuous-time setting

    DEFF Research Database (Denmark)

    Dias, Gustavo Fruet; Fernandes, Marcelo; Scherrer, Cristina

    We formulate a continuous-time price discovery model in which the price discovery measure varies (stochastically) at daily frequency. We estimate daily measures of price discovery using a kernel-based OLS estimator instead of running separate daily VECM regressions as standard in the literature. We...... show that our estimator is not only consistent, but also outperforms the standard daily VECM in finite samples. We illustrate our theoretical findings by studying the price discovery process of 10 actively traded stocks in the U.S. from 2007 to 2013....

  14. Vehicle routing with stochastic time-dependent travel times

    NARCIS (Netherlands)

    Lecluyse, C.; Woensel, van T.; Peremans, H.

    2009-01-01

    Assigning and scheduling vehicle routes in a stochastic time-dependent environment is a crucial management problem. The assumption that in a real-life environment everything goes according to an a priori determined static schedule is unrealistic. Our methodology builds on earlier work in which the

  15. Vehicle routing with stochastic time-dependent travel times

    NARCIS (Netherlands)

    Lecluyse, C.; Woensel, van T.; Peremans, H.

    2007-01-01

    Assigning and scheduling vehicle routes in a stochastic time-dependent environment is a crucial management problem. The assumption that in a real-life environment everything goes according to an a priori determined static schedule is unrealistic. Our methodology builds on earlier work in which the

  16. Mean-variance Optimal Reinsurance-investment Strategy in Continuous Time

    Directory of Open Access Journals (Sweden)

    Daheng Peng

    2017-10-01

    Full Text Available In this paper, Lagrange method is used to solve the continuous-time mean-variance reinsurance-investment problem. Proportional reinsurance, multiple risky assets and risk-free asset are considered synthetically in the optimal strategy for insurers. By solving the backward stochastic differential equation for the Lagrange multiplier, we get the mean-variance optimal reinsurance-investment strategy and its effective frontier in explicit forms.

  17. Continuous Time Structural Equation Modeling with R Package ctsem

    Directory of Open Access Journals (Sweden)

    Charles C. Driver

    2017-04-01

    Full Text Available We introduce ctsem, an R package for continuous time structural equation modeling of panel (N > 1 and time series (N = 1 data, using full information maximum likelihood. Most dynamic models (e.g., cross-lagged panel models in the social and behavioural sciences are discrete time models. An assumption of discrete time models is that time intervals between measurements are equal, and that all subjects were assessed at the same intervals. Violations of this assumption are often ignored due to the difficulty of accounting for varying time intervals, therefore parameter estimates can be biased and the time course of effects becomes ambiguous. By using stochastic differential equations to estimate an underlying continuous process, continuous time models allow for any pattern of measurement occasions. By interfacing to OpenMx, ctsem combines the flexible specification of structural equation models with the enhanced data gathering opportunities and improved estimation of continuous time models. ctsem can estimate relationships over time for multiple latent processes, measured by multiple noisy indicators with varying time intervals between observations. Within and between effects are estimated simultaneously by modeling both observed covariates and unobserved heterogeneity. Exogenous shocks with different shapes, group differences, higher order diffusion effects and oscillating processes can all be simply modeled. We first introduce and define continuous time models, then show how to specify and estimate a range of continuous time models using ctsem.

  18. Stochastic time series analysis of hydrology data for water resources

    Science.gov (United States)

    Sathish, S.; Khadar Babu, S. K.

    2017-11-01

    The prediction to current publication of stochastic time series analysis in hydrology and seasonal stage. The different statistical tests for predicting the hydrology time series on Thomas-Fiering model. The hydrology time series of flood flow have accept a great deal of consideration worldwide. The concentration of stochastic process areas of time series analysis method are expanding with develop concerns about seasonal periods and global warming. The recent trend by the researchers for testing seasonal periods in the hydrologic flowseries using stochastic process on Thomas-Fiering model. The present article proposed to predict the seasonal periods in hydrology using Thomas-Fiering model.

  19. Stochastic time scale for the Universe

    International Nuclear Information System (INIS)

    Szydlowski, M.; Golda, Z.

    1986-01-01

    An intrinsic time scale is naturally defined within stochastic gradient dynamical systems. It should be interpreted as a ''relaxation time'' to a local potential minimum after the system has been randomly perturbed. It is shown that for a flat Friedman-like cosmological model this time scale is of order of the age of the Universe. 7 refs. (author)

  20. TIME-DEPENDENT STOCHASTIC ACCELERATION MODEL FOR FERMI BUBBLES

    Energy Technology Data Exchange (ETDEWEB)

    Sasaki, Kento; Asano, Katsuaki; Terasawa, Toshio, E-mail: kentos@icrr.u-tokyo.ac.jp, E-mail: asanok@icrr.u-tokyo.ac.jp, E-mail: terasawa@icrr.u-tokyo.ac.jp [Institute for Cosmic Ray Research, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8582 (Japan)

    2015-12-01

    We study stochastic acceleration models for the Fermi bubbles. Turbulence is excited just behind the shock front via Kelvin–Helmholtz, Rayleigh–Taylor, or Richtmyer–Meshkov instabilities, and plasma particles are continuously accelerated by the interaction with the turbulence. The turbulence gradually decays as it goes away from the shock fronts. Adopting a phenomenological model for the stochastic acceleration, we explicitly solve the temporal evolution of the particle energy distribution in the turbulence. Our results show that the spatial distribution of high-energy particles is different from those for a steady solution. We also show that the contribution of electrons that escaped from the acceleration regions significantly softens the photon spectrum. The photon spectrum and surface brightness profile are reproduced by our models. If the escape efficiency is very high, the radio flux from the escaped low-energy electrons can be comparable to that of the WMAP haze. We also demonstrate hadronic models with the stochastic acceleration, but they are unlikely in the viewpoint of the energy budget.

  1. Quasi-continuous stochastic simulation framework for flood modelling

    Science.gov (United States)

    Moustakis, Yiannis; Kossieris, Panagiotis; Tsoukalas, Ioannis; Efstratiadis, Andreas

    2017-04-01

    Typically, flood modelling in the context of everyday engineering practices is addressed through event-based deterministic tools, e.g., the well-known SCS-CN method. A major shortcoming of such approaches is the ignorance of uncertainty, which is associated with the variability of soil moisture conditions and the variability of rainfall during the storm event.In event-based modeling, the sole expression of uncertainty is the return period of the design storm, which is assumed to represent the acceptable risk of all output quantities (flood volume, peak discharge, etc.). On the other hand, the varying antecedent soil moisture conditions across the basin are represented by means of scenarios (e.g., the three AMC types by SCS),while the temporal distribution of rainfall is represented through standard deterministic patterns (e.g., the alternative blocks method). In order to address these major inconsistencies,simultaneously preserving the simplicity and parsimony of the SCS-CN method, we have developed a quasi-continuous stochastic simulation approach, comprising the following steps: (1) generation of synthetic daily rainfall time series; (2) update of potential maximum soil moisture retention, on the basis of accumulated five-day rainfall; (3) estimation of daily runoff through the SCS-CN formula, using as inputs the daily rainfall and the updated value of soil moisture retention;(4) selection of extreme events and application of the standard SCS-CN procedure for each specific event, on the basis of synthetic rainfall.This scheme requires the use of two stochastic modelling components, namely the CastaliaR model, for the generation of synthetic daily data, and the HyetosMinute model, for the disaggregation of daily rainfall to finer temporal scales. Outcomes of this approach are a large number of synthetic flood events, allowing for expressing the design variables in statistical terms and thus properly evaluating the flood risk.

  2. Continuous stochastic approach to birth and death processes and co-operative behaviour of systems far from equilibrium

    Energy Technology Data Exchange (ETDEWEB)

    Chechetkin, V.R.; Lutovinov, V.S.

    1986-09-11

    The continuous stochastic formalism for the description of systems with birth and death processes randomly distributed in space is developed with the use of local birth and death operators and local generalization of the corresponding Chapman-Kolmogorov equation. The functional stochastic equation for the evolution of the probability functional is derived and its modifications for evolution of the characteristic functional and the first passage time problem are given. The corresponding evolution equations for equal-time correlators are also derived. The results are generalized then on the exothermic and endothermic chemical reactions. As examples of the particular applications of the results the small fluctuations near stable equilibrium state and fluctuations in mono-molecular reactions, Lotka-Volterra model, Schloegl reaction and brusselator are considered. It is shown that the two-dimensional Lotka-Volterra model may exhibit synergetic phase transition analogous to the topological transition of the Kosterlitz-Thouless-Berezinskii type. At the end of the paper some general consequences from stochastic evolution of the birth and death processes are discussed and the arguments on their importance in evolution of populations, cellular dynamics and in applications to various chemical and biological problems are presented.

  3. Nonlinear stochastic exclusion financial dynamics modeling and time-dependent intrinsic detrended cross-correlation

    Science.gov (United States)

    Zhang, Wei; Wang, Jun

    2017-09-01

    In attempt to reproduce price dynamics of financial markets, a stochastic agent-based financial price model is proposed and investigated by stochastic exclusion process. The exclusion process, one of interacting particle systems, is usually thought of as modeling particle motion (with the conserved number of particles) in a continuous time Markov process. In this work, the process is utilized to imitate the trading interactions among the investing agents, in order to explain some stylized facts found in financial time series dynamics. To better understand the correlation behaviors of the proposed model, a new time-dependent intrinsic detrended cross-correlation (TDI-DCC) is introduced and performed, also, the autocorrelation analyses are applied in the empirical research. Furthermore, to verify the rationality of the financial price model, the actual return series are also considered to be comparatively studied with the simulation ones. The comparison results of return behaviors reveal that this financial price dynamics model can reproduce some correlation features of actual stock markets.

  4. A separation theorem for the stochastic sampled-data LQG problem. [control of continuous linear plant disturbed by white noise

    Science.gov (United States)

    Halyo, N.; Caglayan, A. K.

    1976-01-01

    This paper considers the control of a continuous linear plant disturbed by white plant noise when the control is constrained to be a piecewise constant function of time; i.e. a stochastic sampled-data system. The cost function is the integral of quadratic error terms in the state and control, thus penalizing errors at every instant of time while the plant noise disturbs the system continuously. The problem is solved by reducing the constrained continuous problem to an unconstrained discrete one. It is shown that the separation principle for estimation and control still holds for this problem when the plant disturbance and measurement noise are Gaussian.

  5. Ranking paths in stochastic time-dependent networks

    DEFF Research Database (Denmark)

    Nielsen, Lars Relund; Andersen, Kim Allan; Pretolani, Daniele D.

    2014-01-01

    In this paper we address optimal routing problems in networks where travel times are both stochastic and time-dependent. In these networks, the best route choice is not necessarily a path, but rather a time-adaptive strategy that assigns successors to nodes as a function of time. Nevertheless, in...

  6. On the small-time behavior of stochastic logistic models

    Directory of Open Access Journals (Sweden)

    Dung Tien Nguyen

    2017-09-01

    Full Text Available In this paper we investigate the small-time behaviors of the solution to  a stochastic logistic model. The obtained results allow us to estimate the number of individuals in the population and can be used to study stochastic prey-predator systems.

  7. Stochastic processes in cell biology

    CERN Document Server

    Bressloff, Paul C

    2014-01-01

    This book develops the theory of continuous and discrete stochastic processes within the context of cell biology.  A wide range of biological topics are covered including normal and anomalous diffusion in complex cellular environments, stochastic ion channels and excitable systems, stochastic calcium signaling, molecular motors, intracellular transport, signal transduction, bacterial chemotaxis, robustness in gene networks, genetic switches and oscillators, cell polarization, polymerization, cellular length control, and branching processes. The book also provides a pedagogical introduction to the theory of stochastic process – Fokker Planck equations, stochastic differential equations, master equations and jump Markov processes, diffusion approximations and the system size expansion, first passage time problems, stochastic hybrid systems, reaction-diffusion equations, exclusion processes, WKB methods, martingales and branching processes, stochastic calculus, and numerical methods.   This text is primarily...

  8. A Random Parameter Model for Continuous-Time Mean-Variance Asset-Liability Management

    Directory of Open Access Journals (Sweden)

    Hui-qiang Ma

    2015-01-01

    Full Text Available We consider a continuous-time mean-variance asset-liability management problem in a market with random market parameters; that is, interest rate, appreciation rates, and volatility rates are considered to be stochastic processes. By using the theories of stochastic linear-quadratic (LQ optimal control and backward stochastic differential equations (BSDEs, we tackle this problem and derive optimal investment strategies as well as the mean-variance efficient frontier analytically in terms of the solution of BSDEs. We find that the efficient frontier is still a parabola in a market with random parameters. Comparing with the existing results, we also find that the liability does not affect the feasibility of the mean-variance portfolio selection problem. However, in an incomplete market with random parameters, the liability can not be fully hedged.

  9. A mean-variance frontier in discrete and continuous time

    NARCIS (Netherlands)

    Bekker, Paul A.

    2004-01-01

    The paper presents a mean-variance frontier based on dynamic frictionless investment strategies in continuous time. The result applies to a finite number of risky assets whose price process is given by multivariate geometric Brownian motion with deterministically varying coefficients. The derivation

  10. Assessment of bidirectional influences between family relationships and adolescent problem behavior: Discrete versus continuous time analysis

    NARCIS (Netherlands)

    Delsing, M.J.M.H.; Oud, J.H.L.; Bruyn, E.E.J. De

    2005-01-01

    In family research, bidirectional influences between the family and the individual are usually analyzed in discrete time. Results from discrete time analysis, however, have been shown to be highly dependent on the length of the observation interval. Continuous time analysis using stochastic

  11. Numerical approximations of stochastic differential equations with non-globally Lipschitz continuous coefficients

    CERN Document Server

    Hutzenthaler, Martin

    2015-01-01

    Many stochastic differential equations (SDEs) in the literature have a superlinearly growing nonlinearity in their drift or diffusion coefficient. Unfortunately, moments of the computationally efficient Euler-Maruyama approximation method diverge for these SDEs in finite time. This article develops a general theory based on rare events for studying integrability properties such as moment bounds for discrete-time stochastic processes. Using this approach, the authors establish moment bounds for fully and partially drift-implicit Euler methods and for a class of new explicit approximation method

  12. Identification of the structure parameters using short-time non-stationary stochastic excitation

    Science.gov (United States)

    Jarczewska, Kamila; Koszela, Piotr; Śniady, PaweŁ; Korzec, Aleksandra

    2011-07-01

    In this paper, we propose an approach to the flexural stiffness or eigenvalue frequency identification of a linear structure using a non-stationary stochastic excitation process. The idea of the proposed approach lies within time domain input-output methods. The proposed method is based on transforming the dynamical problem into a static one by integrating the input and the output signals. The output signal is the structure reaction, i.e. structure displacements due to the short-time, irregular load of random type. The systems with single and multiple degrees of freedom, as well as continuous systems are considered.

  13. Stochastic modeling and analysis of telecoms networks

    CERN Document Server

    Decreusefond, Laurent

    2012-01-01

    This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems.The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an

  14. Stochastic analysis of biochemical systems

    CERN Document Server

    Anderson, David F

    2015-01-01

    This book focuses on counting processes and continuous-time Markov chains motivated by examples and applications drawn from chemical networks in systems biology.  The book should serve well as a supplement for courses in probability and stochastic processes.  While the material is presented in a manner most suitable for students who have studied stochastic processes up to and including martingales in continuous time, much of the necessary background material is summarized in the Appendix. Students and Researchers with a solid understanding of calculus, differential equations, and elementary probability and who are well-motivated by the applications will find this book of interest.    David F. Anderson is Associate Professor in the Department of Mathematics at the University of Wisconsin and Thomas G. Kurtz is Emeritus Professor in the Departments of Mathematics and Statistics at that university. Their research is focused on probability and stochastic processes with applications in biology and other ar...

  15. Testing for Change in Mean of Independent Multivariate Observations with Time Varying Covariance

    Directory of Open Access Journals (Sweden)

    Mohamed Boutahar

    2012-01-01

    Full Text Available We consider a nonparametric CUSUM test for change in the mean of multivariate time series with time varying covariance. We prove that under the null, the test statistic has a Kolmogorov limiting distribution. The asymptotic consistency of the test against a large class of alternatives which contains abrupt, smooth and continuous changes is established. We also perform a simulation study to analyze the size distortion and the power of the proposed test.

  16. Drunk driving detection based on classification of multivariate time series.

    Science.gov (United States)

    Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

    2015-09-01

    This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

  17. Continuous-Time Mean-Variance Portfolio Selection under the CEV Process

    OpenAIRE

    Ma, Hui-qiang

    2014-01-01

    We consider a continuous-time mean-variance portfolio selection model when stock price follows the constant elasticity of variance (CEV) process. The aim of this paper is to derive an optimal portfolio strategy and the efficient frontier. The mean-variance portfolio selection problem is formulated as a linearly constrained convex program problem. By employing the Lagrange multiplier method and stochastic optimal control theory, we obtain the optimal portfolio strategy and mean-variance effici...

  18. Stochastic Landau equation with time-dependent drift

    International Nuclear Information System (INIS)

    Swift, J.B.; Hohenberg, P.C.; Ahlers, G.

    1991-01-01

    The stochastic differential equation τ 0 ∂ tA =ε(t)A-g 3 A 3 +bar f(t), where bar f(t) is Gaussian white noise, is studied for arbitrary time dependence of ε(t). In particular, cases are considered where ε(t) goes through the bifurcation of the deterministic system, which occurs at ε=0. In the limit of weak noise an approximate analytic expression generalizing earlier work of Suzuki [Phys. Lett. A 67, 339 (1978); Prog. Theor. Phys. (Kyoto) Suppl. 64, 402 (1978)] is obtained for the time-dependent distribution function P(A,t). The results compare favorably with a numerical simulation of the stochastic equation for the case of a linear ramp (both increasing and decreasing) and for a periodic time dependence of ε(t). The procedure can be generalized to an arbitrary deterministic part ∂ tA =D(A,t)+bar f(t), but the deterministic equation may then have to be solved numerically

  19. Network structure of multivariate time series.

    Science.gov (United States)

    Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

    2015-10-21

    Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

  20. Stochastic Simulation and Forecast of Hydrologic Time Series Based on Probabilistic Chaos Expansion

    Science.gov (United States)

    Li, Z.; Ghaith, M.

    2017-12-01

    Hydrological processes are characterized by many complex features, such as nonlinearity, dynamics and uncertainty. How to quantify and address such complexities and uncertainties has been a challenging task for water engineers and managers for decades. To support robust uncertainty analysis, an innovative approach for the stochastic simulation and forecast of hydrologic time series is developed is this study. Probabilistic Chaos Expansions (PCEs) are established through probabilistic collocation to tackle uncertainties associated with the parameters of traditional hydrological models. The uncertainties are quantified in model outputs as Hermite polynomials with regard to standard normal random variables. Sequentially, multivariate analysis techniques are used to analyze the complex nonlinear relationships between meteorological inputs (e.g., temperature, precipitation, evapotranspiration, etc.) and the coefficients of the Hermite polynomials. With the established relationships between model inputs and PCE coefficients, forecasts of hydrologic time series can be generated and the uncertainties in the future time series can be further tackled. The proposed approach is demonstrated using a case study in China and is compared to a traditional stochastic simulation technique, the Markov-Chain Monte-Carlo (MCMC) method. Results show that the proposed approach can serve as a reliable proxy to complicated hydrological models. It can provide probabilistic forecasting in a more computationally efficient manner, compared to the traditional MCMC method. This work provides technical support for addressing uncertainties associated with hydrological modeling and for enhancing the reliability of hydrological modeling results. Applications of the developed approach can be extended to many other complicated geophysical and environmental modeling systems to support the associated uncertainty quantification and risk analysis.

  1. A primer on stochastic epidemic models: Formulation, numerical simulation, and analysis

    Directory of Open Access Journals (Sweden)

    Linda J.S. Allen

    2017-05-01

    Full Text Available Some mathematical methods for formulation and numerical simulation of stochastic epidemic models are presented. Specifically, models are formulated for continuous-time Markov chains and stochastic differential equations. Some well-known examples are used for illustration such as an SIR epidemic model and a host-vector malaria model. Analytical methods for approximating the probability of a disease outbreak are also discussed. Keywords: Branching process, Continuous-time Markov chain, Minor outbreak, Stochastic differential equation, 2000 MSC: 60H10, 60J28, 92D30

  2. Stochastic lag time in nucleated linear self-assembly

    Energy Technology Data Exchange (ETDEWEB)

    Tiwari, Nitin S. [Group Theory of Polymers and Soft Matter, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven (Netherlands); Schoot, Paul van der [Group Theory of Polymers and Soft Matter, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven (Netherlands); Institute for Theoretical Physics, Utrecht University, Leuvenlaan 4, 3584 CE Utrecht (Netherlands)

    2016-06-21

    Protein aggregation is of great importance in biology, e.g., in amyloid fibrillation. The aggregation processes that occur at the cellular scale must be highly stochastic in nature because of the statistical number fluctuations that arise on account of the small system size at the cellular scale. We study the nucleated reversible self-assembly of monomeric building blocks into polymer-like aggregates using the method of kinetic Monte Carlo. Kinetic Monte Carlo, being inherently stochastic, allows us to study the impact of fluctuations on the polymerization reactions. One of the most important characteristic features in this kind of problem is the existence of a lag phase before self-assembly takes off, which is what we focus attention on. We study the associated lag time as a function of system size and kinetic pathway. We find that the leading order stochastic contribution to the lag time before polymerization commences is inversely proportional to the system volume for large-enough system size for all nine reaction pathways tested. Finite-size corrections to this do depend on the kinetic pathway.

  3. A study on the stochastic model for nuclide transport in the fractured porous rock using continuous time Markov process

    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

  4. Adaptive logical stochastic resonance in time-delayed synthetic genetic networks

    Science.gov (United States)

    Zhang, Lei; Zheng, Wenbin; Song, Aiguo

    2018-04-01

    In the paper, the concept of logical stochastic resonance is applied to implement logic operation and latch operation in time-delayed synthetic genetic networks derived from a bacteriophage λ. Clear logic operation and latch operation can be obtained when the network is tuned by modulated periodic force and time-delay. In contrast with the previous synthetic genetic networks based on logical stochastic resonance, the proposed system has two advantages. On one hand, adding modulated periodic force to the background noise can increase the length of the optimal noise plateau of obtaining desired logic response and make the system adapt to varying noise intensity. On the other hand, tuning time-delay can extend the optimal noise plateau to larger range. The result provides possible help for designing new genetic regulatory networks paradigm based on logical stochastic resonance.

  5. A theory of Markovian time-inconsistent stochastic control in discrete time

    DEFF Research Database (Denmark)

    Bjork, Tomas; Murgoci, Agatha

    2014-01-01

    We develop a theory for a general class of discrete-time stochastic control problems that, in various ways, are time-inconsistent in the sense that they do not admit a Bellman optimality principle. We attack these problems by viewing them within a game theoretic framework, and we look for subgame...

  6. H∞ state estimation for discrete-time memristive recurrent neural networks with stochastic time-delays

    Science.gov (United States)

    Liu, Hongjian; Wang, Zidong; Shen, Bo; Alsaadi, Fuad E.

    2016-07-01

    This paper deals with the robust H∞ state estimation problem for a class of memristive recurrent neural networks with stochastic time-delays. The stochastic time-delays under consideration are governed by a Bernoulli-distributed stochastic sequence. The purpose of the addressed problem is to design the robust state estimator such that the dynamics of the estimation error is exponentially stable in the mean square, and the prescribed ? performance constraint is met. By utilizing the difference inclusion theory and choosing a proper Lyapunov-Krasovskii functional, the existence condition of the desired estimator is derived. Based on it, the explicit expression of the estimator gain is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is employed to demonstrate the effectiveness and applicability of the proposed estimation approach.

  7. Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data.

    Science.gov (United States)

    Vial, Flavie; Wei, Wei; Held, Leonhard

    2016-12-20

    In an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues' two-component model, to two multivariate animal health datasets from Switzerland. In our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities. Stochastic modelling approaches offer the

  8. DTW-APPROACH FOR UNCORRELATED MULTIVARIATE TIME SERIES IMPUTATION

    OpenAIRE

    Phan , Thi-Thu-Hong; Poisson Caillault , Emilie; Bigand , André; Lefebvre , Alain

    2017-01-01

    International audience; Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper , we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of...

  9. Investment timing under hybrid stochastic and local volatility

    International Nuclear Information System (INIS)

    Kim, Jeong-Hoon; Lee, Min-Ku; Sohn, So Young

    2014-01-01

    Highlights: • The effects of hybrid stochastic volatility on real option prices are studied. • The stochastic volatility consists of a fast mean-reverting component and a CEV type one. • A fast mean-reverting factor lowers real option prices and investment thresholds. • The increase of elasticity raises real option prices and investment thresholds. • The effects of the addition of a slowly varying factor depend upon the project value. - Abstract: We consider an investment timing problem under a real option model where the instantaneous volatility of the project value is given by a combination of a hidden stochastic process and the project value itself. The stochastic volatility part is given by a function of a fast mean-reverting process as well as a slowly varying process and the local volatility part is a power (the elasticity parameter) of the project value itself. The elasticity parameter controls directly the correlation between the project value and the volatility. Knowing that the project value represents the market price of a real asset in many applications and the value of the elasticity parameter depends on the asset, the elasticity parameter should be treated with caution for investment decision problems. Based on the hybrid structure of volatility, we investigate the simultaneous impact of the elasticity and the stochastic volatility on the real option value as well as the investment threshold

  10. Global exponential stability of neutral high-order stochastic Hopfield neural networks with Markovian jump parameters and mixed time delays.

    Science.gov (United States)

    Huang, Haiying; Du, Qiaosheng; Kang, Xibing

    2013-11-01

    In this paper, a class of neutral high-order stochastic Hopfield neural networks with Markovian jump parameters and mixed time delays is investigated. The jumping parameters are modeled as a continuous-time finite-state Markov chain. At first, the existence of equilibrium point for the addressed neural networks is studied. By utilizing the Lyapunov stability theory, stochastic analysis theory and linear matrix inequality (LMI) technique, new delay-dependent stability criteria are presented in terms of linear matrix inequalities to guarantee the neural networks to be globally exponentially stable in the mean square. Numerical simulations are carried out to illustrate the main results. © 2013 ISA. Published by ISA. All rights reserved.

  11. Statistical Methods for Stochastic Differential Equations

    CERN Document Server

    Kessler, Mathieu; Sorensen, Michael

    2012-01-01

    The seventh volume in the SemStat series, Statistical Methods for Stochastic Differential Equations presents current research trends and recent developments in statistical methods for stochastic differential equations. Written to be accessible to both new students and seasoned researchers, each self-contained chapter starts with introductions to the topic at hand and builds gradually towards discussing recent research. The book covers Wiener-driven equations as well as stochastic differential equations with jumps, including continuous-time ARMA processes and COGARCH processes. It presents a sp

  12. I - Multivariate Classification and Machine Learning in HEP

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    Traditional multivariate methods for classification (Stochastic Gradient Boosted Decision Trees and Multi-Layer Perceptrons) are explained in theory and practise using examples from HEP. General aspects of multivariate classification are discussed, in particular different regularisation techniques. Afterwards, data-driven techniques are introduced and compared to MC-based methods.

  13. 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

  14. A mean-variance frontier in discrete and continuous time

    OpenAIRE

    Bekker, Paul A.

    2004-01-01

    The paper presents a mean-variance frontier based on dynamic frictionless investment strategies in continuous time. The result applies to a finite number of risky assets whose price process is given by multivariate geometric Brownian motion with deterministically varying coefficients. The derivation is based on the solution for the frontier in discrete time. Using the same multiperiod framework as Li and Ng (2000), I provide an alternative derivation and an alternative formulation of the solu...

  15. Lyapunov functionals and stability of stochastic functional differential equations

    CERN Document Server

    Shaikhet, Leonid

    2013-01-01

    Stability conditions for functional differential equations can be obtained using Lyapunov functionals. Lyapunov Functionals and Stability of Stochastic Functional Differential Equations describes the general method of construction of Lyapunov functionals to investigate the stability of differential equations with delays. This work continues and complements the author’s previous book Lyapunov Functionals and Stability of Stochastic Difference Equations, where this method is described for discrete- and continuous-time difference equations. The text begins with a description of the peculiarities of deterministic and stochastic functional differential equations. There follow basic definitions for stability theory of stochastic hereditary systems, and a formal procedure of Lyapunov functionals construction is presented. Stability investigation is conducted for stochastic linear and nonlinear differential equations with constant and distributed delays. The proposed method is used for stability investigation of di...

  16. Multivariate normal maximum likelihood with both ordinal and continuous variables, and data missing at random.

    Science.gov (United States)

    Pritikin, Joshua N; Brick, Timothy R; Neale, Michael C

    2018-04-01

    A novel method for the maximum likelihood estimation of structural equation models (SEM) with both ordinal and continuous indicators is introduced using a flexible multivariate probit model for the ordinal indicators. A full information approach ensures unbiased estimates for data missing at random. Exceeding the capability of prior methods, up to 13 ordinal variables can be included before integration time increases beyond 1 s per row. The method relies on the axiom of conditional probability to split apart the distribution of continuous and ordinal variables. Due to the symmetry of the axiom, two similar methods are available. A simulation study provides evidence that the two similar approaches offer equal accuracy. A further simulation is used to develop a heuristic to automatically select the most computationally efficient approach. Joint ordinal continuous SEM is implemented in OpenMx, free and open-source software.

  17. Time-adaptive and history-adaptive multicriterion routing in stochastic, time-dependent networks

    DEFF Research Database (Denmark)

    Pretolani, Daniele; Nielsen, Lars Relund; Andersen, Kim Allan

    2009-01-01

    We compare two different models for multicriterion routing in stochastic time-dependent networks: the classic "time-adaptive'' model and the more flexible "history-adaptive'' one. We point out several properties of the sets of efficient solutions found under the two models. We also devise a method...

  18. Exponential stability of uncertain stochastic neural networks with mixed time-delays

    International Nuclear Information System (INIS)

    Wang Zidong; Lauria, Stanislao; Fang Jian'an; Liu Xiaohui

    2007-01-01

    This paper is concerned with the global exponential stability analysis problem for a class of stochastic neural networks with mixed time-delays and parameter uncertainties. The mixed delays comprise discrete and distributed time-delays, the parameter uncertainties are norm-bounded, and the neural networks are subjected to stochastic disturbances described in terms of a Brownian motion. The purpose of the stability analysis problem is to derive easy-to-test criteria under which the delayed stochastic neural network is globally, robustly, exponentially stable in the mean square for all admissible parameter uncertainties. By resorting to the Lyapunov-Krasovskii stability theory and the stochastic analysis tools, sufficient stability conditions are established by using an efficient linear matrix inequality (LMI) approach. The proposed criteria can be checked readily by using recently developed numerical packages, where no tuning of parameters is required. An example is provided to demonstrate the usefulness of the proposed criteria

  19. A Range-Based Multivariate Model for Exchange Rate Volatility

    NARCIS (Netherlands)

    B. Tims (Ben); R.J. Mahieu (Ronald)

    2003-01-01

    textabstractIn this paper we present a parsimonious multivariate model for exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates. The multivariate stochastic volatility model divides the log range of each exchange rate into two independent latent factors, which are

  20. Permutation Tests for Stochastic Ordering and ANOVA

    CERN Document Server

    Basso, Dario; Salmaso, Luigi; Solari, Aldo

    2009-01-01

    Permutation testing for multivariate stochastic ordering and ANOVA designs is a fundamental issue in many scientific fields such as medicine, biology, pharmaceutical studies, engineering, economics, psychology, and social sciences. This book presents advanced methods and related R codes to perform complex multivariate analyses

  1. Stochastic resonance in a time-delayed asymmetric bistable system with mixed periodic signal

    International Nuclear Information System (INIS)

    Yong-Feng, Guo; Wei, Xu; Liang, Wang

    2010-01-01

    This paper studies the phenomenon of stochastic resonance in an asymmetric bistable system with time-delayed feedback and mixed periodic signal by using the theory of signal-to-noise ratio in the adiabatic limit. A general approximate Fokker–Planck equation and the expression of the signal-to-noise ratio are derived through the small time delay approximation at both fundamental harmonics and mixed harmonics. The effects of the additive noise intensity Q, multiplicative noise intensity D, static asymmetry r and delay time τ on the signal-to-noise ratio are discussed. It is found that the higher mixed harmonics and the static asymmetry r can restrain stochastic resonance, and the delay time τ can enhance stochastic resonance. Moreover, the longer the delay time τ is, the larger the additive noise intensity Q and the multiplicative noise intensity D are, when the stochastic resonance appears. (general)

  2. Stochastic Averaging and Stochastic Extremum Seeking

    CERN Document Server

    Liu, Shu-Jun

    2012-01-01

    Stochastic Averaging and Stochastic Extremum Seeking develops methods of mathematical analysis inspired by the interest in reverse engineering  and analysis of bacterial  convergence by chemotaxis and to apply similar stochastic optimization techniques in other environments. The first half of the text presents significant advances in stochastic averaging theory, necessitated by the fact that existing theorems are restricted to systems with linear growth, globally exponentially stable average models, vanishing stochastic perturbations, and prevent analysis over infinite time horizon. The second half of the text introduces stochastic extremum seeking algorithms for model-free optimization of systems in real time using stochastic perturbations for estimation of their gradients. Both gradient- and Newton-based algorithms are presented, offering the user the choice between the simplicity of implementation (gradient) and the ability to achieve a known, arbitrary convergence rate (Newton). The design of algorithms...

  3. Constructing ordinal partition transition networks from multivariate time series.

    Science.gov (United States)

    Zhang, Jiayang; Zhou, Jie; Tang, Ming; Guo, Heng; Small, Michael; Zou, Yong

    2017-08-10

    A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.

  4. An overview of multivariate gamma distributions as seen from a (multivariate) matrix exponential perspective

    DEFF Research Database (Denmark)

    Bladt, Mogens; Nielsen, Bo Friis

    2012-01-01

    Laplace transform. In a longer perspective stochastic and statistical analysis for MVME will in particular apply to any of the previously defined distributions. Multivariate gamma distributions have been used in a variety of fields like hydrology, [11], [10], [6], space (wind modeling) [9] reliability [3......Numerous definitions of multivariate exponential and gamma distributions can be retrieved from the literature [4]. These distribtuions belong to the class of Multivariate Matrix-- Exponetial Distributions (MVME) whenever their joint Laplace transform is a rational function. The majority...... of these distributions further belongs to an important subclass of MVME distributions [5, 1] where the multivariate random vector can be interpreted as a number of simultaneously collected rewards during sojourns in a the states of a Markov chain with one absorbing state, the rest of the states being transient. We...

  5. Capabilities of R Package mixAK for Clustering Based on Multivariate Continuous and Discrete Longitudinal Data

    Directory of Open Access Journals (Sweden)

    Arnošt Komárek

    2014-09-01

    Full Text Available R package mixAK originally implemented routines primarily for Bayesian estimation of finite normal mixture models for possibly interval-censored data. The functionality of the package was considerably enhanced by implementing methods for Bayesian estimation of mixtures of multivariate generalized linear mixed models proposed in Komrek and Komrkov (2013. Among other things, this allows for a cluster analysis (classification based on multivariate continuous and discrete longitudinal data that arise whenever multiple outcomes of a different nature are recorded in a longitudinal study. This package also allows for a data-driven selection of a number of clusters as methods for selecting a number of mixture components were implemented. A model and clustering methodology for multivariate continuous and discrete longitudinal data is overviewed. Further, a step-by-step cluster analysis based jointly on three longitudinal variables of different types (continuous, count, dichotomous is given, which provides a user manual for using the package for similar problems.

  6. Exact norm-conserving stochastic time-dependent Hartree-Fock

    International Nuclear Information System (INIS)

    Tessieri, Luca; Wilkie, Joshua; Cetinbas, Murat

    2005-01-01

    We derive an exact single-body decomposition of the time-dependent Schroedinger equation for N pairwise interacting fermions. Each fermion obeys a stochastic time-dependent norm-preserving wave equation. As a first test of the method, we calculate the low energy spectrum of helium. An extension of the method to bosons is outlined

  7. Introduction to stochastic calculus

    CERN Document Server

    Karandikar, Rajeeva L

    2018-01-01

    This book sheds new light on stochastic calculus, the branch of mathematics that is most widely applied in financial engineering and mathematical finance. The first book to introduce pathwise formulae for the stochastic integral, it provides a simple but rigorous treatment of the subject, including a range of advanced topics. The book discusses in-depth topics such as quadratic variation, Ito formula, and Emery topology. The authors briefly address continuous semi-martingales to obtain growth estimates and study solution of a stochastic differential equation (SDE) by using the technique of random time change. Later, by using Metivier–Pellumail inequality, the solutions to SDEs driven by general semi-martingales are discussed. The connection of the theory with mathematical finance is briefly discussed and the book has extensive treatment on the representation of martingales as stochastic integrals and a second fundamental theorem of asset pricing. Intended for undergraduate- and beginning graduate-level stud...

  8. On the speed towards the mean for continuous time autoregressive moving average processes with applications to energy markets

    International Nuclear Information System (INIS)

    Benth, Fred Espen; Taib, Che Mohd Imran Che

    2013-01-01

    We extend the concept of half life of an Ornstein–Uhlenbeck process to Lévy-driven continuous-time autoregressive moving average processes with stochastic volatility. The half life becomes state dependent, and we analyze its properties in terms of the characteristics of the process. An empirical example based on daily temperatures observed in Petaling Jaya, Malaysia, is presented, where the proposed model is estimated and the distribution of the half life is simulated. The stationarity of the dynamics yield futures prices which asymptotically tend to constant at an exponential rate when time to maturity goes to infinity. The rate is characterized by the eigenvalues of the dynamics. An alternative description of this convergence can be given in terms of our concept of half life. - Highlights: • The concept of half life is extended to Levy-driven continuous time autoregressive moving average processes • The dynamics of Malaysian temperatures are modeled using a continuous time autoregressive model with stochastic volatility • Forward prices on temperature become constant when time to maturity tends to infinity • Convergence in time to maturity is at an exponential rate given by the eigenvalues of the model temperature model

  9. Event-Triggered Faults Tolerant Control for Stochastic Systems with Time Delays

    Directory of Open Access Journals (Sweden)

    Ling Huang

    2016-01-01

    Full Text Available This paper is concerned with the state-feedback controller design for stochastic networked control systems (NCSs with random actuator failures and transmission delays. Firstly, an event-triggered scheme is introduced to optimize the performance of the stochastic NCSs. Secondly, stochastic NCSs under event-triggered scheme are modeled as stochastic time-delay systems. Thirdly, some less conservative delay-dependent stability criteria in terms of linear matrix inequalities for the codesign of both the controller gain and the trigger parameters are obtained by using delay-decomposition technique and convex combination approach. Finally, a numerical example is provided to show the less sampled data transmission and less conservatism of the proposed theory.

  10. Brownian motion, martingales, and stochastic calculus

    CERN Document Server

    Le Gall, Jean-François

    2016-01-01

    This book offers a rigorous and self-contained presentation of stochastic integration and stochastic calculus within the general framework of continuous semimartingales. The main tools of stochastic calculus, including Itô’s formula, the optional stopping theorem and Girsanov’s theorem, are treated in detail alongside many illustrative examples. The book also contains an introduction to Markov processes, with applications to solutions of stochastic differential equations and to connections between Brownian motion and partial differential equations. The theory of local times of semimartingales is discussed in the last chapter. Since its invention by Itô, stochastic calculus has proven to be one of the most important techniques of modern probability theory, and has been used in the most recent theoretical advances as well as in applications to other fields such as mathematical finance. Brownian Motion, Martingales, and Stochastic Calculus provides a strong theoretical background to the reader interested i...

  11. Single-molecule stochastic times in a reversible bimolecular reaction

    Science.gov (United States)

    Keller, Peter; Valleriani, Angelo

    2012-08-01

    In this work, we consider the reversible reaction between reactants of species A and B to form the product C. We consider this reaction as a prototype of many pseudobiomolecular reactions in biology, such as for instance molecular motors. We derive the exact probability density for the stochastic waiting time that a molecule of species A needs until the reaction with a molecule of species B takes place. We perform this computation taking fully into account the stochastic fluctuations in the number of molecules of species B. We show that at low numbers of participating molecules, the exact probability density differs from the exponential density derived by assuming the law of mass action. Finally, we discuss the condition of detailed balance in the exact stochastic and in the approximate treatment.

  12. Stochastic time-dependent vehicle routing problem: Mathematical models and ant colony algorithm

    Directory of Open Access Journals (Sweden)

    Zhengyu Duan

    2015-11-01

    Full Text Available This article addresses the stochastic time-dependent vehicle routing problem. Two mathematical models named robust optimal schedule time model and minimum expected schedule time model are proposed for stochastic time-dependent vehicle routing problem, which can guarantee delivery within the time windows of customers. The robust optimal schedule time model only requires the variation range of link travel time, which can be conveniently derived from historical traffic data. In addition, the robust optimal schedule time model based on robust optimization method can be converted into a time-dependent vehicle routing problem. Moreover, an ant colony optimization algorithm is designed to solve stochastic time-dependent vehicle routing problem. As the improvements in initial solution and transition probability, ant colony optimization algorithm has a good performance in convergence. Through computational instances and Monte Carlo simulation tests, robust optimal schedule time model is proved to be better than minimum expected schedule time model in computational efficiency and coping with the travel time fluctuations. Therefore, robust optimal schedule time model is applicable in real road network.

  13. Stochastic inflation as a time-dependent random walk

    International Nuclear Information System (INIS)

    Kandrup, H.E.

    1989-01-01

    This paper exploits the ''stochastic inflation'' paradigm introduced by Starobinskii to study the evolution of long-wavelength modes for a free scalar field Phi in an inflationary Universe. By relaxing the assumption of a ''slow roll,'' it becomes obvious that the well-known late-time infrared divergence of the vacuum for a massless field in de Sitter space may be viewed as a consequence of the fluctuation-dissipation theorem. This stochastic model is also extended to allow for nonvacuum states and power-law inflation, situations where the fluctuation-dissipation theorem no longer holds. One recovers the correct late-time form for the expectation value 2 > in these cases as well, corroborating thereby the intuitive picture that, quite generally, the long-wavelength modes of the field evolve in a thermal ''bath'' provided by the shorter-wavelength modes

  14. A Computationally Efficient and Robust Implementation of the Continuous-Discrete Extended Kalman Filter

    DEFF Research Database (Denmark)

    Jørgensen, John Bagterp; Thomsen, Per Grove; Madsen, Henrik

    2007-01-01

    for nonlinear stochastic continuous-discrete time systems is more than two orders of magnitude faster than a conventional implementation. This is of significance in nonlinear model predictive control applications, statistical process monitoring as well as grey-box modelling of systems described by stochastic......We present a novel numerically robust and computationally efficient extended Kalman filter for state estimation in nonlinear continuous-discrete stochastic systems. The resulting differential equations for the mean-covariance evolution of the nonlinear stochastic continuous-discrete time systems...

  15. Stochastic Model Checking of the Stochastic Quality Calculus

    DEFF Research Database (Denmark)

    Nielson, Flemming; Nielson, Hanne Riis; Zeng, Kebin

    2015-01-01

    The Quality Calculus uses quality binders for input to express strategies for continuing the computation even when the desired input has not been received. The Stochastic Quality Calculus adds generally distributed delays for output actions and real-time constraints on the quality binders for input....... This gives rise to Generalised Semi-Markov Decision Processes for which few analytical techniques are available. We restrict delays on output actions to be exponentially distributed while still admitting real-time constraints on the quality binders. This facilitates developing analytical techniques based...

  16. Parameter estimation in stochastic differential equations

    CERN Document Server

    Bishwal, Jaya P N

    2008-01-01

    Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modelling complex phenomena and making beautiful decisions. The subject has attracted researchers from several areas of mathematics and other related fields like economics and finance. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods. Useful because of the current availability of high frequency data is the study of refined asymptotic properties of several estimators when the observation time length is large and the observation time interval is small. Also space time white noise driven models, useful for spatial data, and more sophisticated non-Markovian and non-semimartingale models like fractional diffusions that model the long memory phenomena are examined in this volume.

  17. Quantum mechanics and stochastic mechanics for compatible observables at different times

    International Nuclear Information System (INIS)

    Correggi, M.; Morchio, G.

    2002-01-01

    Bohm mechanics and Nelson stochastic mechanics are confronted with quantum mechanics in the presence of noninteracting subsystems. In both cases, it is shown that correlations at different times of compatible position observables on stationary states agree with quantum mechanics only in the case of product wave functions. By appropriate Bell-like inequalities it is shown that no classical theory, in particular no stochastic process, can reproduce the quantum mechanical correlations of position variables of noninteracting systems at different times

  18. Stochastic differential equations as a tool to regularize the parameter estimation problem for continuous time dynamical systems given discrete time measurements.

    Science.gov (United States)

    Leander, Jacob; Lundh, Torbjörn; Jirstrand, Mats

    2014-05-01

    In this paper we consider the problem of estimating parameters in ordinary differential equations given discrete time experimental data. The impact of going from an ordinary to a stochastic differential equation setting is investigated as a tool to overcome the problem of local minima in the objective function. Using two different models, it is demonstrated that by allowing noise in the underlying model itself, the objective functions to be minimized in the parameter estimation procedures are regularized in the sense that the number of local minima is reduced and better convergence is achieved. The advantage of using stochastic differential equations is that the actual states in the model are predicted from data and this will allow the prediction to stay close to data even when the parameters in the model is incorrect. The extended Kalman filter is used as a state estimator and sensitivity equations are provided to give an accurate calculation of the gradient of the objective function. The method is illustrated using in silico data from the FitzHugh-Nagumo model for excitable media and the Lotka-Volterra predator-prey system. The proposed method performs well on the models considered, and is able to regularize the objective function in both models. This leads to parameter estimation problems with fewer local minima which can be solved by efficient gradient-based methods. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  19. A Stochastic Model for Malaria Transmission Dynamics

    Directory of Open Access Journals (Sweden)

    Rachel Waema Mbogo

    2018-01-01

    Full Text Available Malaria is one of the three most dangerous infectious diseases worldwide (along with HIV/AIDS and tuberculosis. In this paper we compare the disease dynamics of the deterministic and stochastic models in order to determine the effect of randomness in malaria transmission dynamics. Relationships between the basic reproduction number for malaria transmission dynamics between humans and mosquitoes and the extinction thresholds of corresponding continuous-time Markov chain models are derived under certain assumptions. The stochastic model is formulated using the continuous-time discrete state Galton-Watson branching process (CTDSGWbp. The reproduction number of deterministic models is an essential quantity to predict whether an epidemic will spread or die out. Thresholds for disease extinction from stochastic models contribute crucial knowledge on disease control and elimination and mitigation of infectious diseases. Analytical and numerical results show some significant differences in model predictions between the stochastic and deterministic models. In particular, we find that malaria outbreak is more likely if the disease is introduced by infected mosquitoes as opposed to infected humans. These insights demonstrate the importance of a policy or intervention focusing on controlling the infected mosquito population if the control of malaria is to be realized.

  20. Susceptibility of optimal train schedules to stochastic disturbances of process times

    DEFF Research Database (Denmark)

    Larsen, Rune; Pranzo, Marco; D’Ariano, Andrea

    2013-01-01

    study, an advanced branch and bound algorithm, on average, outperforms a First In First Out scheduling rule both in deterministic and stochastic traffic scenarios. However, the characteristic of the stochastic processes and the way a stochastic instance is handled turn out to have a serious impact...... and dwell times). In fact, the objective of railway traffic management is to reduce delay propagation and to increase disturbance robustness of train schedules at a network scale. We present a quantitative study of traffic disturbances and their effects on the schedules computed by simple and advanced...

  1. Adaptive Asymptotical Synchronization for Stochastic Complex Networks with Time-Delay and Markovian Switching

    Directory of Open Access Journals (Sweden)

    Xueling Jiang

    2014-01-01

    Full Text Available The problem of adaptive asymptotical synchronization is discussed for the stochastic complex dynamical networks with time-delay and Markovian switching. By applying the stochastic analysis approach and the M-matrix method for stochastic complex networks, several sufficient conditions to ensure adaptive asymptotical synchronization for stochastic complex networks are derived. Through the adaptive feedback control techniques, some suitable parameters update laws are obtained. Simulation result is provided to substantiate the effectiveness and characteristics of the proposed approach.

  2. Existence of time-periodic weak solutions to the stochastic Navier-Stokes equations around a moving body

    International Nuclear Information System (INIS)

    Chen, Feng; Han, Yuecai

    2013-01-01

    The existence of time-periodic stochastic motions of an incompressible fluid is obtained. Here the fluid is subject to a time-periodic body force and an additional time-periodic stochastic force that is produced by a rigid body moves periodically stochastically with the same period in the fluid

  3. Existence of time-periodic weak solutions to the stochastic Navier-Stokes equations around a moving body

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Feng, E-mail: chenfengmath@163.com, E-mail: hanyc@jlu.edu.cn; Han, Yuecai, E-mail: chenfengmath@163.com, E-mail: hanyc@jlu.edu.cn [School of Mathematics, Jilin University, Changchun 130012 (China)

    2013-12-15

    The existence of time-periodic stochastic motions of an incompressible fluid is obtained. Here the fluid is subject to a time-periodic body force and an additional time-periodic stochastic force that is produced by a rigid body moves periodically stochastically with the same period in the fluid.

  4. Continuous time modelling of dynamical spatial lattice data observed at sparsely distributed times

    DEFF Research Database (Denmark)

    Rasmussen, Jakob Gulddahl; Møller, Jesper

    2007-01-01

    Summary. We consider statistical and computational aspects of simulation-based Bayesian inference for a spatial-temporal model based on a multivariate point process which is only observed at sparsely distributed times. The point processes are indexed by the sites of a spatial lattice......, and they exhibit spatial interaction. For specificity we consider a particular dynamical spatial lattice data set which has previously been analysed by a discrete time model involving unknown normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared...... with discrete time processes in the setting of the present paper as well as other spatial-temporal situations....

  5. Effectiveness of Multivariate Time Series Classification Using Shapelets

    Directory of Open Access Journals (Sweden)

    A. P. Karpenko

    2015-01-01

    Full Text Available Typically, time series classifiers require signal pre-processing (filtering signals from noise and artifact removal, etc., enhancement of signal features (amplitude, frequency, spectrum, etc., classification of signal features in space using the classical techniques and classification algorithms of multivariate data. We consider a method of classifying time series, which does not require enhancement of the signal features. The method uses the shapelets of time series (time series shapelets i.e. small fragments of this series, which reflect properties of one of its classes most of all.Despite the significant number of publications on the theory and shapelet applications for classification of time series, the task to evaluate the effectiveness of this technique remains relevant. An objective of this publication is to study the effectiveness of a number of modifications of the original shapelet method as applied to the multivariate series classification that is a littlestudied problem. The paper presents the problem statement of multivariate time series classification using the shapelets and describes the shapelet–based basic method of binary classification, as well as various generalizations and proposed modification of the method. It also offers the software that implements a modified method and results of computational experiments confirming the effectiveness of the algorithmic and software solutions.The paper shows that the modified method and the software to use it allow us to reach the classification accuracy of about 85%, at best. The shapelet search time increases in proportion to input data dimension.

  6. Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

    Science.gov (United States)

    Price, Larry R.

    2012-01-01

    The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

  7. Time varying, multivariate volume data reduction

    Energy Technology Data Exchange (ETDEWEB)

    Ahrens, James P [Los Alamos National Laboratory; Fout, Nathaniel [UC DAVIS; Ma, Kwan - Liu [UC DAVIS

    2010-01-01

    Large-scale supercomputing is revolutionizing the way science is conducted. A growing challenge, however, is understanding the massive quantities of data produced by large-scale simulations. The data, typically time-varying, multivariate, and volumetric, can occupy from hundreds of gigabytes to several terabytes of storage space. Transferring and processing volume data of such sizes is prohibitively expensive and resource intensive. Although it may not be possible to entirely alleviate these problems, data compression should be considered as part of a viable solution, especially when the primary means of data analysis is volume rendering. In this paper we present our study of multivariate compression, which exploits correlations among related variables, for volume rendering. Two configurations for multidimensional compression based on vector quantization are examined. We emphasize quality reconstruction and interactive rendering, which leads us to a solution using graphics hardware to perform on-the-fly decompression during rendering. In this paper we present a solution which addresses the need for data reduction in large supercomputing environments where data resulting from simulations occupies tremendous amounts of storage. Our solution employs a lossy encoding scheme to acrueve data reduction with several options in terms of rate-distortion behavior. We focus on encoding of multiple variables together, with optional compression in space and time. The compressed volumes can be rendered directly with commodity graphics cards at interactive frame rates and rendering quality similar to that of static volume renderers. Compression results using a multivariate time-varying data set indicate that encoding multiple variables results in acceptable performance in the case of spatial and temporal encoding as compared to independent compression of variables. The relative performance of spatial vs. temporal compression is data dependent, although temporal compression has the

  8. Integrating stochastic time-dependent travel speed in solution methods for the dynamic dial-a-ride problem.

    Science.gov (United States)

    Schilde, M; Doerner, K F; Hartl, R F

    2014-10-01

    In urban areas, logistic transportation operations often run into problems because travel speeds change, depending on the current traffic situation. If not accounted for, time-dependent and stochastic travel speeds frequently lead to missed time windows and thus poorer service. Especially in the case of passenger transportation, it often leads to excessive passenger ride times as well. Therefore, time-dependent and stochastic influences on travel speeds are relevant for finding feasible and reliable solutions. This study considers the effect of exploiting statistical information available about historical accidents, using stochastic solution approaches for the dynamic dial-a-ride problem (dynamic DARP). The authors propose two pairs of metaheuristic solution approaches, each consisting of a deterministic method (average time-dependent travel speeds for planning) and its corresponding stochastic version (exploiting stochastic information while planning). The results, using test instances with up to 762 requests based on a real-world road network, show that in certain conditions, exploiting stochastic information about travel speeds leads to significant improvements over deterministic approaches.

  9. Effects of stochastic time-delayed feedback on a dynamical system modeling a chemical oscillator

    Science.gov (United States)

    González Ochoa, Héctor O.; Perales, Gualberto Solís; Epstein, Irving R.; Femat, Ricardo

    2018-05-01

    We examine how stochastic time-delayed negative feedback affects the dynamical behavior of a model oscillatory reaction. We apply constant and stochastic time-delayed negative feedbacks to a point Field-Körös-Noyes photosensitive oscillator and compare their effects. Negative feedback is applied in the form of simulated inhibitory electromagnetic radiation with an intensity proportional to the concentration of oxidized light-sensitive catalyst in the oscillator. We first characterize the system under nondelayed inhibitory feedback; then we explore and compare the effects of constant (deterministic) versus stochastic time-delayed feedback. We find that the oscillatory amplitude, frequency, and waveform are essentially preserved when low-dispersion stochastic delayed feedback is used, whereas small but measurable changes appear when a large dispersion is applied.

  10. Optimal timing of joint replacement using mathematical programming and stochastic programming models.

    Science.gov (United States)

    Keren, Baruch; Pliskin, Joseph S

    2011-12-01

    The optimal timing for performing radical medical procedures as joint (e.g., hip) replacement must be seriously considered. In this paper we show that under deterministic assumptions the optimal timing for joint replacement is a solution of a mathematical programming problem, and under stochastic assumptions the optimal timing can be formulated as a stochastic programming problem. We formulate deterministic and stochastic models that can serve as decision support tools. The results show that the benefit from joint replacement surgery is heavily dependent on timing. Moreover, for a special case where the patient's remaining life is normally distributed along with a normally distributed survival of the new joint, the expected benefit function from surgery is completely solved. This enables practitioners to draw the expected benefit graph, to find the optimal timing, to evaluate the benefit for each patient, to set priorities among patients and to decide if joint replacement should be performed and when.

  11. Neural network connectivity and response latency modelled by stochastic processes

    DEFF Research Database (Denmark)

    Tamborrino, Massimiliano

    is connected to thousands of other neurons. The rst question is: how to model neural networks through stochastic processes? A multivariate Ornstein-Uhlenbeck process, obtained as a diffusion approximation of a jump process, is the proposed answer. Obviously, dependencies between neurons imply dependencies......Stochastic processes and their rst passage times have been widely used to describe the membrane potential dynamics of single neurons and to reproduce neuronal spikes, respectively.However, cerebral cortex in human brains is estimated to contain 10-20 billions of neurons and each of them...... between their spike times. Therefore, the second question is: how to detect neural network connectivity from simultaneously recorded spike trains? Answering this question corresponds to investigate the joint distribution of sequences of rst passage times. A non-parametric method based on copulas...

  12. Stochastic Power Control for Time-Varying Long-Term Fading Wireless Networks

    Directory of Open Access Journals (Sweden)

    Charalambous Charalambos D

    2006-01-01

    Full Text Available A new time-varying (TV long-term fading (LTF channel model which captures both the space and time variations of wireless systems is developed. The proposed TV LTF model is based on a stochastic differential equation driven by Brownian motion. This model is more realistic than the static models usually encountered in the literature. It allows viewing the wireless channel as a dynamical system, thus enabling well-developed tools of adaptive and nonadaptive estimation and identification techniques to be applied to this class of problems. In contrast with the traditional models, the statistics of the proposed model are shown to be TV, but converge in steady state to their static counterparts. Moreover, optimal power control algorithms (PCAs based on the new model are proposed. A centralized PCA is shown to reduce to a simple linear programming problem if predictable power control strategies (PPCS are used. In addition, an iterative distributed stochastic PCA is used to solve for the optimization problem using stochastic approximations. The latter solely requires each mobile to know its received signal-to-interference ratio. Generalizations of the power control problem based on convex optimization techniques are provided if PPCS are not assumed. Numerical results show that there are potentially large gains to be achieved by using TV stochastic models, and the distributed stochastic PCA provides better power stability and consumption than the distributed deterministic PCA.

  13. Hopf Bifurcation Analysis for a Stochastic Discrete-Time Hyperchaotic System

    Directory of Open Access Journals (Sweden)

    Jie Ran

    2015-01-01

    Full Text Available The dynamics of a discrete-time hyperchaotic system and the amplitude control of Hopf bifurcation for a stochastic discrete-time hyperchaotic system are investigated in this paper. Numerical simulations are presented to exhibit the complex dynamical behaviors in the discrete-time hyperchaotic system. Furthermore, the stochastic discrete-time hyperchaotic system with random parameters is transformed into its equivalent deterministic system with the orthogonal polynomial theory of discrete random function. In addition, the dynamical features of the discrete-time hyperchaotic system with random disturbances are obtained through its equivalent deterministic system. By using the Hopf bifurcation conditions of the deterministic discrete-time system, the specific conditions for the existence of Hopf bifurcation in the equivalent deterministic system are derived. And the amplitude control with random intensity is discussed in detail. Finally, the feasibility of the control method is demonstrated by numerical simulations.

  14. Studies in the Control of Stochastic Systems

    Science.gov (United States)

    2017-10-31

    control of continuous time stochastic systems with noise that is Brownian motions or fractional Brownian motions, the control of discrete time...in both continuous and discrete time. All of the above types of problems have been studied with the support of this grant. The achievement of these...scientists and engineers. 2. Math Awareness Months (MAM) (Every April for the past twenty-three years) Agenda: workshops each year for fifth

  15. Stochastic Approach to Determine CO2 Hydrate Induction Time in Clay Mineral Suspensions

    Science.gov (United States)

    Lee, K.; Lee, S.; Lee, W.

    2008-12-01

    A large number of induction time data for carbon dioxide hydrate formation were obtained from a batch reactor consisting of four independent reaction cells. Using resistance temperature detector(RTD)s and a digital microscope, we successfully monitored the whole process of hydrate formation (i.e., nucleation and crystal growth) and detected the induction time. The experiments were carried out in kaolinite and montmorillonite suspensions at temperatures between 274 and 277 K and pressures ranging from 3.0 to 4.0 MPa. Each set of data was analyzed beforehand whether to be treated by stochastic manner or not. Geochemical factors potentially influencing the hydrate induction time under different experimental conditions were investigated by stochastic analyses. We observed that clay mineral type, pressure, and temperature significantly affect the stochastic behavior of the induction times for CO2 hydrate formation in this study. The hydrate formation kinetics along with stochastic analyses can provide basic understanding for CO2 hydrate storage in deep-sea sediment and geologic formation, securing its stability under the environments.

  16. Stochastic processes

    CERN Document Server

    Parzen, Emanuel

    1962-01-01

    Well-written and accessible, this classic introduction to stochastic processes and related mathematics is appropriate for advanced undergraduate students of mathematics with a knowledge of calculus and continuous probability theory. The treatment offers examples of the wide variety of empirical phenomena for which stochastic processes provide mathematical models, and it develops the methods of probability model-building.Chapter 1 presents precise definitions of the notions of a random variable and a stochastic process and introduces the Wiener and Poisson processes. Subsequent chapters examine

  17. Structure and Randomness of Continuous-Time, Discrete-Event Processes

    Science.gov (United States)

    Marzen, Sarah E.; Crutchfield, James P.

    2017-10-01

    Loosely speaking, the Shannon entropy rate is used to gauge a stochastic process' intrinsic randomness; the statistical complexity gives the cost of predicting the process. We calculate, for the first time, the entropy rate and statistical complexity of stochastic processes generated by finite unifilar hidden semi-Markov models—memoryful, state-dependent versions of renewal processes. Calculating these quantities requires introducing novel mathematical objects (ɛ -machines of hidden semi-Markov processes) and new information-theoretic methods to stochastic processes.

  18. Quantum mechanics, stochasticity and space-time

    International Nuclear Information System (INIS)

    Ramanathan, R.

    1986-04-01

    An extended and more rigorous version of a recent proposal for an objective stochastic formulation of quantum mechanics along with its extension to the relativistic case without spin is presented. The relativistic Klein-Gordon equation is shown to be a particular form of the relativistic Kolmogorov-Fokker-Planck equation which is derived from a covariant formulation of the Chapman-Kolmogorov condition. Complexification of probability amplitudes is again achieved only through a conformal rotation of Minkowski space-time M 4 . (author)

  19. Doubly stochastic Poisson process models for precipitation at fine time-scales

    Science.gov (United States)

    Ramesh, Nadarajah I.; Onof, Christian; Xie, Dichao

    2012-09-01

    This paper considers a class of stochastic point process models, based on doubly stochastic Poisson processes, in the modelling of rainfall. We examine the application of this class of models, a neglected alternative to the widely-known Poisson cluster models, in the analysis of fine time-scale rainfall intensity. These models are mainly used to analyse tipping-bucket raingauge data from a single site but an extension to multiple sites is illustrated which reveals the potential of this class of models to study the temporal and spatial variability of precipitation at fine time-scales.

  20. The interpolation method of stochastic functions and the stochastic variational principle

    International Nuclear Information System (INIS)

    Liu Xianbin; Chen Qiu

    1993-01-01

    -order stochastic finite element equations are not very reasonable. On the other hand, Galerkin Method is hopeful, along with the method, the projection principle had been advanced to solve the stochastic operator equations. In Galerkin Method, by means of projecting the stochastic solution functions into the subspace of the solution function space, the treatment of the stochasticity of the structural physical properties and the loads is reasonable. However, the construction or the selection of the subspace of the solution function space which is a Hilbert Space of stochastic functions is difficult, and furthermore it is short of a reasonable rule to measure whether the approximation of the subspace to the solution function space is fine or not. In stochastic finite element method, the discretization of stochastic functions in space and time shows a very importance, so far, the discrete patterns consist of Local Average Theory, Interpolation Method and Orthogonal Expansion Method. Although the Local Average Theory has already been a success in the stationary random fields, it is not suitable for the non-stationary ones as well. For the general stochastic functions, whether it is stationary or not, interpolation method is available. In the present paper, the authors have shown that the error between the true solution function and its approximation, its projection in the subspace, depends continuously on the errors between the stochastic functions and their interpolation functions, the latter rely continuously on the scales of the discrete elements; so a conclusion can be obtained that the Interpolation method of stochastic functions is convergent. That is to say that the approximation solution functions would limit to the true solution functions when the scales of the discrete elements goes smaller and smaller. Using the Interpolation method, a basis of subspace of the solution function space is constructed in this paper, and by means of combining the projection principle and

  1. Continuous-time random walks on networks with vertex- and time-dependent forcing.

    Science.gov (United States)

    Angstmann, C N; Donnelly, I C; Henry, B I; Langlands, T A M

    2013-08-01

    We have investigated the transport of particles moving as random walks on the vertices of a network, subject to vertex- and time-dependent forcing. We have derived the generalized master equations for this transport using continuous time random walks, characterized by jump and waiting time densities, as the underlying stochastic process. The forcing is incorporated through a vertex- and time-dependent bias in the jump densities governing the random walking particles. As a particular case, we consider particle forcing proportional to the concentration of particles on adjacent vertices, analogous to self-chemotactic attraction in a spatial continuum. Our algebraic and numerical studies of this system reveal an interesting pair-aggregation pattern formation in which the steady state is composed of a high concentration of particles on a small number of isolated pairs of adjacent vertices. The steady states do not exhibit this pair aggregation if the transport is random on the vertices, i.e., without forcing. The manifestation of pair aggregation on a transport network may thus be a signature of self-chemotactic-like forcing.

  2. Stochastic solution of population balance equations for reactor networks

    International Nuclear Information System (INIS)

    Menz, William J.; Akroyd, Jethro; Kraft, Markus

    2014-01-01

    This work presents a sequential modular approach to solve a generic network of reactors with a population balance model using a stochastic numerical method. Full-coupling to the gas-phase is achieved through operator-splitting. The convergence of the stochastic particle algorithm in test networks is evaluated as a function of network size, recycle fraction and numerical parameters. These test cases are used to identify methods through which systematic and statistical error may be reduced, including by use of stochastic weighted algorithms. The optimal algorithm was subsequently used to solve a one-dimensional example of silicon nanoparticle synthesis using a multivariate particle model. This example demonstrated the power of stochastic methods in resolving particle structure by investigating the transient and spatial evolution of primary polydispersity, degree of sintering and TEM-style images. Highlights: •An algorithm is presented to solve reactor networks with a population balance model. •A stochastic method is used to solve the population balance equations. •The convergence and efficiency of the reported algorithms are evaluated. •The algorithm is applied to simulate silicon nanoparticle synthesis in a 1D reactor. •Particle structure is reported as a function of reactor length and time

  3. Weather Derivatives and Stochastic Modelling of Temperature

    Directory of Open Access Journals (Sweden)

    Fred Espen Benth

    2011-01-01

    Full Text Available We propose a continuous-time autoregressive model for the temperature dynamics with volatility being the product of a seasonal function and a stochastic process. We use the Barndorff-Nielsen and Shephard model for the stochastic volatility. The proposed temperature dynamics is flexible enough to model temperature data accurately, and at the same time being analytically tractable. Futures prices for commonly traded contracts at the Chicago Mercantile Exchange on indices like cooling- and heating-degree days and cumulative average temperatures are computed, as well as option prices on them.

  4. Multivariable dynamic calculus on time scales

    CERN Document Server

    Bohner, Martin

    2016-01-01

    This book offers the reader an overview of recent developments of multivariable dynamic calculus on time scales, taking readers beyond the traditional calculus texts. Covering topics from parameter-dependent integrals to partial differentiation on time scales, the book’s nine pedagogically oriented chapters provide a pathway to this active area of research that will appeal to students and researchers in mathematics and the physical sciences. The authors present a clear and well-organized treatment of the concept behind the mathematics and solution techniques, including many practical examples and exercises.

  5. Geometric noise reduction for multivariate time series.

    Science.gov (United States)

    Mera, M Eugenia; Morán, Manuel

    2006-03-01

    We propose an algorithm for the reduction of observational noise in chaotic multivariate time series. The algorithm is based on a maximum likelihood criterion, and its goal is to reduce the mean distance of the points of the cleaned time series to the attractor. We give evidence of the convergence of the empirical measure associated with the cleaned time series to the underlying invariant measure, implying the possibility to predict the long run behavior of the true dynamics.

  6. A stochastic model for neutron simulation considering the spectrum and nuclear properties with continuous dependence of energy

    International Nuclear Information System (INIS)

    Camargo, Dayana Q. de; Bodmann, Bardo E.J.; Vilhena, Marco T. de; Froehlich, Herberth B.

    2011-01-01

    In this work we developed a stochastic model to simulate neutron transport in a heterogeneous environment, considering continuous neutron spectra and the nuclear properties with its continuous dependence on energy. This model was implemented using the Monte Carlo method for the propagation of neutrons in different environments. Due to restrictions with respect to the number of neutrons that can be simulated in reasonable computational time we introduced a variable control volume together with (pseudo-) periodic boundary conditions in order to overcome this problem. This study allowed a detailed analysis of the influence of energy on the neutron population and its impact on the life cycle of neutrons. From the results, even for a simple geometrical arrangement, we can conclude that there is need to consider the energy dependence and hence defined a spectral effective multiplication factor per Monte Carlo step. (author)

  7. Partial Finite-Time Synchronization of Switched Stochastic Chua's Circuits via Sliding-Mode Control

    Directory of Open Access Journals (Sweden)

    Zhang-Lin Wan

    2011-01-01

    Full Text Available This paper considers the problem of partial finite-time synchronization between switched stochastic Chua's circuits accompanied by a time-driven switching law. Based on the Ito formula and Lyapunov stability theory, a sliding-mode controller is developed to guarantee the synchronization of switched stochastic master-slave Chua's circuits and for the mean of error states to obtain the partial finite-time stability. Numerical simulations demonstrate the effectiveness of the proposed methods.

  8. Contribution to the stochastically studies of space-time dependable hydrological processes

    International Nuclear Information System (INIS)

    Kjaevski, Ivancho

    2002-12-01

    One of the fundaments of today's planning and water economy is Science of Hydrology. Science of Hydrology through the history had followed the development of the water management systems. Water management systems, during the time from single-approach evolved to complex and multi purpose systems. The dynamic and development of the today's society contributed for increasing the demand of clean water, and in the same time, the resources of clean water in the nature are reduced. In this kind of conditions, water management systems should resolve problems that are more complicated during managing of water sources. Solving the problems in water management, enable development and applying new methods and technologies in planning and management with water resources and water management systems like: systematical analyses, operational research, hierarchy decisions, expert systems, computer technology etc. Planning and management of water sources needs historical measured data for hydro metrological processes. In our country there are data of hydro metrological processes in period of 50-70, but in some Europe countries there are data more than 100 years. Water economy trends follow the hydro metrological trend research. The basic statistic techniques like sampling, probability distribution function, correlation and regression, are used about one intended and simple water management problems. Solving new problems about water management needs using of space-time stochastic technique, modem mathematical and statistical techniques during simulation and optimization of complex water systems. We need tree phases of development of the techniques to get secure hydrological models: i) Estimate the quality of hydro meteorological data, analyzing of their consistency, and homogeneous; ii) Structural analyze of hydro meteorological processes; iii) Mathematical models for modeling hydro meteorological processes. Very often, the third phase is applied for analyzing and modeling of hydro

  9. Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays

    Science.gov (United States)

    Syed Ali, M.; Balasubramaniam, P.

    2008-07-01

    In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB.

  10. Robust stability for uncertain stochastic fuzzy BAM neural networks with time-varying delays

    International Nuclear Information System (INIS)

    Syed Ali, M.; Balasubramaniam, P.

    2008-01-01

    In this Letter, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, we analyze the global asymptotic stability of uncertain stochastic fuzzy Bidirectional Associative Memory (BAM) neural networks with time-varying delays which are represented by the Takagi-Sugeno (TS) fuzzy models. A new class of uncertain stochastic fuzzy BAM neural networks with time varying delays has been studied and sufficient conditions have been derived to obtain conservative result in stochastic settings. The developed results are more general than those reported in the earlier literatures. In addition, the numerical examples are provided to illustrate the applicability of the result using LMI toolbox in MATLAB

  11. Fast and Flexible Multivariate Time Series Subsequence Search

    Data.gov (United States)

    National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...

  12. Robustness Analysis of Hybrid Stochastic Neural Networks with Neutral Terms and Time-Varying Delays

    Directory of Open Access Journals (Sweden)

    Chunmei Wu

    2015-01-01

    Full Text Available We analyze the robustness of global exponential stability of hybrid stochastic neural networks subject to neutral terms and time-varying delays simultaneously. Given globally exponentially stable hybrid stochastic neural networks, we characterize the upper bounds of contraction coefficients of neutral terms and time-varying delays by using the transcendental equation. Moreover, we prove theoretically that, for any globally exponentially stable hybrid stochastic neural networks, if additive neutral terms and time-varying delays are smaller than the upper bounds arrived, then the perturbed neural networks are guaranteed to also be globally exponentially stable. Finally, a numerical simulation example is given to illustrate the presented criteria.

  13. Estimating the decomposition of predictive information in multivariate systems

    Science.gov (United States)

    Faes, Luca; Kugiumtzis, Dimitris; Nollo, Giandomenico; Jurysta, Fabrice; Marinazzo, Daniele

    2015-03-01

    In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.

  14. Globally Asymptotic Stability of Stochastic Nonlinear Systems with Time-Varying Delays via Output Feedback Control

    Directory of Open Access Journals (Sweden)

    Mingzhu Song

    2016-01-01

    Full Text Available We address the problem of globally asymptotic stability for a class of stochastic nonlinear systems with time-varying delays. By the backstepping method and Lyapunov theory, we design a linear output feedback controller recursively based on the observable linearization for a class of stochastic nonlinear systems with time-varying delays to guarantee that the closed-loop system is globally asymptotically stable in probability. In particular, we extend the deterministic nonlinear system to stochastic nonlinear systems with time-varying delays. Finally, an example and its simulations are given to illustrate the theoretical results.

  15. The stochastic versus the Euclidean approach to quantum fields on a static space-time

    International Nuclear Information System (INIS)

    De Angelis, G.F.; de Falco, D.

    1986-01-01

    Equations are presented which modify the definition of the Gaussian field in the Rindler chart in order to make contact with the Wightman state, the Hartle-Hawking state, and the Euclidean field. By taking Ornstein-Uhlenbeck processes the authors have chosen, in the sense of stochastic mechanics, to place precisely the Fulling modes in their harmonic oscillator ground state. In this respect, together with the periodicity of Minkowski space-time, the authors observe that the covariance of the Ornstein-Uhlenbeck process can be obtained by analytical continuation of the Wightman function of the harmonic oscillator at zero temperature

  16. Sunspot Cycle Prediction Using Multivariate Regression and Binary ...

    Indian Academy of Sciences (India)

    49

    Multivariate regression model has been derived based on the available cycles 1 .... The flare index correlates well with various parameters of the solar activity. ...... 32) Sabarinath A and Anilkumar A K 2011 A stochastic prediction model for the.

  17. Stochastic quantization of geometrodynamic curved space-time

    International Nuclear Information System (INIS)

    Prugovecki, E.

    1981-01-01

    It is proposed that quantum rather than classical test particles be used in recent operational definitions of space-time. In the resulting quantum space-time the role of test particle trajectories is taken over by propagators. The introduced co-ordinate values are stochastic rather than deterministic, the afore-mentioned propagators providing probability amplitudes describing fluctuations of measured co-ordinates around their mean values. It is shown that, if a geometrodynamic point of view based on 3 + 1 foliations of space-time is adopted, self-consistent families of propagators for quantum test particles in free fall can be constructed. The resulting formalism for quantum space-time is outlined and the quantization of spatially flat Robertson-Walker space-times is provided as an illustration. (author)

  18. Empirical method to measure stochasticity and multifractality in nonlinear time series

    Science.gov (United States)

    Lin, Chih-Hao; Chang, Chia-Seng; Li, Sai-Ping

    2013-12-01

    An empirical algorithm is used here to study the stochastic and multifractal nature of nonlinear time series. A parameter can be defined to quantitatively measure the deviation of the time series from a Wiener process so that the stochasticity of different time series can be compared. The local volatility of the time series under study can be constructed using this algorithm, and the multifractal structure of the time series can be analyzed by using this local volatility. As an example, we employ this method to analyze financial time series from different stock markets. The result shows that while developed markets evolve very much like an Ito process, the emergent markets are far from efficient. Differences about the multifractal structures and leverage effects between developed and emergent markets are discussed. The algorithm used here can be applied in a similar fashion to study time series of other complex systems.

  19. Continuous-Time Random Walk with multi-step memory: an application to market dynamics

    Science.gov (United States)

    Gubiec, Tomasz; Kutner, Ryszard

    2017-11-01

    An extended version of the Continuous-Time Random Walk (CTRW) model with memory is herein developed. This memory involves the dependence between arbitrary number of successive jumps of the process while waiting times between jumps are considered as i.i.d. random variables. This dependence was established analyzing empirical histograms for the stochastic process of a single share price on a market within the high frequency time scale. Then, it was justified theoretically by considering bid-ask bounce mechanism containing some delay characteristic for any double-auction market. Our model appeared exactly analytically solvable. Therefore, it enables a direct comparison of its predictions with their empirical counterparts, for instance, with empirical velocity autocorrelation function. Thus, the present research significantly extends capabilities of the CTRW formalism. Contribution to the Topical Issue "Continuous Time Random Walk Still Trendy: Fifty-year History, Current State and Outlook", edited by Ryszard Kutner and Jaume Masoliver.

  20. Monte Carlo Tree Search for Continuous and Stochastic Sequential Decision Making Problems

    International Nuclear Information System (INIS)

    Couetoux, Adrien

    2013-01-01

    In this thesis, I studied sequential decision making problems, with a focus on the unit commitment problem. Traditionally solved by dynamic programming methods, this problem is still a challenge, due to its high dimension and to the sacrifices made on the accuracy of the model to apply state of the art methods. I investigated on the applicability of Monte Carlo Tree Search methods for this problem, and other problems that are single player, stochastic and continuous sequential decision making problems. In doing so, I obtained a consistent and anytime algorithm, that can easily be combined with existing strong heuristic solvers. (author)

  1. Space-time relationship in continuously moving table method for large FOV peripheral contrast-enhanced magnetic resonance angiography

    International Nuclear Information System (INIS)

    Sabati, M; Lauzon, M L; Frayne, R

    2003-01-01

    Data acquisition using a continuously moving table approach is a method capable of generating large field-of-view (FOV) 3D MR angiograms. However, in order to obtain venous contamination-free contrast-enhanced (CE) MR angiograms in the lower limbs, one of the major challenges is to acquire all necessary k-space data during the restricted arterial phase of the contrast agent. Preliminary investigation on the space-time relationship of continuously acquired peripheral angiography is performed in this work. Deterministic and stochastic undersampled hybrid-space (x, k y , k z ) acquisitions are simulated for large FOV peripheral runoff studies. Initial results show the possibility of acquiring isotropic large FOV images of the entire peripheral vascular system. An optimal trade-off between the spatial and temporal sampling properties was found that produced a high-spatial resolution peripheral CE-MR angiogram. The deterministic sampling pattern was capable of reconstructing the global structure of the peripheral arterial tree and showed slightly better global quantitative results than stochastic patterns. Optimal stochastic sampling patterns, on the other hand, enhanced small vessels and had more favourable local quantitative results. These simulations demonstrate the complex spatial-temporal relationship when sampling large FOV peripheral runoff studies. They also suggest that more investigation is required to maximize image quality as a function of hybrid-space coverage, acquisition repetition time and sampling pattern parameters

  2. Stochastic analysis of epidemics on adaptive time varying networks

    Science.gov (United States)

    Kotnis, Bhushan; Kuri, Joy

    2013-06-01

    Many studies investigating the effect of human social connectivity structures (networks) and human behavioral adaptations on the spread of infectious diseases have assumed either a static connectivity structure or a network which adapts itself in response to the epidemic (adaptive networks). However, human social connections are inherently dynamic or time varying. Furthermore, the spread of many infectious diseases occur on a time scale comparable to the time scale of the evolving network structure. Here we aim to quantify the effect of human behavioral adaptations on the spread of asymptomatic infectious diseases on time varying networks. We perform a full stochastic analysis using a continuous time Markov chain approach for calculating the outbreak probability, mean epidemic duration, epidemic reemergence probability, etc. Additionally, we use mean-field theory for calculating epidemic thresholds. Theoretical predictions are verified using extensive simulations. Our studies have uncovered the existence of an “adaptive threshold,” i.e., when the ratio of susceptibility (or infectivity) rate to recovery rate is below the threshold value, adaptive behavior can prevent the epidemic. However, if it is above the threshold, no amount of behavioral adaptations can prevent the epidemic. Our analyses suggest that the interaction patterns of the infected population play a major role in sustaining the epidemic. Our results have implications on epidemic containment policies, as awareness campaigns and human behavioral responses can be effective only if the interaction levels of the infected populace are kept in check.

  3. Stochastic resonance driven by time-modulated correlated coloured noise sources in a single-mode laser

    International Nuclear Information System (INIS)

    De-Yi, Chen; Li, Zhang

    2009-01-01

    This paper investigates the phenomenon of stochastic resonance in a single-mode laser driven by time-modulated correlated coloured noise sources. The power spectrum and signal-to-noise ratio R of the laser intensity are calculated by the linear approximation. The effects caused by noise self-correlation time τ 1 , τ 2 and cross-correlated time τ 3 for stochastic resonance are analysed in two ways: τ 1 , τ 2 and τ 3 are taken to be the independent variables and the parameters respectively. The effects of the gain coefficient Γ and loss coefficient K on the stochastic resonance are also discussed. It is found that besides the presence of the standard form and the broad sense of stochastic resonance, the number of extrema in the curve of R versus K is reduced with the increase of the gain coefficient Γ

  4. A stochastic programming approach to manufacturing flow control

    OpenAIRE

    Haurie, Alain; Moresino, Francesco

    2012-01-01

    This paper proposes and tests an approximation of the solution of a class of piecewise deterministic control problems, typically used in the modeling of manufacturing flow processes. This approximation uses a stochastic programming approach on a suitably discretized and sampled system. The method proceeds through two stages: (i) the Hamilton-Jacobi-Bellman (HJB) dynamic programming equations for the finite horizon continuous time stochastic control problem are discretized over a set of sample...

  5. A Range-Based Multivariate Model for Exchange Rate Volatility

    OpenAIRE

    Tims, Ben; Mahieu, Ronald

    2003-01-01

    textabstractIn this paper we present a parsimonious multivariate model for exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates. The multivariate stochastic volatility model divides the log range of each exchange rate into two independent latent factors, which are interpreted as the underlying currency specific components. Due to the normality of logarithmic volatilities the model can be estimated conveniently with standard Kalman filter techniques. Our resu...

  6. Global synchronization of general delayed complex networks with stochastic disturbances

    International Nuclear Information System (INIS)

    Tu Li-Lan

    2011-01-01

    In this paper, global synchronization of general delayed complex networks with stochastic disturbances, which is a zero-mean real scalar Wiener process, is investigated. The networks under consideration are continuous-time networks with time-varying delay. Based on the stochastic Lyapunov stability theory, Ito's differential rule and the linear matrix inequality (LMI) optimization technique, several delay-dependent synchronous criteria are established, which guarantee the asymptotical mean-square synchronization of drive networks and response networks with stochastic disturbances. The criteria are expressed in terms of LMI, which can be easily solved using the Matlab LMI Control Toolbox. Finally, two examples show the effectiveness and feasibility of the proposed synchronous conditions. (general)

  7. Continuity in a pathwise sense with respect to the coefficients of solutions of stochastic differential equations

    DEFF Research Database (Denmark)

    Knudsen, Thomas Skov

    1997-01-01

    For stochastic differential equations (SDEs) of the form dX(t) = b(X)(t)) dt + sigma(X(t))dW(t) where b and sigma are Lipschitz continuous, it is shown that if we consider a fixed sigma is an element of C-5, bounded and with bounded derivatives, the random field of solutions is pathwise locally...

  8. A Multivariate Time Series Method for Monte Carlo Reactor Analysis

    International Nuclear Information System (INIS)

    Taro Ueki

    2008-01-01

    A robust multivariate time series method has been established for the Monte Carlo calculation of neutron multiplication problems. The method is termed Coarse Mesh Projection Method (CMPM) and can be implemented using the coarse statistical bins for acquisition of nuclear fission source data. A novel aspect of CMPM is the combination of the general technical principle of projection pursuit in the signal processing discipline and the neutron multiplication eigenvalue problem in the nuclear engineering discipline. CMPM enables reactor physicists to accurately evaluate major eigenvalue separations of nuclear reactors with continuous energy Monte Carlo calculation. CMPM was incorporated in the MCNP Monte Carlo particle transport code of Los Alamos National Laboratory. The great advantage of CMPM over the traditional Fission Matrix method is demonstrated for the three space-dimensional modeling of the initial core of a pressurized water reactor

  9. GillespieSSA: Implementing the Gillespie Stochastic Simulation Algorithm in R

    Directory of Open Access Journals (Sweden)

    Mario Pineda-Krch

    2008-02-01

    Full Text Available The deterministic dynamics of populations in continuous time are traditionally described using coupled, first-order ordinary differential equations. While this approach is accurate for large systems, it is often inadequate for small systems where key species may be present in small numbers or where key reactions occur at a low rate. The Gillespie stochastic simulation algorithm (SSA is a procedure for generating time-evolution trajectories of finite populations in continuous time and has become the standard algorithm for these types of stochastic models. This article presents a simple-to-use and flexible framework for implementing the SSA using the high-level statistical computing language R and the package GillespieSSA. Using three ecological models as examples (logistic growth, Rosenzweig-MacArthur predator-prey model, and Kermack-McKendrick SIRS metapopulation model, this paper shows how a deterministic model can be formulated as a finite-population stochastic model within the framework of SSA theory and how it can be implemented in R. Simulations of the stochastic models are performed using four different SSA Monte Carlo methods: one exact method (Gillespie's direct method; and three approximate methods (explicit, binomial, and optimized tau-leap methods. Comparison of simulation results confirms that while the time-evolution trajectories obtained from the different SSA methods are indistinguishable, the approximate methods are up to four orders of magnitude faster than the exact methods.

  10. Stationary stochastic processes theory and applications

    CERN Document Server

    Lindgren, Georg

    2012-01-01

    Some Probability and Process BackgroundSample space, sample function, and observablesRandom variables and stochastic processesStationary processes and fieldsGaussian processesFour historical landmarksSample Function PropertiesQuadratic mean propertiesSample function continuityDerivatives, tangents, and other characteristicsStochastic integrationAn ergodic resultExercisesSpectral RepresentationsComplex-valued stochastic processesBochner's theorem and the spectral distributionSpectral representation of a stationary processGaussian processesStationary counting processesExercisesLinear Filters - General PropertiesLinear time invariant filtersLinear filters and differential equationsWhite noise in linear systemsLong range dependence, non-integrable spectra, and unstable systemsThe ARMA-familyLinear Filters - Special TopicsThe Hilbert transform and the envelopeThe sampling theoremKarhunen-Loève expansionClassical Ergodic Theory and MixingThe basic ergodic theorem in L2Stationarity and transformationsThe ergodic th...

  11. Higher-order stochastic differential equations and the positive Wigner function

    Science.gov (United States)

    Drummond, P. D.

    2017-12-01

    General higher-order stochastic processes that correspond to any diffusion-type tensor of higher than second order are obtained. The relationship of multivariate higher-order stochastic differential equations with tensor decomposition theory and tensor rank is explained. Techniques for generating the requisite complex higher-order noise are proved to exist either using polar coordinates and γ distributions, or from products of Gaussian variates. This method is shown to allow the calculation of the dynamics of the Wigner function, after it is extended to a complex phase space. The results are illustrated physically through dynamical calculations of the positive Wigner distribution for three-mode parametric downconversion, widely used in quantum optics. The approach eliminates paradoxes arising from truncation of the higher derivative terms in Wigner function time evolution. Anomalous results of negative populations and vacuum scattering found in truncated Wigner quantum simulations in quantum optics and Bose-Einstein condensate dynamics are shown not to occur with this type of stochastic theory.

  12. On orthogonality preserving quadratic stochastic operators

    Energy Technology Data Exchange (ETDEWEB)

    Mukhamedov, Farrukh; Taha, Muhammad Hafizuddin Mohd [Department of Computational and Theoretical Sciences, Faculty of Science International Islamic University Malaysia, P.O. Box 141, 25710 Kuantan, Pahang Malaysia (Malaysia)

    2015-05-15

    A quadratic stochastic operator (in short QSO) is usually used to present the time evolution of differing species in biology. Some quadratic stochastic operators have been studied by Lotka and Volterra. In the present paper, we first give a simple characterization of Volterra QSO in terms of absolutely continuity of discrete measures. Further, we introduce a notion of orthogonal preserving QSO, and describe such kind of operators defined on two dimensional simplex. It turns out that orthogonal preserving QSOs are permutations of Volterra QSO. The associativity of genetic algebras generated by orthogonal preserving QSO is studied too.

  13. On orthogonality preserving quadratic stochastic operators

    International Nuclear Information System (INIS)

    Mukhamedov, Farrukh; Taha, Muhammad Hafizuddin Mohd

    2015-01-01

    A quadratic stochastic operator (in short QSO) is usually used to present the time evolution of differing species in biology. Some quadratic stochastic operators have been studied by Lotka and Volterra. In the present paper, we first give a simple characterization of Volterra QSO in terms of absolutely continuity of discrete measures. Further, we introduce a notion of orthogonal preserving QSO, and describe such kind of operators defined on two dimensional simplex. It turns out that orthogonal preserving QSOs are permutations of Volterra QSO. The associativity of genetic algebras generated by orthogonal preserving QSO is studied too

  14. Some classes of multivariate infinitely divisible distributions admitting stochastic integral representations

    DEFF Research Database (Denmark)

    Barndorff-Nielsen, Ole Eiler; Maejima, M.; Sato, K.

    2006-01-01

    The class of distributions on R generated by convolutions of Γ-distributions and the class generated by convolutions of mixtures of exponential distributions are generalized to higher dimensions and denoted by T(Rd) and B(Rd) . From the Lévy process {Xt(μ)} on Rd with distribution μ at t=1, Υ...... divisible distributions and of self-decomposable distributions on Rd , respectively. The relations with the mapping Φ from μ to the distribution at each time of the stationary process of Ornstein-Uhlenbeck type with background driving Lévy process {Xt(μ)} are studied. Developments of these results......(μ) is defined as the distribution of the stochastic integral ∫01log(1/t)dXt(μ) . This mapping is a generalization of the mapping Υ introduced by Barndorff-Nielsen and Thorbjørnsen in one dimension. It is proved that ϒ(ID(Rd))=B(Rd) and ϒ(L(Rd))=T(Rd) , where ID(Rd) and L(Rd) are the classes of infinitely...

  15. A Stochastic Hybrid Systems framework for analysis of Markov reward models

    International Nuclear Information System (INIS)

    Dhople, S.V.; DeVille, L.; Domínguez-García, A.D.

    2014-01-01

    In this paper, we propose a framework to analyze Markov reward models, which are commonly used in system performability analysis. The framework builds on a set of analytical tools developed for a class of stochastic processes referred to as Stochastic Hybrid Systems (SHS). The state space of an SHS is comprised of: (i) a discrete state that describes the possible configurations/modes that a system can adopt, which includes the nominal (non-faulty) operational mode, but also those operational modes that arise due to component faults, and (ii) a continuous state that describes the reward. Discrete state transitions are stochastic, and governed by transition rates that are (in general) a function of time and the value of the continuous state. The evolution of the continuous state is described by a stochastic differential equation and reward measures are defined as functions of the continuous state. Additionally, each transition is associated with a reset map that defines the mapping between the pre- and post-transition values of the discrete and continuous states; these mappings enable the definition of impulses and losses in the reward. The proposed SHS-based framework unifies the analysis of a variety of previously studied reward models. We illustrate the application of the framework to performability analysis via analytical and numerical examples

  16. Analytical Solutions for Multi-Time Scale Fractional Stochastic Differential Equations Driven by Fractional Brownian Motion and Their Applications

    OpenAIRE

    Xiao-Li Ding; Juan J. Nieto

    2018-01-01

    In this paper, we investigate analytical solutions of multi-time scale fractional stochastic differential equations driven by fractional Brownian motions. We firstly decompose homogeneous multi-time scale fractional stochastic differential equations driven by fractional Brownian motions into independent differential subequations, and give their analytical solutions. Then, we use the variation of constant parameters to obtain the solutions of nonhomogeneous multi-time scale fractional stochast...

  17. Training and evaluation of neural networks for multi-variate time series processing

    DEFF Research Database (Denmark)

    Fog, Torben L.; Larsen, Jan; Hansen, Lars Kai

    1995-01-01

    We study the training and generalization for multi-variate time series processing. It is suggested to used a quasi-maximum likelihood approach rather than the standard sum of squared errors, thus taking dependencies among the errors of the individual time series into account. This may lead...... to improved generalization performance. Further, we extend the optimal brain damage pruning technique to the multi-variate case. A key ingredient is an algebraic expression for the generalization ability of a multi-variate model. The variability of the suggested techniques are successfully demonstrated...

  18. Detectability of Granger causality for subsampled continuous-time neurophysiological processes.

    Science.gov (United States)

    Barnett, Lionel; Seth, Anil K

    2017-01-01

    Granger causality is well established within the neurosciences for inference of directed functional connectivity from neurophysiological data. These data usually consist of time series which subsample a continuous-time biophysiological process. While it is well known that subsampling can lead to imputation of spurious causal connections where none exist, less is known about the effects of subsampling on the ability to reliably detect causal connections which do exist. We present a theoretical analysis of the effects of subsampling on Granger-causal inference. Neurophysiological processes typically feature signal propagation delays on multiple time scales; accordingly, we base our analysis on a distributed-lag, continuous-time stochastic model, and consider Granger causality in continuous time at finite prediction horizons. Via exact analytical solutions, we identify relationships among sampling frequency, underlying causal time scales and detectability of causalities. We reveal complex interactions between the time scale(s) of neural signal propagation and sampling frequency. We demonstrate that detectability decays exponentially as the sample time interval increases beyond causal delay times, identify detectability "black spots" and "sweet spots", and show that downsampling may potentially improve detectability. We also demonstrate that the invariance of Granger causality under causal, invertible filtering fails at finite prediction horizons, with particular implications for inference of Granger causality from fMRI data. Our analysis emphasises that sampling rates for causal analysis of neurophysiological time series should be informed by domain-specific time scales, and that state-space modelling should be preferred to purely autoregressive modelling. On the basis of a very general model that captures the structure of neurophysiological processes, we are able to help identify confounds, and offer practical insights, for successful detection of causal connectivity

  19. Characteristic functions of scale mixtures of multivariate skew-normal distributions

    KAUST Repository

    Kim, Hyoung-Moon; Genton, Marc G.

    2011-01-01

    We obtain the characteristic function of scale mixtures of skew-normal distributions both in the univariate and multivariate cases. The derivation uses the simple stochastic relationship between skew-normal distributions and scale mixtures of skew

  20. multivariate time series modeling of selected childhood diseases

    African Journals Online (AJOL)

    2016-06-17

    Jun 17, 2016 ... KEYWORDS: Multivariate Approach, Pre-whitening, Vector Time Series, .... Alternatively, the process may be written in mean adjusted form as .... The AIC criterion asymptotically over estimates the order with positive probability, whereas the BIC and HQC criteria ... has the same asymptotic distribution as Ǫ.

  1. Reduced linear noise approximation for biochemical reaction networks with time-scale separation: The stochastic tQSSA+

    Science.gov (United States)

    Herath, Narmada; Del Vecchio, Domitilla

    2018-03-01

    Biochemical reaction networks often involve reactions that take place on different time scales, giving rise to "slow" and "fast" system variables. This property is widely used in the analysis of systems to obtain dynamical models with reduced dimensions. In this paper, we consider stochastic dynamics of biochemical reaction networks modeled using the Linear Noise Approximation (LNA). Under time-scale separation conditions, we obtain a reduced-order LNA that approximates both the slow and fast variables in the system. We mathematically prove that the first and second moments of this reduced-order model converge to those of the full system as the time-scale separation becomes large. These mathematical results, in particular, provide a rigorous justification to the accuracy of LNA models derived using the stochastic total quasi-steady state approximation (tQSSA). Since, in contrast to the stochastic tQSSA, our reduced-order model also provides approximations for the fast variable stochastic properties, we term our method the "stochastic tQSSA+". Finally, we demonstrate the application of our approach on two biochemical network motifs found in gene-regulatory and signal transduction networks.

  2. Modeling stochastic frontier based on vine copulas

    Science.gov (United States)

    Constantino, Michel; Candido, Osvaldo; Tabak, Benjamin M.; da Costa, Reginaldo Brito

    2017-11-01

    This article models a production function and analyzes the technical efficiency of listed companies in the United States, Germany and England between 2005 and 2012 based on the vine copula approach. Traditional estimates of the stochastic frontier assume that data is multivariate normally distributed and there is no source of asymmetry. The proposed method based on vine copulas allow us to explore different types of asymmetry and multivariate distribution. Using data on product, capital and labor, we measure the relative efficiency of the vine production function and estimate the coefficient used in the stochastic frontier literature for comparison purposes. This production vine copula predicts the value added by firms with given capital and labor in a probabilistic way. It thereby stands in sharp contrast to the production function, where the output of firms is completely deterministic. The results show that, on average, S&P500 companies are more efficient than companies listed in England and Germany, which presented similar average efficiency coefficients. For comparative purposes, the traditional stochastic frontier was estimated and the results showed discrepancies between the coefficients obtained by the application of the two methods, traditional and frontier-vine, opening new paths of non-linear research.

  3. A Compositional Semantics for Stochastic Reo Connectors

    Directory of Open Access Journals (Sweden)

    Young-Joo Moon

    2010-07-01

    Full Text Available In this paper we present a compositional semantics for the channel-based coordination language Reo which enables the analysis of quality of service (QoS properties of service compositions. For this purpose, we annotate Reo channels with stochastic delay rates and explicitly model data-arrival rates at the boundary of a connector, to capture its interaction with the services that comprise its environment. We propose Stochastic Reo automata as an extension of Reo automata, in order to compositionally derive a QoS-aware semantics for Reo. We further present a translation of Stochastic Reo automata to Continuous-Time Markov Chains (CTMCs. This translation enables us to use third-party CTMC verification tools to do an end-to-end performance analysis of service compositions.

  4. Bridging time scales in cellular decision making with a stochastic bistable switch

    Directory of Open Access Journals (Sweden)

    Waldherr Steffen

    2010-08-01

    Full Text Available Abstract Background Cellular transformations which involve a significant phenotypical change of the cell's state use bistable biochemical switches as underlying decision systems. Some of these transformations act over a very long time scale on the cell population level, up to the entire lifespan of the organism. Results In this work, we aim at linking cellular decisions taking place on a time scale of years to decades with the biochemical dynamics in signal transduction and gene regulation, occuring on a time scale of minutes to hours. We show that a stochastic bistable switch forms a viable biochemical mechanism to implement decision processes on long time scales. As a case study, the mechanism is applied to model the initiation of follicle growth in mammalian ovaries, where the physiological time scale of follicle pool depletion is on the order of the organism's lifespan. We construct a simple mathematical model for this process based on experimental evidence for the involved genetic mechanisms. Conclusions Despite the underlying stochasticity, the proposed mechanism turns out to yield reliable behavior in large populations of cells subject to the considered decision process. Our model explains how the physiological time constant may emerge from the intrinsic stochasticity of the underlying gene regulatory network. Apart from ovarian follicles, the proposed mechanism may also be of relevance for other physiological systems where cells take binary decisions over a long time scale.

  5. Stochastic conditional intensity processes

    DEFF Research Database (Denmark)

    Bauwens, Luc; Hautsch, Nikolaus

    2006-01-01

    model allows for a wide range of (cross-)autocorrelation structures in multivariate point processes. The model is estimated by simulated maximum likelihood (SML) using the efficient importance sampling (EIS) technique. By modeling price intensities based on NYSE trading, we provide significant evidence......In this article, we introduce the so-called stochastic conditional intensity (SCI) model by extending Russell’s (1999) autoregressive conditional intensity (ACI) model by a latent common dynamic factor that jointly drives the individual intensity components. We show by simulations that the proposed...... for a joint latent factor and show that its inclusion allows for an improved and more parsimonious specification of the multivariate intensity process...

  6. Eco-reliable path finding in time-variant and stochastic networks

    International Nuclear Information System (INIS)

    Li, Wenjie; Yang, Lixing; Wang, Li; Zhou, Xuesong; Liu, Ronghui; Gao, Ziyou

    2017-01-01

    This paper addresses a route guidance problem for finding the most eco-reliable path in time-variant and stochastic networks such that travelers can arrive at the destination with the maximum on-time probability while meeting vehicle emission standards imposed by government regulators. To characterize the dynamics and randomness of transportation networks, the link travel times and emissions are assumed to be time-variant random variables correlated over the entire network. A 0–1 integer mathematical programming model is formulated to minimize the probability of late arrival by simultaneously considering the least expected emission constraint. Using the Lagrangian relaxation approach, the primal model is relaxed into a dualized model which is further decomposed into two simple sub-problems. A sub-gradient method is developed to reduce gaps between upper and lower bounds. Three sets of numerical experiments are tested to demonstrate the efficiency and performance of our proposed model and algorithm. - Highlights: • The most eco-reliable path is defined in time-variant and stochastic networks. • The model is developed with on-time arrival probability and emission constraints. • The sub-gradient and label correcting algorithm are integrated to solve the model. • Numerical experiments demonstrate the effectiveness of developed approaches.

  7. Nonlinear stochastic dynamics of mesoscopic homogeneous biochemical reaction systems—an analytical theory

    International Nuclear Information System (INIS)

    Qian, Hong

    2011-01-01

    The nonlinear dynamics of biochemical reactions in a small-sized system on the order of a cell are stochastic. Assuming spatial homogeneity, the populations of n molecular species follow a multi-dimensional birth-and-death process on Z n . We introduce the Delbrück–Gillespie process, a continuous-time Markov jump process, whose Kolmogorov forward equation has been known as the chemical master equation, and whose stochastic trajectories can be computed via the Gillespie algorithm. Using simple models, we illustrate that a system of nonlinear ordinary differential equations on R n emerges in the infinite system size limit. For finite system size, transitions among multiple attractors of the nonlinear dynamical system are rare events with exponentially long transit times. There is a separation of time scales between the deterministic ODEs and the stochastic Markov jumps between attractors. No diffusion process can provide a global representation that is accurate on both short and long time scales for the nonlinear, stochastic population dynamics. On the short time scale and near deterministic stable fixed points, Ornstein–Uhlenbeck Gaussian processes give linear stochastic dynamics that exhibit time-irreversible circular motion for open, driven chemical systems. Extending this individual stochastic behaviour-based nonlinear population theory of molecular species to other biological systems is discussed. (invited article)

  8. Stochastic Games for Continuous-Time Jump Processes Under Finite-Horizon Payoff Criterion

    Energy Technology Data Exchange (ETDEWEB)

    Wei, Qingda, E-mail: weiqd@hqu.edu.cn [Huaqiao University, School of Economics and Finance (China); Chen, Xian, E-mail: chenxian@amss.ac.cn [Peking University, School of Mathematical Sciences (China)

    2016-10-15

    In this paper we study two-person nonzero-sum games for continuous-time jump processes with the randomized history-dependent strategies under the finite-horizon payoff criterion. The state space is countable, and the transition rates and payoff functions are allowed to be unbounded from above and from below. Under the suitable conditions, we introduce a new topology for the set of all randomized Markov multi-strategies and establish its compactness and metrizability. Then by constructing the approximating sequences of the transition rates and payoff functions, we show that the optimal value function for each player is a unique solution to the corresponding optimality equation and obtain the existence of a randomized Markov Nash equilibrium. Furthermore, we illustrate the applications of our main results with a controlled birth and death system.

  9. Stochastic Games for Continuous-Time Jump Processes Under Finite-Horizon Payoff Criterion

    International Nuclear Information System (INIS)

    Wei, Qingda; Chen, Xian

    2016-01-01

    In this paper we study two-person nonzero-sum games for continuous-time jump processes with the randomized history-dependent strategies under the finite-horizon payoff criterion. The state space is countable, and the transition rates and payoff functions are allowed to be unbounded from above and from below. Under the suitable conditions, we introduce a new topology for the set of all randomized Markov multi-strategies and establish its compactness and metrizability. Then by constructing the approximating sequences of the transition rates and payoff functions, we show that the optimal value function for each player is a unique solution to the corresponding optimality equation and obtain the existence of a randomized Markov Nash equilibrium. Furthermore, we illustrate the applications of our main results with a controlled birth and death system.

  10. Limits for Stochastic Reaction Networks

    DEFF Research Database (Denmark)

    Cappelletti, Daniele

    Reaction systems have been introduced in the 70s to model biochemical systems. Nowadays their range of applications has increased and they are fruitfully used in dierent elds. The concept is simple: some chemical species react, the set of chemical reactions form a graph and a rate function...... is associated with each reaction. Such functions describe the speed of the dierent reactions, or their propensities. Two modelling regimes are then available: the evolution of the dierent species concentrations can be deterministically modelled through a system of ODE, while the counts of the dierent species...... at a certain time are stochastically modelled by means of a continuous-time Markov chain. Our work concerns primarily stochastic reaction systems, and their asymptotic properties. In Paper I, we consider a reaction system with intermediate species, i.e. species that are produced and fast degraded along a path...

  11. Exponential stability result for discrete-time stochastic fuzzy uncertain neural networks

    International Nuclear Information System (INIS)

    Mathiyalagan, K.; Sakthivel, R.; Marshal Anthoni, S.

    2012-01-01

    This Letter addresses the stability analysis problem for a class of uncertain discrete-time stochastic fuzzy neural networks (DSFNNs) with time-varying delays. By constructing a new Lyapunov–Krasovskii functional combined with the free weighting matrix technique, a new set of delay-dependent sufficient conditions for the robust exponential stability of the considered DSFNNs is established in terms of Linear Matrix Inequalities (LMIs). Finally, numerical examples with simulation results are provided to illustrate the applicability and usefulness of the obtained theory. -- Highlights: ► Applications of neural networks require the knowledge of dynamic behaviors. ► Exponential stability of discrete-time stochastic fuzzy neural networks is studied. ► Linear matrix inequality optimization approach is used to obtain the result. ► Delay-dependent stability criterion is established in terms of LMIs. ► Examples with simulation are provided to show the effectiveness of the result.

  12. Fast state estimation subject to random data loss in discrete-time nonlinear stochastic systems

    Science.gov (United States)

    Mahdi Alavi, S. M.; Saif, Mehrdad

    2013-12-01

    This paper focuses on the design of the standard observer in discrete-time nonlinear stochastic systems subject to random data loss. By the assumption that the system response is incrementally bounded, two sufficient conditions are subsequently derived that guarantee exponential mean-square stability and fast convergence of the estimation error for the problem at hand. An efficient algorithm is also presented to obtain the observer gain. Finally, the proposed methodology is employed for monitoring the Continuous Stirred Tank Reactor (CSTR) via a wireless communication network. The effectiveness of the designed observer is extensively assessed by using an experimental tested-bed that has been fabricated for performance evaluation of the over wireless-network estimation techniques under realistic radio channel conditions.

  13. Extreme-Strike and Small-time Asymptotics for Gaussian Stochastic Volatility Models

    OpenAIRE

    Zhang, Xin

    2016-01-01

    Asymptotic behavior of implied volatility is of our interest in this dissertation. For extreme strike, we consider a stochastic volatility asset price model in which the volatility is the absolute value of a continuous Gaussian process with arbitrary prescribed mean and covariance. By exhibiting a Karhunen-Loève expansion for the integrated variance, and using sharp estimates of the density of a general second-chaos variable, we derive asymptotics for the asset price density for large or smal...

  14. A mathematical approach for evaluating Markov models in continuous time without discrete-event simulation.

    Science.gov (United States)

    van Rosmalen, Joost; Toy, Mehlika; O'Mahony, James F

    2013-08-01

    Markov models are a simple and powerful tool for analyzing the health and economic effects of health care interventions. These models are usually evaluated in discrete time using cohort analysis. The use of discrete time assumes that changes in health states occur only at the end of a cycle period. Discrete-time Markov models only approximate the process of disease progression, as clinical events typically occur in continuous time. The approximation can yield biased cost-effectiveness estimates for Markov models with long cycle periods and if no half-cycle correction is made. The purpose of this article is to present an overview of methods for evaluating Markov models in continuous time. These methods use mathematical results from stochastic process theory and control theory. The methods are illustrated using an applied example on the cost-effectiveness of antiviral therapy for chronic hepatitis B. The main result is a mathematical solution for the expected time spent in each state in a continuous-time Markov model. It is shown how this solution can account for age-dependent transition rates and discounting of costs and health effects, and how the concept of tunnel states can be used to account for transition rates that depend on the time spent in a state. The applied example shows that the continuous-time model yields more accurate results than the discrete-time model but does not require much computation time and is easily implemented. In conclusion, continuous-time Markov models are a feasible alternative to cohort analysis and can offer several theoretical and practical advantages.

  15. Forecasting financial asset processes: stochastic dynamics via learning neural networks.

    Science.gov (United States)

    Giebel, S; Rainer, M

    2010-01-01

    Models for financial asset dynamics usually take into account their inherent unpredictable nature by including a suitable stochastic component into their process. Unknown (forward) values of financial assets (at a given time in the future) are usually estimated as expectations of the stochastic asset under a suitable risk-neutral measure. This estimation requires the stochastic model to be calibrated to some history of sufficient length in the past. Apart from inherent limitations, due to the stochastic nature of the process, the predictive power is also limited by the simplifying assumptions of the common calibration methods, such as maximum likelihood estimation and regression methods, performed often without weights on the historic time series, or with static weights only. Here we propose a novel method of "intelligent" calibration, using learning neural networks in order to dynamically adapt the parameters of the stochastic model. Hence we have a stochastic process with time dependent parameters, the dynamics of the parameters being themselves learned continuously by a neural network. The back propagation in training the previous weights is limited to a certain memory length (in the examples we consider 10 previous business days), which is similar to the maximal time lag of autoregressive processes. We demonstrate the learning efficiency of the new algorithm by tracking the next-day forecasts for the EURTRY and EUR-HUF exchange rates each.

  16. Adaptive control of chaotic systems with stochastic time varying unknown parameters

    Energy Technology Data Exchange (ETDEWEB)

    Salarieh, Hassan [Center of Excellence in Design, Robotics and Automation, Department of Mechanical Engineering, Sharif University of Technology, P.O. Box 11365-9567, Azadi Avenue, Tehran (Iran, Islamic Republic of)], E-mail: salarieh@mech.sharif.edu; Alasty, Aria [Center of Excellence in Design, Robotics and Automation, Department of Mechanical Engineering, Sharif University of Technology, P.O. Box 11365-9567, Azadi Avenue, Tehran (Iran, Islamic Republic of)], E-mail: aalasti@sharif.edu

    2008-10-15

    In this paper based on the Lyapunov stability theorem, an adaptive control scheme is proposed for stabilizing the unstable periodic orbits (UPO) of chaotic systems. It is assumed that the chaotic system has some linearly dependent unknown parameters which are stochastically time varying. The stochastic parameters are modeled through the Weiner process derivative. To demonstrate the effectiveness of the proposed technique it has been applied to the Lorenz, Chen and Rossler dynamical systems, as some case studies. Simulation results indicate that the proposed adaptive controller has a high performance in stabilizing the UPO of chaotic systems in noisy environment.

  17. Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

    DEFF Research Database (Denmark)

    Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

    2014-01-01

    A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented co...... applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed......A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant...

  18. Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits

    DEFF Research Database (Denmark)

    Pimentel Maia, Rafael; Madsen, Per; Labouriau, Rodrigo

    2013-01-01

    A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented co...... applications. The methods presented are implemented in such a way that large and complex quantitative genetic data can be analyzed......A class of multivariate mixed survival models for continuous and discrete time with a complex covariance structure is introduced in a context of quantitative genetic applications. The methods introduced can be used in many applications in quantitative genetics although the discussion presented...... concentrates on longevity studies. The framework presented allows to combine models based on continuous time with models based on discrete time in a joint analysis. The continuous time models are approximations of the frailty model in which the hazard function will be assumed to be piece-wise constant...

  19. Time Series, Stochastic Processes and Completeness of Quantum Theory

    International Nuclear Information System (INIS)

    Kupczynski, Marian

    2011-01-01

    Most of physical experiments are usually described as repeated measurements of some random variables. Experimental data registered by on-line computers form time series of outcomes. The frequencies of different outcomes are compared with the probabilities provided by the algorithms of quantum theory (QT). In spite of statistical predictions of QT a claim was made that it provided the most complete description of the data and of the underlying physical phenomena. This claim could be easily rejected if some fine structures, averaged out in the standard descriptive statistical analysis, were found in time series of experimental data. To search for these structures one has to use more subtle statistical tools which were developed to study time series produced by various stochastic processes. In this talk we review some of these tools. As an example we show how the standard descriptive statistical analysis of the data is unable to reveal a fine structure in a simulated sample of AR (2) stochastic process. We emphasize once again that the violation of Bell inequalities gives no information on the completeness or the non locality of QT. The appropriate way to test the completeness of quantum theory is to search for fine structures in time series of the experimental data by means of the purity tests or by studying the autocorrelation and partial autocorrelation functions.

  20. Bicriterion a priori route choice in stochastic time-dependent networks

    DEFF Research Database (Denmark)

    Nielsen, Lars Relund; Andersen, Kim Allan; Pretolani, Daniele

    In recent years there has been a growing interest in using stochastic time-dependent (STD) networks as a modelling tool for a number of applications within such areas as transportation and telecommunications. It is known that an optimal routing policy does not necessarily correspond to a path...

  1. Bicriterion a priori route choice in stochastic time-dependent networks

    DEFF Research Database (Denmark)

    Nielsen, Lars Relund; Pretolani, D; Andersen, K A

    2006-01-01

    In recent years there has been a growing interest in using stochastic time-dependent (STD) networks as a modelling tool for a number of applications within such areas as transportation and telecommunications. It is known that an optimal routing policy does not necessarily correspond to a path...

  2. Stochastic behavior of a cold standby system with maximum repair time

    Directory of Open Access Journals (Sweden)

    Ashish Kumar

    2015-09-01

    Full Text Available The main aim of the present paper is to analyze the stochastic behavior of a cold standby system with concept of preventive maintenance, priority and maximum repair time. For this purpose, a stochastic model is developed in which initially one unit is operative and other is kept as cold standby. There is a single server who visits the system immediately as and when required. The server takes the unit under preventive maintenance after a maximum operation time at normal mode if one standby unit is available for operation. If the repair of the failed unit is not possible up to a maximum repair time, failed unit is replaced by new one. The failure time, maximum operation time and maximum repair time distributions of the unit are considered as exponentially distributed while repair and maintenance time distributions are considered as arbitrary. All random variables are statistically independent and repairs are perfect. Various measures of system effectiveness are obtained by using the technique of semi-Markov process and RPT. To highlight the importance of the study numerical results are also obtained for MTSF, availability and profit function.

  3. A Newton Algorithm for Multivariate Total Least Squares Problems

    Directory of Open Access Journals (Sweden)

    WANG Leyang

    2016-04-01

    Full Text Available In order to improve calculation efficiency of parameter estimation, an algorithm for multivariate weighted total least squares adjustment based on Newton method is derived. The relationship between the solution of this algorithm and that of multivariate weighted total least squares adjustment based on Lagrange multipliers method is analyzed. According to propagation of cofactor, 16 computational formulae of cofactor matrices of multivariate total least squares adjustment are also listed. The new algorithm could solve adjustment problems containing correlation between observation matrix and coefficient matrix. And it can also deal with their stochastic elements and deterministic elements with only one cofactor matrix. The results illustrate that the Newton algorithm for multivariate total least squares problems could be practiced and have higher convergence rate.

  4. Volatility Degree Forecasting of Stock Market by Stochastic Time Strength Neural Network

    Directory of Open Access Journals (Sweden)

    Haiyan Mo

    2013-01-01

    Full Text Available In view of the applications of artificial neural networks in economic and financial forecasting, a stochastic time strength function is introduced in the backpropagation neural network model to predict the fluctuations of stock price changes. In this model, stochastic time strength function gives a weight for each historical datum and makes the model have the effect of random movement, and then we investigate and forecast the behavior of volatility degrees of returns for the Chinese stock market indexes and some global market indexes. The empirical research is performed in testing the prediction effect of SSE, SZSE, HSI, DJIA, IXIC, and S&P 500 with different selected volatility degrees in the established model.

  5. A continuous stochastic model for non-equilibrium dense gases

    Science.gov (United States)

    Sadr, M.; Gorji, M. H.

    2017-12-01

    While accurate simulations of dense gas flows far from the equilibrium can be achieved by direct simulation adapted to the Enskog equation, the significant computational demand required for collisions appears as a major constraint. In order to cope with that, an efficient yet accurate solution algorithm based on the Fokker-Planck approximation of the Enskog equation is devised in this paper; the approximation is very much associated with the Fokker-Planck model derived from the Boltzmann equation by Jenny et al. ["A solution algorithm for the fluid dynamic equations based on a stochastic model for molecular motion," J. Comput. Phys. 229, 1077-1098 (2010)] and Gorji et al. ["Fokker-Planck model for computational studies of monatomic rarefied gas flows," J. Fluid Mech. 680, 574-601 (2011)]. The idea behind these Fokker-Planck descriptions is to project the dynamics of discrete collisions implied by the molecular encounters into a set of continuous Markovian processes subject to the drift and diffusion. Thereby, the evolution of particles representing the governing stochastic process becomes independent from each other and thus very efficient numerical schemes can be constructed. By close inspection of the Enskog operator, it is observed that the dense gas effects contribute further to the advection of molecular quantities. That motivates a modelling approach where the dense gas corrections can be cast in the extra advection of particles. Therefore, the corresponding Fokker-Planck approximation is derived such that the evolution in the physical space accounts for the dense effects present in the pressure, stress tensor, and heat fluxes. Hence the consistency between the devised Fokker-Planck approximation and the Enskog operator is shown for the velocity moments up to the heat fluxes. For validation studies, a homogeneous gas inside a box besides Fourier, Couette, and lid-driven cavity flow setups is considered. The results based on the Fokker-Planck model are

  6. Scale and shape mixtures of multivariate skew-normal distributions

    KAUST Repository

    Arellano-Valle, Reinaldo B.

    2018-02-26

    We introduce a broad and flexible class of multivariate distributions obtained by both scale and shape mixtures of multivariate skew-normal distributions. We present the probabilistic properties of this family of distributions in detail and lay down the theoretical foundations for subsequent inference with this model. In particular, we study linear transformations, marginal distributions, selection representations, stochastic representations and hierarchical representations. We also describe an EM-type algorithm for maximum likelihood estimation of the parameters of the model and demonstrate its implementation on a wind dataset. Our family of multivariate distributions unifies and extends many existing models of the literature that can be seen as submodels of our proposal.

  7. Reconstructing the hidden states in time course data of stochastic models.

    Science.gov (United States)

    Zimmer, Christoph

    2015-11-01

    Parameter estimation is central for analyzing models in Systems Biology. The relevance of stochastic modeling in the field is increasing. Therefore, the need for tailored parameter estimation techniques is increasing as well. Challenges for parameter estimation are partial observability, measurement noise, and the computational complexity arising from the dimension of the parameter space. This article extends the multiple shooting for stochastic systems' method, developed for inference in intrinsic stochastic systems. The treatment of extrinsic noise and the estimation of the unobserved states is improved, by taking into account the correlation between unobserved and observed species. This article demonstrates the power of the method on different scenarios of a Lotka-Volterra model, including cases in which the prey population dies out or explodes, and a Calcium oscillation system. Besides showing how the new extension improves the accuracy of the parameter estimates, this article analyzes the accuracy of the state estimates. In contrast to previous approaches, the new approach is well able to estimate states and parameters for all the scenarios. As it does not need stochastic simulations, it is of the same order of speed as conventional least squares parameter estimation methods with respect to computational time. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  8. Stochastic approach to equilibrium and nonequilibrium thermodynamics.

    Science.gov (United States)

    Tomé, Tânia; de Oliveira, Mário J

    2015-04-01

    We develop the stochastic approach to thermodynamics based on stochastic dynamics, which can be discrete (master equation) and continuous (Fokker-Planck equation), and on two assumptions concerning entropy. The first is the definition of entropy itself and the second the definition of entropy production rate, which is non-negative and vanishes in thermodynamic equilibrium. Based on these assumptions, we study interacting systems with many degrees of freedom in equilibrium or out of thermodynamic equilibrium and how the macroscopic laws are derived from the stochastic dynamics. These studies include the quasiequilibrium processes; the convexity of the equilibrium surface; the monotonic time behavior of thermodynamic potentials, including entropy; the bilinear form of the entropy production rate; the Onsager coefficients and reciprocal relations; and the nonequilibrium steady states of chemical reactions.

  9. Time-variant reliability assessment through equivalent stochastic process transformation

    International Nuclear Information System (INIS)

    Wang, Zequn; Chen, Wei

    2016-01-01

    Time-variant reliability measures the probability that an engineering system successfully performs intended functions over a certain period of time under various sources of uncertainty. In practice, it is computationally prohibitive to propagate uncertainty in time-variant reliability assessment based on expensive or complex numerical models. This paper presents an equivalent stochastic process transformation approach for cost-effective prediction of reliability deterioration over the life cycle of an engineering system. To reduce the high dimensionality, a time-independent reliability model is developed by translating random processes and time parameters into random parameters in order to equivalently cover all potential failures that may occur during the time interval of interest. With the time-independent reliability model, an instantaneous failure surface is attained by using a Kriging-based surrogate model to identify all potential failure events. To enhance the efficacy of failure surface identification, a maximum confidence enhancement method is utilized to update the Kriging model sequentially. Then, the time-variant reliability is approximated using Monte Carlo simulations of the Kriging model where system failures over a time interval are predicted by the instantaneous failure surface. The results of two case studies demonstrate that the proposed approach is able to accurately predict the time evolution of system reliability while requiring much less computational efforts compared with the existing analytical approach. - Highlights: • Developed a new approach for time-variant reliability analysis. • Proposed a novel stochastic process transformation procedure to reduce the dimensionality. • Employed Kriging models with confidence-based adaptive sampling scheme to enhance computational efficiency. • The approach is effective for handling random process in time-variant reliability analysis. • Two case studies are used to demonstrate the efficacy

  10. Introduction to modeling and analysis of stochastic systems

    CERN Document Server

    Kulkarni, V G

    2011-01-01

    This is an introductory-level text on stochastic modeling. It is suited for undergraduate students in engineering, operations research, statistics, mathematics, actuarial science, business management, computer science, and public policy. It employs a large number of examples to teach the students to use stochastic models of real-life systems to predict their performance, and use this analysis to design better systems. The book is devoted to the study of important classes of stochastic processes: discrete and continuous time Markov processes, Poisson processes, renewal and regenerative processes, semi-Markov processes, queueing models, and diffusion processes. The book systematically studies the short-term and the long-term behavior, cost/reward models, and first passage times. All the material is illustrated with many examples, and case studies. The book provides a concise review of probability in the appendix. The book emphasizes numerical answers to the problems. A collection of MATLAB programs to accompany...

  11. Modeling Covariance Breakdowns in Multivariate GARCH

    OpenAIRE

    Jin, Xin; Maheu, John M

    2014-01-01

    This paper proposes a flexible way of modeling dynamic heterogeneous covariance breakdowns in multivariate GARCH (MGARCH) models. During periods of normal market activity, volatility dynamics are governed by an MGARCH specification. A covariance breakdown is any significant temporary deviation of the conditional covariance matrix from its implied MGARCH dynamics. This is captured through a flexible stochastic component that allows for changes in the conditional variances, covariances and impl...

  12. Multivariate log-skew-elliptical distributions with applications to precipitation data

    KAUST Repository

    Marchenko, Yulia V.

    2009-07-13

    We introduce a family of multivariate log-skew-elliptical distributions, extending the list of multivariate distributions with positive support. We investigate their probabilistic properties such as stochastic representations, marginal and conditional distributions, and existence of moments, as well as inferential properties. We demonstrate, for example, that as for the log-t distribution, the positive moments of the log-skew-t distribution do not exist. Our emphasis is on two special cases, the log-skew-normal and log-skew-t distributions, which we use to analyze US national (univariate) and regional (multivariate) monthly precipitation data. © 2009 John Wiley & Sons, Ltd.

  13. Multivariate log-skew-elliptical distributions with applications to precipitation data

    KAUST Repository

    Marchenko, Yulia V.; Genton, Marc G.

    2009-01-01

    We introduce a family of multivariate log-skew-elliptical distributions, extending the list of multivariate distributions with positive support. We investigate their probabilistic properties such as stochastic representations, marginal and conditional distributions, and existence of moments, as well as inferential properties. We demonstrate, for example, that as for the log-t distribution, the positive moments of the log-skew-t distribution do not exist. Our emphasis is on two special cases, the log-skew-normal and log-skew-t distributions, which we use to analyze US national (univariate) and regional (multivariate) monthly precipitation data. © 2009 John Wiley & Sons, Ltd.

  14. Two-boundary first exit time of Gauss-Markov processes for stochastic modeling of acto-myosin dynamics.

    Science.gov (United States)

    D'Onofrio, Giuseppe; Pirozzi, Enrica

    2017-05-01

    We consider a stochastic differential equation in a strip, with coefficients suitably chosen to describe the acto-myosin interaction subject to time-varying forces. By simulating trajectories of the stochastic dynamics via an Euler discretization-based algorithm, we fit experimental data and determine the values of involved parameters. The steps of the myosin are represented by the exit events from the strip. Motivated by these results, we propose a specific stochastic model based on the corresponding time-inhomogeneous Gauss-Markov and diffusion process evolving between two absorbing boundaries. We specify the mean and covariance functions of the stochastic modeling process taking into account time-dependent forces including the effect of an external load. We accurately determine the probability density function (pdf) of the first exit time (FET) from the strip by solving a system of two non singular second-type Volterra integral equations via a numerical quadrature. We provide numerical estimations of the mean of FET as approximations of the dwell-time of the proteins dynamics. The percentage of backward steps is given in agreement to experimental data. Numerical and simulation results are compared and discussed.

  15. A Framework and Algorithms for Multivariate Time Series Analytics (MTSA): Learning, Monitoring, and Recommendation

    Science.gov (United States)

    Ngan, Chun-Kit

    2013-01-01

    Making decisions over multivariate time series is an important topic which has gained significant interest in the past decade. A time series is a sequence of data points which are measured and ordered over uniform time intervals. A multivariate time series is a set of multiple, related time series in a particular domain in which domain experts…

  16. Hybrid Semantics of Stochastic Programs with Dynamic Reconfiguration

    Directory of Open Access Journals (Sweden)

    Alberto Policriti

    2009-10-01

    Full Text Available We begin by reviewing a technique to approximate the dynamics of stochastic programs --written in a stochastic process algebra-- by a hybrid system, suitable to capture a mixed discrete/continuous evolution. In a nutshell, the discrete dynamics is kept stochastic while the continuous evolution is given in terms of ODEs, and the overall technique, therefore, naturally associates a Piecewise Deterministic Markov Process with a stochastic program. The specific contribution in this work consists in an increase of the flexibility of the translation scheme, obtained by allowing a dynamic reconfiguration of the degree of discreteness/continuity of the semantics. We also discuss the relationships of this approach with other hybrid simulation strategies for biochemical systems.

  17. Stochastic multiresonance for a fractional linear oscillator with time-delayed kernel and quadratic noise

    Science.gov (United States)

    Guo, Feng; Wang, Xue-Yuan; Zhu, Cheng-Yin; Cheng, Xiao-Feng; Zhang, Zheng-Yu; Huang, Xu-Hui

    2017-12-01

    The stochastic resonance for a fractional oscillator with time-delayed kernel and quadratic trichotomous noise is investigated. Applying linear system theory and Laplace transform, the system output amplitude (SPA) for the fractional oscillator is obtained. It is found that the SPA is a periodical function of the kernel delayed-time. Stochastic multiplicative phenomenon appears on the SPA versus the driving frequency, versus the noise amplitude, and versus the fractional exponent. The non-monotonous dependence of the SPA on the system parameters is also discussed.

  18. Analytical Solutions for Multi-Time Scale Fractional Stochastic Differential Equations Driven by Fractional Brownian Motion and Their Applications

    Directory of Open Access Journals (Sweden)

    Xiao-Li Ding

    2018-01-01

    Full Text Available In this paper, we investigate analytical solutions of multi-time scale fractional stochastic differential equations driven by fractional Brownian motions. We firstly decompose homogeneous multi-time scale fractional stochastic differential equations driven by fractional Brownian motions into independent differential subequations, and give their analytical solutions. Then, we use the variation of constant parameters to obtain the solutions of nonhomogeneous multi-time scale fractional stochastic differential equations driven by fractional Brownian motions. Finally, we give three examples to demonstrate the applicability of our obtained results.

  19. Multivariable Real-Time Control of Viscosity Curve for a Continuous Production Process of a Non-Newtonian Fluid

    Directory of Open Access Journals (Sweden)

    Roberto Mei

    2018-01-01

    Full Text Available The application of a multivariable predictive controller to the mixing process for the production of a non-Newtonian fluid is discussed in this work. A data-driven model has been developed to describe the dynamic behaviour of the rheological properties of the fluid as a function of the operating conditions using experimental data collected in a pilot plant. The developed model provides a realistic process representation and it is used to test and verify the multivariable controller, which has been designed to maintain viscosity curves of the non-Newtonian fluid within a given region of the viscosity-vs-shear rate plane in presence of process disturbances occurring in the mixing process.

  20. Relevance of control theory to design and maintenance problems in time-variant reliability: The case of stochastic viability

    International Nuclear Information System (INIS)

    Rougé, Charles; Mathias, Jean-Denis; Deffuant, Guillaume

    2014-01-01

    The goal of this paper is twofold: (1) to show that time-variant reliability and a branch of control theory called stochastic viability address similar problems with different points of view, and (2) to demonstrate the relevance of concepts and methods from stochastic viability in reliability problems. On the one hand, reliability aims at evaluating the probability of failure of a system subjected to uncertainty and stochasticity. On the other hand, viability aims at maintaining a controlled dynamical system within a survival set. When the dynamical system is stochastic, this work shows that a viability problem belongs to a specific class of design and maintenance problems in time-variant reliability. Dynamic programming, which is used for solving Markovian stochastic viability problems, then yields the set of design states for which there exists a maintenance strategy which guarantees reliability with a confidence level β for a given period of time T. Besides, it leads to a straightforward computation of the date of the first outcrossing, informing on when the system is most likely to fail. We illustrate this approach with a simple example of population dynamics, including a case where load increases with time. - Highlights: • Time-variant reliability tools cannot devise complex maintenance strategies. • Stochastic viability is a control theory that computes a probability of failure. • Some design and maintenance problems are stochastic viability problems. • Used in viability, dynamic programming can find reliable maintenance actions. • Confronting reliability and control theories such as viability is promising

  1. Asymptotic theory for the sample covariance matrix of a heavy-tailed multivariate time series

    DEFF Research Database (Denmark)

    Davis, Richard A.; Mikosch, Thomas Valentin; Pfaffel, Olivier

    2016-01-01

    In this paper we give an asymptotic theory for the eigenvalues of the sample covariance matrix of a multivariate time series. The time series constitutes a linear process across time and between components. The input noise of the linear process has regularly varying tails with index α∈(0,4) in...... particular, the time series has infinite fourth moment. We derive the limiting behavior for the largest eigenvalues of the sample covariance matrix and show point process convergence of the normalized eigenvalues. The limiting process has an explicit form involving points of a Poisson process and eigenvalues...... of a non-negative definite matrix. Based on this convergence we derive limit theory for a host of other continuous functionals of the eigenvalues, including the joint convergence of the largest eigenvalues, the joint convergence of the largest eigenvalue and the trace of the sample covariance matrix...

  2. Quantum continual measurements and a posteriori collapse on CCR

    International Nuclear Information System (INIS)

    Belavkin, V.P.

    1992-01-01

    A quantum stochastic model for the Markovian dynamics of an open system under the nondemolition unsharp observation which is continuous in time, is given. A stochastic equation for the posterior evolution of a quantum continuously observed system is derived and the spontaneous collapse (stochastically continuous reduction of the wave packet) is described. The quantum Langevin evolution equation is solved for the case of a quasi-free Hamiltonian in the initial CCR algebra with a linear output channel, and the posterior dynamics corresponding to an initial Gaussian state is found. It is shown for an example of the posterior dynamics of a quantum oscillator that any mixed state under a complete nondemolition measurement collapses exponentially to a pure Gaussian one. (orig.)

  3. Simulation of multivariate diffusion bridges

    DEFF Research Database (Denmark)

    Bladt, Mogens; Finch, Samuel; Sørensen, Michael

    We propose simple methods for multivariate diffusion bridge simulation, which plays a fundamental role in simulation-based likelihood and Bayesian inference for stochastic differential equations. By a novel application of classical coupling methods, the new approach generalizes a previously...... proposed simulation method for one-dimensional bridges to the mulit-variate setting. First a method of simulating approzimate, but often very accurate, diffusion bridges is proposed. These approximate bridges are used as proposal for easily implementable MCMC algorithms that produce exact diffusion bridges...

  4. Multivariate Markov chain modeling for stock markets

    Science.gov (United States)

    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.

  5. Multivariate Non-Symmetric Stochastic Models for Spatial Dependence Models

    Science.gov (United States)

    Haslauer, C. P.; Bárdossy, A.

    2017-12-01

    A copula based multivariate framework allows more flexibility to describe different kind of dependences than what is possible using models relying on the confining assumption of symmetric Gaussian models: different quantiles can be modelled with a different degree of dependence; it will be demonstrated how this can be expected given process understanding. maximum likelihood based multivariate quantitative parameter estimation yields stable and reliable results; not only improved results in cross-validation based measures of uncertainty are obtained but also a more realistic spatial structure of uncertainty compared to second order models of dependence; as much information as is available is included in the parameter estimation: incorporation of censored measurements (e.g., below detection limit, or ones that are above the sensitive range of the measurement device) yield to more realistic spatial models; the proportion of true zeros can be jointly estimated with and distinguished from censored measurements which allow estimates about the age of a contaminant in the system; secondary information (categorical and on the rational scale) has been used to improve the estimation of the primary variable; These copula based multivariate statistical techniques are demonstrated based on hydraulic conductivity observations at the Borden (Canada) site, the MADE site (USA), and a large regional groundwater quality data-set in south-west Germany. Fields of spatially distributed K were simulated with identical marginal simulation, identical second order spatial moments, yet substantially differing solute transport characteristics when numerical tracer tests were performed. A statistical methodology is shown that allows the delineation of a boundary layer separating homogenous parts of a spatial data-set. The effects of this boundary layer (macro structure) and the spatial dependence of K (micro structure) on solute transport behaviour is shown.

  6. Robust stability for stochastic bidirectional associative memory neural networks with time delays

    Science.gov (United States)

    Shu, H. S.; Lv, Z. W.; Wei, G. L.

    2008-02-01

    In this paper, the asymptotic stability is considered for a class of uncertain stochastic bidirectional associative memory neural networks with time delays and parameter uncertainties. The delays are time-invariant and the uncertainties are norm-bounded that enter into all network parameters. The aim of this paper is to establish easily verifiable conditions under which the delayed neural network is robustly asymptotically stable in the mean square for all admissible parameter uncertainties. By employing a Lyapunov-Krasovskii functional and conducting the stochastic analysis, a linear matrix inequality matrix inequality (LMI) approach is developed to derive the stability criteria. The proposed criteria can be easily checked by the Matlab LMI toolbox. A numerical example is given to demonstrate the usefulness of the proposed criteria.

  7. Deterministic and stochastic CTMC models from Zika disease transmission

    Science.gov (United States)

    Zevika, Mona; Soewono, Edy

    2018-03-01

    Zika infection is one of the most important mosquito-borne diseases in the world. Zika virus (ZIKV) is transmitted by many Aedes-type mosquitoes including Aedes aegypti. Pregnant women with the Zika virus are at risk of having a fetus or infant with a congenital defect and suffering from microcephaly. Here, we formulate a Zika disease transmission model using two approaches, a deterministic model and a continuous-time Markov chain stochastic model. The basic reproduction ratio is constructed from a deterministic model. Meanwhile, the CTMC stochastic model yields an estimate of the probability of extinction and outbreaks of Zika disease. Dynamical simulations and analysis of the disease transmission are shown for the deterministic and stochastic models.

  8. On the small time asymptotics of 3D stochastic primitive equations

    OpenAIRE

    Dong, Zhao; Zhang, Rangrang

    2017-01-01

    In this paper, we establish a small time large deviation principle for the strong solution of 3D stochastic primitive equations driven by multiplicative noise. Both the small noise and the small, but highly nonlinear, unbounded nonlinear terms should be taken into consideration.

  9. Assessing and accounting for time heterogeneity in stochastic actor oriented models

    NARCIS (Netherlands)

    Lospinoso, Joshua A.; Schweinberger, Michael; Snijders, Tom A. B.; Ripley, Ruth M.

    This paper explores time heterogeneity in stochastic actor oriented models (SAOM) proposed by Snijders (Sociological methodology. Blackwell, Boston, pp 361-395, 2001) which are meant to study the evolution of networks. SAOMs model social networks as directed graphs with nodes representing people,

  10. The stochastic system approach for estimating dynamic treatments effect.

    Science.gov (United States)

    Commenges, Daniel; Gégout-Petit, Anne

    2015-10-01

    The problem of assessing the effect of a treatment on a marker in observational studies raises the difficulty that attribution of the treatment may depend on the observed marker values. As an example, we focus on the analysis of the effect of a HAART on CD4 counts, where attribution of the treatment may depend on the observed marker values. This problem has been treated using marginal structural models relying on the counterfactual/potential response formalism. Another approach to causality is based on dynamical models, and causal influence has been formalized in the framework of the Doob-Meyer decomposition of stochastic processes. Causal inference however needs assumptions that we detail in this paper and we call this approach to causality the "stochastic system" approach. First we treat this problem in discrete time, then in continuous time. This approach allows incorporating biological knowledge naturally. When working in continuous time, the mechanistic approach involves distinguishing the model for the system and the model for the observations. Indeed, biological systems live in continuous time, and mechanisms can be expressed in the form of a system of differential equations, while observations are taken at discrete times. Inference in mechanistic models is challenging, particularly from a numerical point of view, but these models can yield much richer and reliable results.

  11. Urban Freight Management with Stochastic Time-Dependent Travel Times and Application to Large-Scale Transportation Networks

    Directory of Open Access Journals (Sweden)

    Shichao Sun

    2015-01-01

    Full Text Available This paper addressed the vehicle routing problem (VRP in large-scale urban transportation networks with stochastic time-dependent (STD travel times. The subproblem which is how to find the optimal path connecting any pair of customer nodes in a STD network was solved through a robust approach without requiring the probability distributions of link travel times. Based on that, the proposed STD-VRP model can be converted into solving a normal time-dependent VRP (TD-VRP, and algorithms for such TD-VRPs can also be introduced to obtain the solution. Numerical experiments were conducted to address STD-VRPTW of practical sizes on a real world urban network, demonstrated here on the road network of Shenzhen, China. The stochastic time-dependent link travel times of the network were calibrated by historical floating car data. A route construction algorithm was applied to solve the STD problem in 4 delivery scenarios efficiently. The computational results showed that the proposed STD-VRPTW model can improve the level of customer service by satisfying the time-window constraint under any circumstances. The improvement can be very significant especially for large-scale network delivery tasks with no more increase in cost and environmental impacts.

  12. Stochastic Modelling and Self Tuning Control of a Continuous Cement Raw Material Mixing System

    Directory of Open Access Journals (Sweden)

    Hannu T. Toivonen

    1980-01-01

    Full Text Available The control of a continuously operating system for cement raw material mixing is studied. The purpose of the mixing system is to maintain a constant composition of the cement raw meal for the kiln despite variations of the raw material compositions. Experimental knowledge of the process dynamics and the characteristics of the various disturbances is used for deriving a stochastic model of the system. The optimal control strategy is then obtained as a minimum variance strategy. The control problem is finally solved using a self-tuning minimum variance regulator, and results from a successful implementation of the regulator are given.

  13. Continuous-time random walks with reset events. Historical background and new perspectives

    Science.gov (United States)

    Montero, Miquel; Masó-Puigdellosas, Axel; Villarroel, Javier

    2017-09-01

    In this paper, we consider a stochastic process that may experience random reset events which relocate the system to its starting position. We focus our attention on a one-dimensional, monotonic continuous-time random walk with a constant drift: the process moves in a fixed direction between the reset events, either by the effect of the random jumps, or by the action of a deterministic bias. However, the orientation of its motion is randomly determined after each restart. As a result of these alternating dynamics, interesting properties do emerge. General formulas for the propagator as well as for two extreme statistics, the survival probability and the mean first-passage time, are also derived. The rigor of these analytical results is verified by numerical estimations, for particular but illuminating examples.

  14. Multidimensional stochastic approximation using locally contractive functions

    Science.gov (United States)

    Lawton, W. M.

    1975-01-01

    A Robbins-Monro type multidimensional stochastic approximation algorithm which converges in mean square and with probability one to the fixed point of a locally contractive regression function is developed. The algorithm is applied to obtain maximum likelihood estimates of the parameters for a mixture of multivariate normal distributions.

  15. Backward jump continuous-time random walk: An application to market trading

    Science.gov (United States)

    Gubiec, Tomasz; Kutner, Ryszard

    2010-10-01

    The backward jump modification of the continuous-time random walk model or the version of the model driven by the negative feedback was herein derived for spatiotemporal continuum in the context of a share price evolution on a stock exchange. In the frame of the model, we described stochastic evolution of a typical share price on a stock exchange with a moderate liquidity within a high-frequency time scale. The model was validated by satisfactory agreement of the theoretical velocity autocorrelation function with its empirical counterpart obtained for the continuous quotation. This agreement is mainly a result of a sharp backward correlation found and considered in this article. This correlation is a reminiscence of such a bid-ask bounce phenomenon where backward price jump has the same or almost the same length as preceding jump. We suggested that this correlation dominated the dynamics of the stock market with moderate liquidity. Although assumptions of the model were inspired by the market high-frequency empirical data, its potential applications extend beyond the financial market, for instance, to the field covered by the Le Chatelier-Braun principle of contrariness.

  16. A Continuous-Time Model for Valuing Foreign Exchange Options

    Directory of Open Access Journals (Sweden)

    James J. Kung

    2013-01-01

    Full Text Available This paper makes use of stochastic calculus to develop a continuous-time model for valuing European options on foreign exchange (FX when both domestic and foreign spot rates follow a generalized Wiener process. Using the dollar/euro exchange rate as input for parameter estimation and employing our FX option model as a yardstick, we find that the traditional Garman-Kohlhagen FX option model, which assumes constant spot rates, values incorrectly calls and puts for different values of the ratio of exchange rate to exercise price. Specifically, it undervalues calls when the ratio is between 0.70 and 1.08, and it overvalues calls when the ratio is between 1.18 and 1.30, whereas it overvalues puts when the ratio is between 0.70 and 0.82, and it undervalues puts when the ratio is between 0.86 and 1.30.

  17. Analyzing a stochastic time series obeying a second-order differential equation.

    Science.gov (United States)

    Lehle, B; Peinke, J

    2015-06-01

    The stochastic properties of a Langevin-type Markov process can be extracted from a given time series by a Markov analysis. Also processes that obey a stochastically forced second-order differential equation can be analyzed this way by employing a particular embedding approach: To obtain a Markovian process in 2N dimensions from a non-Markovian signal in N dimensions, the system is described in a phase space that is extended by the temporal derivative of the signal. For a discrete time series, however, this derivative can only be calculated by a differencing scheme, which introduces an error. If the effects of this error are not accounted for, this leads to systematic errors in the estimation of the drift and diffusion functions of the process. In this paper we will analyze these errors and we will propose an approach that correctly accounts for them. This approach allows an accurate parameter estimation and, additionally, is able to cope with weak measurement noise, which may be superimposed to a given time series.

  18. Some considerations on stochastic neutron populations (u)

    International Nuclear Information System (INIS)

    Souto, Francisco J.; Prinja, Anil K.

    2010-01-01

    The neutron population in a multiplying body containing a weak random source may depart considerably from its average or expected value. The resulting behavior of the system is then unpredictable and a fully stochastic description of the neutron population becomes necessary. Stochastic considerations are especially important when dealing with pulsed reactors or in the case of criticality excursions in the presence of a weak source. Using the theory of discrete-state continuous-time Markov processes, and subject to some physical approximations, Bell (I) obtained approximate solutions for the neutron number probability distributions (pdf), with and without an intrinsic rapdom neutron source, that were valid at late times and/ large neutron populations. In recent work (4), we obtained exact solutions for Bell's model problem, and in this paper we use these exact probability distributions to: (I) assess the accuracy of Bell's asymptotic solutions and show how the latter follow from the exact solutions, (2) rigorously examine the probability of obtaining a divergent chain reaction, and (3) demonstrate the existence of an abrupt transition from a stochastic to a deterministic phase with increasing source strength.

  19. Sliding mode control-based linear functional observers for discrete-time stochastic systems

    Science.gov (United States)

    Singh, Satnesh; Janardhanan, Sivaramakrishnan

    2017-11-01

    Sliding mode control (SMC) is one of the most popular techniques to stabilise linear discrete-time stochastic systems. However, application of SMC becomes difficult when the system states are not available for feedback. This paper presents a new approach to design a SMC-based functional observer for discrete-time stochastic systems. The functional observer is based on the Kronecker product approach. Existence conditions and stability analysis of the proposed observer are given. The control input is estimated by a novel linear functional observer. This approach leads to a non-switching type of control, thereby eliminating the fundamental cause of chatter. Furthermore, the functional observer is designed in such a way that the effect of process and measurement noise is minimised. Simulation example is given to illustrate and validate the proposed design method.

  20. Testing for Volatility Co-movement in Bivariate Stochastic Volatility Models

    OpenAIRE

    Chen, Jinghui; Kobayashi, Masahito; McAleer, Michael

    2017-01-01

    markdownabstractThe paper considers the problem of volatility co-movement, namely as to whether two financial returns have perfectly correlated common volatility process, in the framework of multivariate stochastic volatility models and proposes a test which checks the volatility co-movement. The proposed test is a stochastic volatility version of the co-movement test proposed by Engle and Susmel (1993), who investigated whether international equity markets have volatility co-movement using t...

  1. A General Theory of Markovian Time Inconsistent Stochastic Control Problems

    DEFF Research Database (Denmark)

    Björk, Tomas; Murgochi, Agatha

    We develop a theory for stochastic control problems which, in various ways, are time inconsistent in the sense that they do not admit a Bellman optimality principle. We attach these problems by viewing them within a game theoretic framework, and we look for Nash subgame perfect equilibrium points...... examples of time inconsistency in the literature are easily seen to be special cases of the present theory. We also prove that for every time inconsistent problem, there exists an associated time consistent problem such that the optimal control and the optimal value function for the consistent problem...

  2. Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines

    Science.gov (United States)

    Neftci, Emre O.; Pedroni, Bruno U.; Joshi, Siddharth; Al-Shedivat, Maruan; Cauwenberghs, Gert

    2016-01-01

    Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware. PMID:27445650

  3. Approximation of itô integrals arising in stochastic time-delayed systems

    NARCIS (Netherlands)

    Bagchi, Arunabha

    1984-01-01

    Likelihood functional for stochastic linear time-delayed systems involve Itô integrals with respect to the observed data. Since the Wiener process appearing in the standard observation process model for such systems is not realizable and the physically observed process is smooth, one needs to study

  4. Finite-Time Nonfragile Synchronization of Stochastic Complex Dynamical Networks with Semi-Markov Switching Outer Coupling

    Directory of Open Access Journals (Sweden)

    Rathinasamy Sakthivel

    2018-01-01

    Full Text Available The problem of robust nonfragile synchronization is investigated in this paper for a class of complex dynamical networks subject to semi-Markov jumping outer coupling, time-varying coupling delay, randomly occurring gain variation, and stochastic noise over a desired finite-time interval. In particular, the network topology is assumed to follow a semi-Markov process such that it may switch from one to another at different instants. In this paper, the random gain variation is represented by a stochastic variable that is assumed to satisfy the Bernoulli distribution with white sequences. Based on these hypotheses and the Lyapunov-Krasovskii stability theory, a new finite-time stochastic synchronization criterion is established for the considered network in terms of linear matrix inequalities. Moreover, the control design parameters that guarantee the required criterion are computed by solving a set of linear matrix inequality constraints. An illustrative example is finally given to show the effectiveness and advantages of the developed analytical results.

  5. Hybrid stochastic simplifications for multiscale gene networks

    Directory of Open Access Journals (Sweden)

    Debussche Arnaud

    2009-09-01

    Full Text Available Abstract Background Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. Results We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion 123 which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Conclusion Hybrid simplifications can be used for onion-like (multi-layered approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach.

  6. Designing time-of-use program based on stochastic security constrained unit commitment considering reliability index

    International Nuclear Information System (INIS)

    Nikzad, Mehdi; Mozafari, Babak; Bashirvand, Mahdi; Solaymani, Soodabeh; Ranjbar, Ali Mohamad

    2012-01-01

    Recently in electricity markets, a massive focus has been made on setting up opportunities for participating demand side. Such opportunities, also known as demand response (DR) options, are triggered by either a grid reliability problem or high electricity prices. Two important challenges that market operators are facing are appropriate designing and reasonable pricing of DR options. In this paper, time-of-use program (TOU) as a prevalent time-varying program is modeled linearly based on own and cross elasticity definition. In order to decide on TOU rates, a stochastic model is proposed in which the optimum TOU rates are determined based on grid reliability index set by the operator. Expected Load Not Supplied (ELNS) is used to evaluate reliability of the power system in each hour. The proposed stochastic model is formulated as a two-stage stochastic mixed-integer linear programming (SMILP) problem and solved using CPLEX solver. The validity of the method is tested over the IEEE 24-bus test system. In this regard, the impact of the proposed pricing method on system load profile; operational costs and required capacity of up- and down-spinning reserve as well as improvement of load factor is demonstrated. Also the sensitivity of the results to elasticity coefficients is investigated. -- Highlights: ► Time-of-use demand response program is linearly modeled. ► A stochastic model is proposed to determine the optimum TOU rates based on ELNS index set by the operator. ► The model is formulated as a short-term two-stage stochastic mixed-integer linear programming problem.

  7. Effects of demographic stochasticity on biological community assembly on evolutionary time scales

    KAUST Repository

    Murase, Yohsuke; Shimada, Takashi; Ito, Nobuyasu; Rikvold, Per Arne

    2010-01-01

    We study the effects of demographic stochasticity on the long-term dynamics of biological coevolution models of community assembly. The noise is induced in order to check the validity of deterministic population dynamics. While mutualistic communities show little dependence on the stochastic population fluctuations, predator-prey models show strong dependence on the stochasticity, indicating the relevance of the finiteness of the populations. For a predator-prey model, the noise causes drastic decreases in diversity and total population size. The communities that emerge under influence of the noise consist of species strongly coupled with each other and have stronger linear stability around the fixed-point populations than the corresponding noiseless model. The dynamics on evolutionary time scales for the predator-prey model are also altered by the noise. Approximate 1/f fluctuations are observed with noise, while 1/ f2 fluctuations are found for the model without demographic noise. © 2010 The American Physical Society.

  8. Effects of demographic stochasticity on biological community assembly on evolutionary time scales

    KAUST Repository

    Murase, Yohsuke

    2010-04-13

    We study the effects of demographic stochasticity on the long-term dynamics of biological coevolution models of community assembly. The noise is induced in order to check the validity of deterministic population dynamics. While mutualistic communities show little dependence on the stochastic population fluctuations, predator-prey models show strong dependence on the stochasticity, indicating the relevance of the finiteness of the populations. For a predator-prey model, the noise causes drastic decreases in diversity and total population size. The communities that emerge under influence of the noise consist of species strongly coupled with each other and have stronger linear stability around the fixed-point populations than the corresponding noiseless model. The dynamics on evolutionary time scales for the predator-prey model are also altered by the noise. Approximate 1/f fluctuations are observed with noise, while 1/ f2 fluctuations are found for the model without demographic noise. © 2010 The American Physical Society.

  9. Multivariate time series analysis with R and financial applications

    CERN Document Server

    Tsay, Ruey S

    2013-01-01

    Since the publication of his first book, Analysis of Financial Time Series, Ruey Tsay has become one of the most influential and prominent experts on the topic of time series. Different from the traditional and oftentimes complex approach to multivariate (MV) time series, this sequel book emphasizes structural specification, which results in simplified parsimonious VARMA modeling and, hence, eases comprehension. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-worl

  10. Monte Carlo simulation of induction time and metastable zone width; stochastic or deterministic?

    Science.gov (United States)

    Kubota, Noriaki

    2018-03-01

    The induction time and metastable zone width (MSZW) measured for small samples (say 1 mL or less) both scatter widely. Thus, these two are observed as stochastic quantities. Whereas, for large samples (say 1000 mL or more), the induction time and MSZW are observed as deterministic quantities. The reason for such experimental differences is investigated with Monte Carlo simulation. In the simulation, the time (under isothermal condition) and supercooling (under polythermal condition) at which a first single crystal is detected are defined as the induction time t and the MSZW ΔT for small samples, respectively. The number of crystals just at the moment of t and ΔT is unity. A first crystal emerges at random due to the intrinsic nature of nucleation, accordingly t and ΔT become stochastic. For large samples, the time and supercooling at which the number density of crystals N/V reaches a detector sensitivity (N/V)det are defined as t and ΔT for isothermal and polythermal conditions, respectively. The points of t and ΔT are those of which a large number of crystals have accumulated. Consequently, t and ΔT become deterministic according to the law of large numbers. Whether t and ΔT may stochastic or deterministic in actual experiments should not be attributed to change in nucleation mechanisms in molecular level. It could be just a problem caused by differences in the experimental definition of t and ΔT.

  11. Multivariate time series modeling of selected childhood diseases in ...

    African Journals Online (AJOL)

    This paper is focused on modeling the five most prevalent childhood diseases in Akwa Ibom State using a multivariate approach to time series. An aggregate of 78,839 reported cases of malaria, upper respiratory tract infection (URTI), Pneumonia, anaemia and tetanus were extracted from five randomly selected hospitals in ...

  12. The Role of Stochastic Models in Interpreting the Origins of Biological Chirality

    Directory of Open Access Journals (Sweden)

    Gábor Lente

    2010-04-01

    Full Text Available This review summarizes recent stochastic modeling efforts in the theoretical research aimed at interpreting the origins of biological chirality. Stochastic kinetic models, especially those based on the continuous time discrete state approach, have great potential in modeling absolute asymmetric reactions, experimental examples of which have been reported in the past decade. An overview of the relevant mathematical background is given and several examples are presented to show how the significant numerical problems characteristic of the use of stochastic models can be overcome by non-trivial, but elementary algebra. In these stochastic models, a particulate view of matter is used rather than the concentration-based view of traditional chemical kinetics using continuous functions to describe the properties system. This has the advantage of giving adequate description of single-molecule events, which were probably important in the origin of biological chirality. The presented models can interpret and predict the random distribution of enantiomeric excess among repetitive experiments, which is the most striking feature of absolute asymmetric reactions. It is argued that the use of the stochastic kinetic approach should be much more widespread in the relevant literature.

  13. Hill functions for stochastic gene regulatory networks from master equations with split nodes and time-scale separation

    Science.gov (United States)

    Lipan, Ovidiu; Ferwerda, Cameron

    2018-02-01

    The deterministic Hill function depends only on the average values of molecule numbers. To account for the fluctuations in the molecule numbers, the argument of the Hill function needs to contain the means, the standard deviations, and the correlations. Here we present a method that allows for stochastic Hill functions to be constructed from the dynamical evolution of stochastic biocircuits with specific topologies. These stochastic Hill functions are presented in a closed analytical form so that they can be easily incorporated in models for large genetic regulatory networks. Using a repressive biocircuit as an example, we show by Monte Carlo simulations that the traditional deterministic Hill function inaccurately predicts time of repression by an order of two magnitudes. However, the stochastic Hill function was able to capture the fluctuations and thus accurately predicted the time of repression.

  14. Assessment of Multivariate Neural Time Series by Phase Synchrony Clustering in a Time-Frequency-Topography Representation

    Directory of Open Access Journals (Sweden)

    M. A. Porta-Garcia

    2018-01-01

    Full Text Available Most EEG phase synchrony measures are of bivariate nature. Those that are multivariate focus on producing global indices of the synchronization state of the system. Thus, better descriptions of spatial and temporal local interactions are still in demand. A framework for characterization of phase synchrony relationships between multivariate neural time series is presented, applied either in a single epoch or over an intertrial assessment, relying on a proposed clustering algorithm, termed Multivariate Time Series Clustering by Phase Synchrony, which generates fuzzy clusters for each multivalued time sample and thereupon obtains hard clusters according to a circular variance threshold; such cluster modes are then depicted in Time-Frequency-Topography representations of synchrony state beyond mere global indices. EEG signals from P300 Speller sessions of four subjects were analyzed, obtaining useful insights of synchrony patterns related to the ERP and even revealing steady-state artifacts at 7.6 Hz. Further, contrast maps of Levenshtein Distance highlight synchrony differences between ERP and no-ERP epochs, mainly at delta and theta bands. The framework, which is not limited to one synchrony measure, allows observing dynamics of phase changes and interactions among channels and can be applied to analyze other cognitive states rather than ERP versus no ERP.

  15. Dynamics of the stochastic Lorenz chaotic system with long memory effects

    Energy Technology Data Exchange (ETDEWEB)

    Zeng, Caibin, E-mail: zeng.cb@mail.scut.edu.cn; Yang, Qigui, E-mail: qgyang@scut.edu.cn [School of Mathematics, South China University of Technology, Guangzhou 510640 (China)

    2015-12-15

    Little seems to be known about the ergodic dynamics of stochastic systems with fractional noise. This paper is devoted to discern such long time dynamics through the stochastic Lorenz chaotic system (SLCS) with long memory effects. By a truncation technique, the SLCS is proved to generate a continuous stochastic dynamical system Λ. Based on the Krylov-Bogoliubov criterion, the required Lyapunov function is further established to ensure the existence of the invariant measure of Λ. Meanwhile, the uniqueness of the invariant measure of Λ is proved by examining the strong Feller property, together with an irreducibility argument. Therefore, the SLCS has exactly one adapted stationary solution.

  16. Studies to the stochastic theory of coupled reactorkinetic-thermohydraulic systems Pt. 2

    International Nuclear Information System (INIS)

    Mesko, L.

    1983-06-01

    The description is given of the noise phenomena taking place in a multivariable coupled system by a comprehensive model based on the theory of stochastic fluctuations. A comparison is made with models using transfer function formalism for systems characterized by deterministic open and closed loop signal transmission properties. The advantages of the stochastic model are illustrated by simple reactor dynamical examples having diagnostical importance. (author)

  17. Deterministic Versus Stochastic Interpretation of Continuously Monitored Sewer Systems

    DEFF Research Database (Denmark)

    Harremoës, Poul; Carstensen, Niels Jacob

    1994-01-01

    An analysis has been made of the uncertainty of input parameters to deterministic models for sewer systems. The analysis reveals a very significant uncertainty, which can be decreased, but not eliminated and has to be considered for engineering application. Stochastic models have a potential for ...

  18. Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis

    Energy Technology Data Exchange (ETDEWEB)

    Lutaif, N.A. [Departamento de Clínica Médica, Faculdade de Ciências Médicas, Universidade Estadual de Campinas, Campinas, SP (Brazil); Palazzo, R. Jr [Departamento de Telemática, Faculdade de Engenharia Elétrica e Computação, Universidade Estadual de Campinas, Campinas, SP (Brazil); Gontijo, J.A.R. [Departamento de Clínica Médica, Faculdade de Ciências Médicas, Universidade Estadual de Campinas, Campinas, SP (Brazil)

    2014-01-17

    Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile.

  19. Early detection of metabolic and energy disorders by thermal time series stochastic complexity analysis

    International Nuclear Information System (INIS)

    Lutaif, N.A.; Palazzo, R. Jr; Gontijo, J.A.R.

    2014-01-01

    Maintenance of thermal homeostasis in rats fed a high-fat diet (HFD) is associated with changes in their thermal balance. The thermodynamic relationship between heat dissipation and energy storage is altered by the ingestion of high-energy diet content. Observation of thermal registers of core temperature behavior, in humans and rodents, permits identification of some characteristics of time series, such as autoreference and stationarity that fit adequately to a stochastic analysis. To identify this change, we used, for the first time, a stochastic autoregressive model, the concepts of which match those associated with physiological systems involved and applied in male HFD rats compared with their appropriate standard food intake age-matched male controls (n=7 per group). By analyzing a recorded temperature time series, we were able to identify when thermal homeostasis would be affected by a new diet. The autoregressive time series model (AR model) was used to predict the occurrence of thermal homeostasis, and this model proved to be very effective in distinguishing such a physiological disorder. Thus, we infer from the results of our study that maximum entropy distribution as a means for stochastic characterization of temperature time series registers may be established as an important and early tool to aid in the diagnosis and prevention of metabolic diseases due to their ability to detect small variations in thermal profile

  20. Multivariate statistical process control of a continuous pharmaceutical twin-screw granulation and fluid bed drying process.

    Science.gov (United States)

    Silva, A F; Sarraguça, M C; Fonteyne, M; Vercruysse, J; De Leersnyder, F; Vanhoorne, V; Bostijn, N; Verstraeten, M; Vervaet, C; Remon, J P; De Beer, T; Lopes, J A

    2017-08-07

    A multivariate statistical process control (MSPC) strategy was developed for the monitoring of the ConsiGma™-25 continuous tablet manufacturing line. Thirty-five logged variables encompassing three major units, being a twin screw high shear granulator, a fluid bed dryer and a product control unit, were used to monitor the process. The MSPC strategy was based on principal component analysis of data acquired under normal operating conditions using a series of four process runs. Runs with imposed disturbances in the dryer air flow and temperature, in the granulator barrel temperature, speed and liquid mass flow and in the powder dosing unit mass flow were utilized to evaluate the model's monitoring performance. The impact of the imposed deviations to the process continuity was also evaluated using Hotelling's T 2 and Q residuals statistics control charts. The influence of the individual process variables was assessed by analyzing contribution plots at specific time points. Results show that the imposed disturbances were all detected in both control charts. Overall, the MSPC strategy was successfully developed and applied. Additionally, deviations not associated with the imposed changes were detected, mainly in the granulator barrel temperature control. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Effects of spike-time-dependent plasticity on the stochastic resonance of small-world neuronal networks

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Haitao; Guo, Xinmeng; Wang, Jiang, E-mail: jiangwang@tju.edu.cn; Deng, Bin; Wei, Xile [School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China)

    2014-09-01

    The phenomenon of stochastic resonance in Newman-Watts small-world neuronal networks is investigated when the strength of synaptic connections between neurons is adaptively adjusted by spike-time-dependent plasticity (STDP). It is shown that irrespective of the synaptic connectivity is fixed or adaptive, the phenomenon of stochastic resonance occurs. The efficiency of network stochastic resonance can be largely enhanced by STDP in the coupling process. Particularly, the resonance for adaptive coupling can reach a much larger value than that for fixed one when the noise intensity is small or intermediate. STDP with dominant depression and small temporal window ratio is more efficient for the transmission of weak external signal in small-world neuronal networks. In addition, we demonstrate that the effect of stochastic resonance can be further improved via fine-tuning of the average coupling strength of the adaptive network. Furthermore, the small-world topology can significantly affect stochastic resonance of excitable neuronal networks. It is found that there exists an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks.

  2. Effects of spike-time-dependent plasticity on the stochastic resonance of small-world neuronal networks

    International Nuclear Information System (INIS)

    Yu, Haitao; Guo, Xinmeng; Wang, Jiang; Deng, Bin; Wei, Xile

    2014-01-01

    The phenomenon of stochastic resonance in Newman-Watts small-world neuronal networks is investigated when the strength of synaptic connections between neurons is adaptively adjusted by spike-time-dependent plasticity (STDP). It is shown that irrespective of the synaptic connectivity is fixed or adaptive, the phenomenon of stochastic resonance occurs. The efficiency of network stochastic resonance can be largely enhanced by STDP in the coupling process. Particularly, the resonance for adaptive coupling can reach a much larger value than that for fixed one when the noise intensity is small or intermediate. STDP with dominant depression and small temporal window ratio is more efficient for the transmission of weak external signal in small-world neuronal networks. In addition, we demonstrate that the effect of stochastic resonance can be further improved via fine-tuning of the average coupling strength of the adaptive network. Furthermore, the small-world topology can significantly affect stochastic resonance of excitable neuronal networks. It is found that there exists an optimal probability of adding links by which the noise-induced transmission of weak periodic signal peaks

  3. A multivariate time series approach to modeling and forecasting demand in the emergency department.

    Science.gov (United States)

    Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L

    2009-02-01

    The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

  4. Relative Error Model Reduction via Time-Weighted Balanced Stochastic Singular Perturbation

    DEFF Research Database (Denmark)

    Tahavori, Maryamsadat; Shaker, Hamid Reza

    2012-01-01

    A new mixed method for relative error model reduction of linear time invariant (LTI) systems is proposed in this paper. This order reduction technique is mainly based upon time-weighted balanced stochastic model reduction method and singular perturbation model reduction technique. Compared...... by using the concept and properties of the reciprocal systems. The results are further illustrated by two practical numerical examples: a model of CD player and a model of the atmospheric storm track....

  5. Statistical inference for discrete-time samples from affine stochastic delay differential equations

    DEFF Research Database (Denmark)

    Küchler, Uwe; Sørensen, Michael

    2013-01-01

    Statistical inference for discrete time observations of an affine stochastic delay differential equation is considered. The main focus is on maximum pseudo-likelihood estimators, which are easy to calculate in practice. A more general class of prediction-based estimating functions is investigated...

  6. Essentials of stochastic processes

    CERN Document Server

    Durrett, Richard

    2016-01-01

    Building upon the previous editions, this textbook is a first course in stochastic processes taken by undergraduate and graduate students (MS and PhD students from math, statistics, economics, computer science, engineering, and finance departments) who have had a course in probability theory. It covers Markov chains in discrete and continuous time, Poisson processes, renewal processes, martingales, and option pricing. One can only learn a subject by seeing it in action, so there are a large number of examples and more than 300 carefully chosen exercises to deepen the reader’s understanding. Drawing from teaching experience and student feedback, there are many new examples and problems with solutions that use TI-83 to eliminate the tedious details of solving linear equations by hand, and the collection of exercises is much improved, with many more biological examples. Originally included in previous editions, material too advanced for this first course in stochastic processes has been eliminated while treatm...

  7. Expressing stochastic unravellings using random evolution operators

    International Nuclear Information System (INIS)

    Salgado, D; Sanchez-Gomez, J L

    2002-01-01

    We prove how the form of the most general invariant stochastic unravelling for Markovian (recently given in the literature by Wiseman and Diosi) and non-Markovian but Lindblad-type open quantum systems can be attained by imposing a single mathematical condition upon the random evolution operator of the system, namely a.s. trace preservation (a.s. stands for almost surely). The use of random operators ensures the complete positivity of the density operator evolution and characterizes the linear/non-linear character of the evolution in a straightforward way. It is also shown how three quantum stochastic evolution models - continuous spontaneous localization, quantum state diffusion and quantum mechanics with universal position localization - appear as concrete choices for the noise term of the evolution random operators are assumed. We finally conjecture how these operators may in the future be used in two different directions: both to connect quantum stochastic evolution models with random properties of space-time and to handle noisy quantum logical gates

  8. ParPor: Particles in Pores. Stochastic Modeling of Polydisperse Transport

    DEFF Research Database (Denmark)

    Yuan, Hao

    2010-01-01

    Liquid flow containing particles in the different types of porous media appear in a large variety of practically important industrial and natural processes. The project aims at developing a stochastic model for the deep bed filtration process in which the polydisperse suspension flow...... in the polydisperse porous media. Instead of the traditional parabolic Advection-Dispersion Equation (ADE) the novel elliptic PDE based on the Continuous Time Random Walk is adopted for the particle size kinetics. The pore kinetics is either described by the stochastic size exclusion mechanism or the incomplete pore...

  9. Mean Field Games for Stochastic Growth with Relative Utility

    Energy Technology Data Exchange (ETDEWEB)

    Huang, Minyi, E-mail: mhuang@math.carleton.ca [Carleton University, School of Mathematics and Statistics (Canada); Nguyen, Son Luu, E-mail: sonluu.nguyen@upr.edu [University of Puerto Rico, Department of Mathematics (United States)

    2016-12-15

    This paper considers continuous time stochastic growth-consumption optimization in a mean field game setting. The individual capital stock evolution is determined by a Cobb–Douglas production function, consumption and stochastic depreciation. The individual utility functional combines an own utility and a relative utility with respect to the population. The use of the relative utility reflects human psychology, leading to a natural pattern of mean field interaction. The fixed point equation of the mean field game is derived with the aid of some ordinary differential equations. Due to the relative utility interaction, our performance analysis depends on some ratio based approximation error estimate.

  10. Mean Field Games for Stochastic Growth with Relative Utility

    International Nuclear Information System (INIS)

    Huang, Minyi; Nguyen, Son Luu

    2016-01-01

    This paper considers continuous time stochastic growth-consumption optimization in a mean field game setting. The individual capital stock evolution is determined by a Cobb–Douglas production function, consumption and stochastic depreciation. The individual utility functional combines an own utility and a relative utility with respect to the population. The use of the relative utility reflects human psychology, leading to a natural pattern of mean field interaction. The fixed point equation of the mean field game is derived with the aid of some ordinary differential equations. Due to the relative utility interaction, our performance analysis depends on some ratio based approximation error estimate.

  11. Long-time analytic approximation of large stochastic oscillators: Simulation, analysis and inference.

    Directory of Open Access Journals (Sweden)

    Giorgos Minas

    2017-07-01

    Full Text Available In order to analyse large complex stochastic dynamical models such as those studied in systems biology there is currently a great need for both analytical tools and also algorithms for accurate and fast simulation and estimation. We present a new stochastic approximation of biological oscillators that addresses these needs. Our method, called phase-corrected LNA (pcLNA overcomes the main limitations of the standard Linear Noise Approximation (LNA to remain uniformly accurate for long times, still maintaining the speed and analytically tractability of the LNA. As part of this, we develop analytical expressions for key probability distributions and associated quantities, such as the Fisher Information Matrix and Kullback-Leibler divergence and we introduce a new approach to system-global sensitivity analysis. We also present algorithms for statistical inference and for long-term simulation of oscillating systems that are shown to be as accurate but much faster than leaping algorithms and algorithms for integration of diffusion equations. Stochastic versions of published models of the circadian clock and NF-κB system are used to illustrate our results.

  12. Stochastic volatility and stochastic leverage

    DEFF Research Database (Denmark)

    Veraart, Almut; Veraart, Luitgard A. M.

    This paper proposes the new concept of stochastic leverage in stochastic volatility models. Stochastic leverage refers to a stochastic process which replaces the classical constant correlation parameter between the asset return and the stochastic volatility process. We provide a systematic...... treatment of stochastic leverage and propose to model the stochastic leverage effect explicitly, e.g. by means of a linear transformation of a Jacobi process. Such models are both analytically tractable and allow for a direct economic interpretation. In particular, we propose two new stochastic volatility...... models which allow for a stochastic leverage effect: the generalised Heston model and the generalised Barndorff-Nielsen & Shephard model. We investigate the impact of a stochastic leverage effect in the risk neutral world by focusing on implied volatilities generated by option prices derived from our new...

  13. Stochastic Reachability Analysis of Hybrid Systems

    CERN Document Server

    Bujorianu, Luminita Manuela

    2012-01-01

    Stochastic reachability analysis (SRA) is a method of analyzing the behavior of control systems which mix discrete and continuous dynamics. For probabilistic discrete systems it has been shown to be a practical verification method but for stochastic hybrid systems it can be rather more. As a verification technique SRA can assess the safety and performance of, for example, autonomous systems, robot and aircraft path planning and multi-agent coordination but it can also be used for the adaptive control of such systems. Stochastic Reachability Analysis of Hybrid Systems is a self-contained and accessible introduction to this novel topic in the analysis and development of stochastic hybrid systems. Beginning with the relevant aspects of Markov models and introducing stochastic hybrid systems, the book then moves on to coverage of reachability analysis for stochastic hybrid systems. Following this build up, the core of the text first formally defines the concept of reachability in the stochastic framework and then...

  14. An Application of a Stochastic Semi-Continuous Simulation Method for Flood Frequency Analysis: A Case Study in Slovakia

    Science.gov (United States)

    Valent, Peter; Paquet, Emmanuel

    2017-09-01

    A reliable estimate of extreme flood characteristics has always been an active topic in hydrological research. Over the decades a large number of approaches and their modifications have been proposed and used, with various methods utilizing continuous simulation of catchment runoff, being the subject of the most intensive research in the last decade. In this paper a new and promising stochastic semi-continuous method is used to estimate extreme discharges in two mountainous Slovak catchments of the rivers Váh and Hron, in which snow-melt processes need to be taken into account. The SCHADEX method used, couples a precipitation probabilistic model with a rainfall-runoff model used to both continuously simulate catchment hydrological conditions and to transform generated synthetic rainfall events into corresponding discharges. The stochastic nature of the method means that a wide range of synthetic rainfall events were simulated on various historical catchment conditions, taking into account not only the saturation of soil, but also the amount of snow accumulated in the catchment. The results showed that the SCHADEX extreme discharge estimates with return periods of up to 100 years were comparable to those estimated by statistical approaches. In addition, two reconstructed historical floods with corresponding return periods of 100 and 1000 years were compared to the SCHADEX estimates. The results confirmed the usability of the method for estimating design discharges with a recurrence interval of more than 100 years and its applicability in Slovak conditions.

  15. Jumps and stochastic volatility in oil prices: Time series evidence

    International Nuclear Information System (INIS)

    Larsson, Karl; Nossman, Marcus

    2011-01-01

    In this paper we examine the empirical performance of affine jump diffusion models with stochastic volatility in a time series study of crude oil prices. We compare four different models and estimate them using the Markov Chain Monte Carlo method. The support for a stochastic volatility model including jumps in both prices and volatility is strong and the model clearly outperforms the others in terms of a superior fit to data. Our estimation method allows us to obtain a detailed study of oil prices during two periods of extreme market stress included in our sample; the Gulf war and the recent financial crisis. We also address the economic significance of model choice in two option pricing applications. The implied volatilities generated by the different estimated models are compared and we price a real option to develop an oil field. Our findings indicate that model choice can have a material effect on the option values.

  16. Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times.

    Science.gov (United States)

    Yang, Xin; Zeng, Zhenxiang; Wang, Ruidong; Sun, Xueshan

    2016-01-01

    This paper presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems.

  17. Stochastic population and epidemic models persistence and extinction

    CERN Document Server

    Allen, Linda J S

    2015-01-01

    This monograph provides a summary of the basic theory of branching processes for single-type and multi-type processes. Classic examples of population and epidemic models illustrate the probability of population or epidemic extinction obtained from the theory of branching processes. The first chapter develops the branching process theory, while in the second chapter two applications to population and epidemic processes of single-type branching process theory are explored. The last two chapters present multi-type branching process applications to epidemic models, and then continuous-time and continuous-state branching processes with applications. In addition, several MATLAB programs for simulating stochastic sample paths  are provided in an Appendix. These notes originated as part of a lecture series on Stochastics in Biological Systems at the Mathematical Biosciences Institute in Ohio, USA. Professor Linda Allen is a Paul Whitfield Horn Professor of Mathematics in the Department of Mathematics and Statistics ...

  18. ARIMA-Based Time Series Model of Stochastic Wind Power Generation

    DEFF Research Database (Denmark)

    Chen, Peiyuan; Pedersen, Troels; Bak-Jensen, Birgitte

    2010-01-01

    This paper proposes a stochastic wind power model based on an autoregressive integrated moving average (ARIMA) process. The model takes into account the nonstationarity and physical limits of stochastic wind power generation. The model is constructed based on wind power measurement of one year from...... the Nysted offshore wind farm in Denmark. The proposed limited-ARIMA (LARIMA) model introduces a limiter and characterizes the stochastic wind power generation by mean level, temporal correlation and driving noise. The model is validated against the measurement in terms of temporal correlation...... and probability distribution. The LARIMA model outperforms a first-order transition matrix based discrete Markov model in terms of temporal correlation, probability distribution and model parameter number. The proposed LARIMA model is further extended to include the monthly variation of the stochastic wind power...

  19. Biochemical Network Stochastic Simulator (BioNetS: software for stochastic modeling of biochemical networks

    Directory of Open Access Journals (Sweden)

    Elston Timothy C

    2004-03-01

    Full Text Available Abstract Background Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. Results We have developed the software package Biochemical Network Stochastic Simulator (BioNetS for efficientlyand accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solvesthe appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. Conclusions We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.

  20. Multimodal Network Equilibrium with Stochastic Travel Times

    Directory of Open Access Journals (Sweden)

    M. Meng

    2014-01-01

    Full Text Available The private car, unlike public traffic modes (e.g., subway, trolley running along dedicated track-ways, is invariably subject to various uncertainties resulting in travel time variation. A multimodal network equilibrium model is formulated that explicitly considers stochastic link capacity variability in the road network. The travel time of combined-mode trips is accumulated based on the concept of the mean excess travel time (METT which is a summation of estimated buffer time and tardy time. The problem is characterized by an equivalent VI (variational inequality formulation where the mode choice is expressed in a hierarchical logit structure. Specifically, the supernetwork theory and expansion technique are used herein to represent the multimodal transportation network, which completely represents the combined-mode trips as constituting multiple modes within a trip. The method of successive weighted average is adopted for problem solutions. The model and solution method are further applied to study the trip distribution and METT variations caused by the different levels of the road conditions. Results of numerical examples show that travelers prefer to choose the combined travel mode as road capacity decreases. Travelers with different attitudes towards risk are shown to exhibit significant differences when making travel choice decisions.

  1. Beam life-time with intrabeam scattering and stochastic cooling

    International Nuclear Information System (INIS)

    Wei, J.; Ruggiero, A.G.

    1991-01-01

    A transport equation has been derived in terms of the longitudinal action variable to describe the time evolution of the longitudinal density distribution of a bunched hadron beam in the presence of intrabeam scattering and stochastic cooling. A computer program has been developed to numerically solve this equation. Both beam loss and bunch-shape evolution have been investigated for the 197 Au 79+ beams during the 10-hour storage in the Relativistic Heavy Ion Collider currently under construction at the Brookhaven National Laboratory. 9 refs., 1 fig

  2. Long-Time Dynamic Response and Stochastic Resonance of Subdiffusive Overdamped Bistable Fractional Fokker-Planck Systems

    International Nuclear Information System (INIS)

    Yan-Mei, Kang; Yao-Lin, Jiang

    2008-01-01

    To explore the influence of anomalous diffusion on stochastic resonance (SR) more deeply and effectively, the method of moments is extended to subdiffusive overdamped bistable fractional Fokker-Planck systems for calculating the long-time linear dynamic response. It is found that the method of moments attains high accuracy with the truncation order N = 10, and in normal diffusion such obtained spectral amplification factor (SAF) of the first-order harmonic is also confirmed by stochastic simulation. Observing the SAF of the odd-order harmonics we find some interesting results, i.e. for smaller driving frequency the decrease of sub diffusion exponent inhibits the stochastic resonance (SR), while for larger driving frequency the decrease of sub diffusion exponent enhances the second SR peak, but the first one vanishes and a double SR is induced in the third-order harmonic at the same time. These observations suggest that the anomalous diffusion has important influence on the bistable dynamics

  3. Stochastic TDHF and the Boltzman-Langevin equation

    International Nuclear Information System (INIS)

    Suraud, E.; Reinhard, P.G.

    1991-01-01

    Outgoing from a time-dependent theory of correlations, we present a stochastic differential equation for the propagation of ensembles of Slater determinants, called Stochastic Time-Dependent Hartree-Fock (Stochastic TDHF). These ensembles are allowed to develop large fluctuations in the Hartree-Fock mean fields. An alternative stochastic differential equation, the Boltzmann-Langevin equation, can be derived from Stochastic TDHF by averaging over subensembles with small fluctuations

  4. A theoretically consistent stochastic cascade for temporal disaggregation of intermittent rainfall

    Science.gov (United States)

    Lombardo, F.; Volpi, E.; Koutsoyiannis, D.; Serinaldi, F.

    2017-06-01

    Generating fine-scale time series of intermittent rainfall that are fully consistent with any given coarse-scale totals is a key and open issue in many hydrological problems. We propose a stationary disaggregation method that simulates rainfall time series with given dependence structure, wet/dry probability, and marginal distribution at a target finer (lower-level) time scale, preserving full consistency with variables at a parent coarser (higher-level) time scale. We account for the intermittent character of rainfall at fine time scales by merging a discrete stochastic representation of intermittency and a continuous one of rainfall depths. This approach yields a unique and parsimonious mathematical framework providing general analytical formulations of mean, variance, and autocorrelation function (ACF) for a mixed-type stochastic process in terms of mean, variance, and ACFs of both continuous and discrete components, respectively. To achieve the full consistency between variables at finer and coarser time scales in terms of marginal distribution and coarse-scale totals, the generated lower-level series are adjusted according to a procedure that does not affect the stochastic structure implied by the original model. To assess model performance, we study rainfall process as intermittent with both independent and dependent occurrences, where dependence is quantified by the probability that two consecutive time intervals are dry. In either case, we provide analytical formulations of main statistics of our mixed-type disaggregation model and show their clear accordance with Monte Carlo simulations. An application to rainfall time series from real world is shown as a proof of concept.

  5. Stochastic optimization: beyond mathematical programming

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    Stochastic optimization, among which bio-inspired algorithms, is gaining momentum in areas where more classical optimization algorithms fail to deliver satisfactory results, or simply cannot be directly applied. This presentation will introduce baseline stochastic optimization algorithms, and illustrate their efficiency in different domains, from continuous non-convex problems to combinatorial optimization problem, to problems for which a non-parametric formulation can help exploring unforeseen possible solution spaces.

  6. The continuous time random walk, still trendy: fifty-year history, state of art and outlook

    Science.gov (United States)

    Kutner, Ryszard; Masoliver, Jaume

    2017-03-01

    In this article we demonstrate the very inspiring role of the continuous-time random walk (CTRW) formalism, the numerous modifications permitted by its flexibility, its various applications, and the promising perspectives in the various fields of knowledge. A short review of significant achievements and possibilities is given. However, this review is still far from completeness. We focused on a pivotal role of CTRWs mainly in anomalous stochastic processes discovered in physics and beyond. This article plays the role of an extended announcement of the Eur. Phys. J. B Special Issue [open-calls-for-papers/123-epj-b/1090-ctrw-50-years-on">http://epjb.epj.org/open-calls-for-papers/123-epj-b/1090-ctrw-50-years-on] containing articles which show incredible possibilities of the CTRWs. Contribution to the Topical Issue "Continuous Time Random Walk Still Trendy: Fifty-year History, Current State and Outlook", edited by Ryszard Kutner and Jaume Masoliver.

  7. Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods

    KAUST Repository

    Loizou, Nicolas

    2017-12-27

    In this paper we study several classes of stochastic optimization algorithms enriched with heavy ball momentum. Among the methods studied are: stochastic gradient descent, stochastic Newton, stochastic proximal point and stochastic dual subspace ascent. This is the first time momentum variants of several of these methods are studied. We choose to perform our analysis in a setting in which all of the above methods are equivalent. We prove global nonassymptotic linear convergence rates for all methods and various measures of success, including primal function values, primal iterates (in L2 sense), and dual function values. We also show that the primal iterates converge at an accelerated linear rate in the L1 sense. This is the first time a linear rate is shown for the stochastic heavy ball method (i.e., stochastic gradient descent method with momentum). Under somewhat weaker conditions, we establish a sublinear convergence rate for Cesaro averages of primal iterates. Moreover, we propose a novel concept, which we call stochastic momentum, aimed at decreasing the cost of performing the momentum step. We prove linear convergence of several stochastic methods with stochastic momentum, and show that in some sparse data regimes and for sufficiently small momentum parameters, these methods enjoy better overall complexity than methods with deterministic momentum. Finally, we perform extensive numerical testing on artificial and real datasets, including data coming from average consensus problems.

  8. Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods

    KAUST Repository

    Loizou, Nicolas; Richtarik, Peter

    2017-01-01

    In this paper we study several classes of stochastic optimization algorithms enriched with heavy ball momentum. Among the methods studied are: stochastic gradient descent, stochastic Newton, stochastic proximal point and stochastic dual subspace ascent. This is the first time momentum variants of several of these methods are studied. We choose to perform our analysis in a setting in which all of the above methods are equivalent. We prove global nonassymptotic linear convergence rates for all methods and various measures of success, including primal function values, primal iterates (in L2 sense), and dual function values. We also show that the primal iterates converge at an accelerated linear rate in the L1 sense. This is the first time a linear rate is shown for the stochastic heavy ball method (i.e., stochastic gradient descent method with momentum). Under somewhat weaker conditions, we establish a sublinear convergence rate for Cesaro averages of primal iterates. Moreover, we propose a novel concept, which we call stochastic momentum, aimed at decreasing the cost of performing the momentum step. We prove linear convergence of several stochastic methods with stochastic momentum, and show that in some sparse data regimes and for sufficiently small momentum parameters, these methods enjoy better overall complexity than methods with deterministic momentum. Finally, we perform extensive numerical testing on artificial and real datasets, including data coming from average consensus problems.

  9. Stochastic processes and quantum theory

    International Nuclear Information System (INIS)

    Klauder, J.R.

    1975-01-01

    The author analyses a variety of stochastic processes, namely real time diffusion phenomena, which are analogues of imaginary time quantum theory and convariant imaginary time quantum field theory. He elaborates some standard properties involving probability measures and stochastic variables and considers a simple class of examples. Finally he develops the fact that certain stochastic theories actually exhibit divergences that simulate those of covariant quantum field theory and presents examples of both renormaizable and unrenormalizable behavior. (V.J.C.)

  10. Stochastic models for structured populations scaling limits and long time behavior

    CERN Document Server

    Meleard, Sylvie

    2015-01-01

    In this contribution, several probabilistic tools to study population dynamics are developed. The focus is on scaling limits of qualitatively different stochastic individual based models and the long time behavior of some classes of limiting processes. Structured population dynamics are modeled by measure-valued processes describing the individual behaviors and taking into account the demographic and mutational parameters, and possible interactions between individuals. Many quantitative parameters appear in these models and several relevant normalizations are considered, leading  to infinite-dimensional deterministic or stochastic large-population approximations. Biologically relevant questions are considered, such as extinction criteria, the effect of large birth events, the impact of  environmental catastrophes, the mutation-selection trade-off, recovery criteria in parasite infections, genealogical properties of a sample of individuals. These notes originated from a lecture series on Structured P...

  11. Stochastic Dynamics of a Time-Delayed Ecosystem Driven by Poisson White Noise Excitation

    Directory of Open Access Journals (Sweden)

    Wantao Jia

    2018-02-01

    Full Text Available We investigate the stochastic dynamics of a prey-predator type ecosystem with time delay and the discrete random environmental fluctuations. In this model, the delay effect is represented by a time delay parameter and the effect of the environmental randomness is modeled as Poisson white noise. The stochastic averaging method and the perturbation method are applied to calculate the approximate stationary probability density functions for both predator and prey populations. The influences of system parameters and the Poisson white noises are investigated in detail based on the approximate stationary probability density functions. It is found that, increasing time delay parameter as well as the mean arrival rate and the variance of the amplitude of the Poisson white noise will enhance the fluctuations of the prey and predator population. While the larger value of self-competition parameter will reduce the fluctuation of the system. Furthermore, the results from Monte Carlo simulation are also obtained to show the effectiveness of the results from averaging method.

  12. Stochastic Generalized Method of Moments

    KAUST Repository

    Yin, Guosheng; Ma, Yanyuan; Liang, Faming; Yuan, Ying

    2011-01-01

    The generalized method of moments (GMM) is a very popular estimation and inference procedure based on moment conditions. When likelihood-based methods are difficult to implement, one can often derive various moment conditions and construct the GMM objective function. However, minimization of the objective function in the GMM may be challenging, especially over a large parameter space. Due to the special structure of the GMM, we propose a new sampling-based algorithm, the stochastic GMM sampler, which replaces the multivariate minimization problem by a series of conditional sampling procedures. We develop the theoretical properties of the proposed iterative Monte Carlo method, and demonstrate its superior performance over other GMM estimation procedures in simulation studies. As an illustration, we apply the stochastic GMM sampler to a Medfly life longevity study. Supplemental materials for the article are available online. © 2011 American Statistical Association.

  13. Stochastic Generalized Method of Moments

    KAUST Repository

    Yin, Guosheng

    2011-08-16

    The generalized method of moments (GMM) is a very popular estimation and inference procedure based on moment conditions. When likelihood-based methods are difficult to implement, one can often derive various moment conditions and construct the GMM objective function. However, minimization of the objective function in the GMM may be challenging, especially over a large parameter space. Due to the special structure of the GMM, we propose a new sampling-based algorithm, the stochastic GMM sampler, which replaces the multivariate minimization problem by a series of conditional sampling procedures. We develop the theoretical properties of the proposed iterative Monte Carlo method, and demonstrate its superior performance over other GMM estimation procedures in simulation studies. As an illustration, we apply the stochastic GMM sampler to a Medfly life longevity study. Supplemental materials for the article are available online. © 2011 American Statistical Association.

  14. Optimal Control for Stochastic Delay Evolution Equations

    Energy Technology Data Exchange (ETDEWEB)

    Meng, Qingxin, E-mail: mqx@hutc.zj.cn [Huzhou University, Department of Mathematical Sciences (China); Shen, Yang, E-mail: skyshen87@gmail.com [York University, Department of Mathematics and Statistics (Canada)

    2016-08-15

    In this paper, we investigate a class of infinite-dimensional optimal control problems, where the state equation is given by a stochastic delay evolution equation with random coefficients, and the corresponding adjoint equation is given by an anticipated backward stochastic evolution equation. We first prove the continuous dependence theorems for stochastic delay evolution equations and anticipated backward stochastic evolution equations, and show the existence and uniqueness of solutions to anticipated backward stochastic evolution equations. Then we establish necessary and sufficient conditions for optimality of the control problem in the form of Pontryagin’s maximum principles. To illustrate the theoretical results, we apply stochastic maximum principles to study two examples, an infinite-dimensional linear-quadratic control problem with delay and an optimal control of a Dirichlet problem for a stochastic partial differential equation with delay. Further applications of the two examples to a Cauchy problem for a controlled linear stochastic partial differential equation and an optimal harvesting problem are also considered.

  15. Characteristic functions of scale mixtures of multivariate skew-normal distributions

    KAUST Repository

    Kim, Hyoung-Moon

    2011-08-01

    We obtain the characteristic function of scale mixtures of skew-normal distributions both in the univariate and multivariate cases. The derivation uses the simple stochastic relationship between skew-normal distributions and scale mixtures of skew-normal distributions. In particular, we describe the characteristic function of skew-normal, skew-t, and other related distributions. © 2011 Elsevier Inc.

  16. Testing for Volatility Co-movement in Bivariate Stochastic Volatility Models

    NARCIS (Netherlands)

    J. Chen (Jinghui); M. Kobayashi (Masahito); M.J. McAleer (Michael)

    2017-01-01

    markdownabstractThe paper considers the problem of volatility co-movement, namely as to whether two financial returns have perfectly correlated common volatility process, in the framework of multivariate stochastic volatility models and proposes a test which checks the volatility co-movement. The

  17. SATA II - Stochastic Algebraic Topology and Applications

    Science.gov (United States)

    2017-01-30

    AFRL-AFOSR-UK-TR-2017-0018 SATA II - Stochastic Algebraic Topology and Applications 150032 Robert Adler TECHNION ISRAEL INSTITUTE OF TECHNOLOGY Final...REPORT TYPE Final 3. DATES COVERED (From - To) 15 Dec 2014 to 14 Dec 2016 4. TITLE AND SUBTITLE SATA II - Stochastic Algebraic Topology and Applications... Topology and Applications Continuation of, and associated with SATA: Stochastic Algebraic Topology and Applications FA8655-11-1-3039, 09/1/2011–08/31/2014

  18. On time-dependent diffusion coefficients arising from stochastic processes with memory

    Science.gov (United States)

    Carpio-Bernido, M. Victoria; Barredo, Wilson I.; Bernido, Christopher C.

    2017-08-01

    Time-dependent diffusion coefficients arise from anomalous diffusion encountered in many physical systems such as protein transport in cells. We compare these coefficients with those arising from analysis of stochastic processes with memory that go beyond fractional Brownian motion. Facilitated by the Hida white noise functional integral approach, diffusion propagators or probability density functions (pdf) are obtained and shown to be solutions of modified diffusion equations with time-dependent diffusion coefficients. This should be useful in the study of complex transport processes.

  19. Continuous-time quantum Monte Carlo impurity solvers

    Science.gov (United States)

    Gull, Emanuel; Werner, Philipp; Fuchs, Sebastian; Surer, Brigitte; Pruschke, Thomas; Troyer, Matthias

    2011-04-01

    representations of quantum dots and molecular conductors and play an increasingly important role in the theory of "correlated electron" materials as auxiliary problems whose solution gives the "dynamical mean field" approximation to the self-energy and local correlation functions. Solution method: Quantum impurity models require a method of solution which provides access to both high and low energy scales and is effective for wide classes of physically realistic models. The continuous-time quantum Monte Carlo algorithms for which we present implementations here meet this challenge. Continuous-time quantum impurity methods are based on partition function expansions of quantum impurity models that are stochastically sampled to all orders using diagrammatic quantum Monte Carlo techniques. For a review of quantum impurity models and their applications and of continuous-time quantum Monte Carlo methods for impurity models we refer the reader to [2]. Additional comments: Use of dmft requires citation of this paper. Use of any ALPS program requires citation of the ALPS [1] paper. Running time: 60 s-8 h per iteration.

  20. Fixation and escape times in stochastic game learning

    International Nuclear Information System (INIS)

    Realpe-Gomez, John; Szczesny, Bartosz; Galla, Tobias; Dall’Asta, Luca

    2012-01-01

    Evolutionary dynamics in finite populations is known to fixate eventually in the absence of mutation. We here show that a similar phenomenon can be found in stochastic game dynamical batch learning, and investigate fixation in learning processes in a simple 2×2 game, for two-player games with cyclic interaction, and in the context of the best-shot network game. The analogues of finite populations in evolution are here finite batches of observations between strategy updates. We study when and how such fixation can occur, and present results on the average time-to-fixation from numerical simulations. Simple cases are also amenable to analytical approaches and we provide estimates of the behaviour of so-called escape times as a function of the batch size. The differences and similarities with escape and fixation in evolutionary dynamics are discussed. (paper)

  1. Stochastic first passage time accelerated with CUDA

    Science.gov (United States)

    Pierro, Vincenzo; Troiano, Luigi; Mejuto, Elena; Filatrella, Giovanni

    2018-05-01

    The numerical integration of stochastic trajectories to estimate the time to pass a threshold is an interesting physical quantity, for instance in Josephson junctions and atomic force microscopy, where the full trajectory is not accessible. We propose an algorithm suitable for efficient implementation on graphical processing unit in CUDA environment. The proposed approach for well balanced loads achieves almost perfect scaling with the number of available threads and processors, and allows an acceleration of about 400× with a GPU GTX980 respect to standard multicore CPU. This method allows with off the shell GPU to challenge problems that are otherwise prohibitive, as thermal activation in slowly tilted potentials. In particular, we demonstrate that it is possible to simulate the switching currents distributions of Josephson junctions in the timescale of actual experiments.

  2. The stochastic dynamics of intermittent porescale particle motion

    Science.gov (United States)

    Dentz, Marco; Morales, Veronica; Puyguiraud, Alexandre; Gouze, Philippe; Willmann, Matthias; Holzner, Markus

    2017-04-01

    Numerical and experimental data for porescale particle dynamics show intermittent patterns in Lagrangian velocities and accelerations, which manifest in long time intervals of low and short durations of high velocities [1, 2]. This phenomenon is due to the spatial persistence of particle velocities on characteristic heterogeneity length scales. In order to systematically quantify these behaviors and extract the stochastic dynamics of particle motion, we focus on the analysis of Lagrangian velocities sampled equidistantly along trajectories [3]. This method removes the intermittency observed under isochrone sampling. The space-Lagrangian velocity series can be quantified by a Markov process that is continuous in distance along streamline. It is fully parameterized in terms of the flux-weighted Eulerian velocity PDF and the characteristic pore-length. The resulting stochastic particle motion describes a continuous time random walk (CTRW). This approach allows for the process based interpretation of experimental and numerical porescale velocity, acceleration and displacement data. It provides a framework for the characterization and upscaling of particle transport and dispersion from the pore to the Darcy-scale based on the medium geometry and Eulerian flow attributes. [1] P. De Anna, T. Le Borgne, M. Dentz, A.M. Tartakovsky, D. Bolster, and P. Davy, "Flow intermittency, dispersion, and correlated continuous time random walks in porous media," Phys. Rev. Lett. 110, 184502 (2013). [2] M. Holzner, V. L. Morales, M. Willmann, and M. Dentz, "Intermittent Lagrangian velocities and accelerations in three- dimensional porous medium flow," Phys. Rev. E 92, 013015 (2015). [3] M. Dentz, P. K. Kang, A. Comolli, T. Le Borgne, and D. R. Lester, "Continuous time random walks for the evolution of Lagrangian velocities," Phys. Rev. Fluids (2016).

  3. Multivariate Option Pricing with Time Varying Volatility and Correlations

    DEFF Research Database (Denmark)

    Rombouts, Jeroen V.K.; Stentoft, Lars Peter

    In recent years multivariate models for asset returns have received much attention, in particular this is the case for models with time varying volatility. In this paper we consider models of this class and examine their potential when it comes to option pricing. Specifically, we derive the risk...... neutral dynamics for a general class of multivariate heteroskedastic models, and we provide a feasible way to price options in this framework. Our framework can be used irrespective of the assumed underlying distribution and dynamics, and it nests several important special cases. We provide an application...... to options on the minimum of two indices. Our results show that not only is correlation important for these options but so is allowing this correlation to be dynamic. Moreover, we show that for the general model exposure to correlation risk carries an important premium, and when this is neglected option...

  4. The time dependent propensity function for acceleration of spatial stochastic simulation of reaction–diffusion systems

    International Nuclear Information System (INIS)

    Fu, Jin; Wu, Sheng; Li, Hong; Petzold, Linda R.

    2014-01-01

    The inhomogeneous stochastic simulation algorithm (ISSA) is a fundamental method for spatial stochastic simulation. However, when diffusion events occur more frequently than reaction events, simulating the diffusion events by ISSA is quite costly. To reduce this cost, we propose to use the time dependent propensity function in each step. In this way we can avoid simulating individual diffusion events, and use the time interval between two adjacent reaction events as the simulation stepsize. We demonstrate that the new algorithm can achieve orders of magnitude efficiency gains over widely-used exact algorithms, scales well with increasing grid resolution, and maintains a high level of accuracy

  5. Stochastic modeling analysis and simulation

    CERN Document Server

    Nelson, Barry L

    1995-01-01

    A coherent introduction to the techniques for modeling dynamic stochastic systems, this volume also offers a guide to the mathematical, numerical, and simulation tools of systems analysis. Suitable for advanced undergraduates and graduate-level industrial engineers and management science majors, it proposes modeling systems in terms of their simulation, regardless of whether simulation is employed for analysis. Beginning with a view of the conditions that permit a mathematical-numerical analysis, the text explores Poisson and renewal processes, Markov chains in discrete and continuous time, se

  6. Mathematical statistics and stochastic processes

    CERN Document Server

    Bosq, Denis

    2013-01-01

    Generally, books on mathematical statistics are restricted to the case of independent identically distributed random variables. In this book however, both this case AND the case of dependent variables, i.e. statistics for discrete and continuous time processes, are studied. This second case is very important for today's practitioners.Mathematical Statistics and Stochastic Processes is based on decision theory and asymptotic statistics and contains up-to-date information on the relevant topics of theory of probability, estimation, confidence intervals, non-parametric statistics and rob

  7. Stochastic differential equation model for linear growth birth and death processes with immigration and emigration

    International Nuclear Information System (INIS)

    Granita; Bahar, A.

    2015-01-01

    This paper discusses on linear birth and death with immigration and emigration (BIDE) process to stochastic differential equation (SDE) model. Forward Kolmogorov equation in continuous time Markov chain (CTMC) with a central-difference approximation was used to find Fokker-Planckequation corresponding to a diffusion process having the stochastic differential equation of BIDE process. The exact solution, mean and variance function of BIDE process was found

  8. Stochastic differential equation model for linear growth birth and death processes with immigration and emigration

    Energy Technology Data Exchange (ETDEWEB)

    Granita, E-mail: granitafc@gmail.com [Dept. Mathematical Education, State Islamic University of Sultan Syarif Kasim Riau, 28293 Indonesia and Dept. of Mathematical Science, Universiti Teknologi Malaysia, 81310,Johor (Malaysia); Bahar, A. [Dept. of Mathematical Science, Universiti Teknologi Malaysia, 81310,Johor Malaysia and UTM Center for Industrial and Applied Mathematics (UTM-CIAM) (Malaysia)

    2015-03-09

    This paper discusses on linear birth and death with immigration and emigration (BIDE) process to stochastic differential equation (SDE) model. Forward Kolmogorov equation in continuous time Markov chain (CTMC) with a central-difference approximation was used to find Fokker-Planckequation corresponding to a diffusion process having the stochastic differential equation of BIDE process. The exact solution, mean and variance function of BIDE process was found.

  9. Stochastic modeling of oligodendrocyte generation in cell culture: model validation with time-lapse data

    Directory of Open Access Journals (Sweden)

    Noble Mark

    2006-05-01

    Full Text Available Abstract Background The purpose of this paper is two-fold. The first objective is to validate the assumptions behind a stochastic model developed earlier by these authors to describe oligodendrocyte generation in cell culture. The second is to generate time-lapse data that may help biomathematicians to build stochastic models of cell proliferation and differentiation under other experimental scenarios. Results Using time-lapse video recording it is possible to follow the individual evolutions of different cells within each clone. This experimental technique is very laborious and cannot replace model-based quantitative inference from clonal data. However, it is unrivalled in validating the structure of a stochastic model intended to describe cell proliferation and differentiation at the clonal level. In this paper, such data are reported and analyzed for oligodendrocyte precursor cells cultured in vitro. Conclusion The results strongly support the validity of the most basic assumptions underpinning the previously proposed model of oligodendrocyte development in cell culture. However, there are some discrepancies; the most important is that the contribution of progenitor cell death to cell kinetics in this experimental system has been underestimated.

  10. Modelling the heat dynamics of a building using stochastic differential equations

    DEFF Research Database (Denmark)

    Andersen, Klaus Kaae; Madsen, Henrik; Hansen, Lars Henrik

    2000-01-01

    estimation and model validation, while physical knowledge is used in forming the model structure. The suggested lumped parameter model is thus based on thermodynamics and formulated as a system of stochastic differential equations. Due to the continuous time formulation the parameters of the model...

  11. Continuous-Time Mean-Variance Portfolio Selection under the CEV Process

    Directory of Open Access Journals (Sweden)

    Hui-qiang Ma

    2014-01-01

    Full Text Available We consider a continuous-time mean-variance portfolio selection model when stock price follows the constant elasticity of variance (CEV process. The aim of this paper is to derive an optimal portfolio strategy and the efficient frontier. The mean-variance portfolio selection problem is formulated as a linearly constrained convex program problem. By employing the Lagrange multiplier method and stochastic optimal control theory, we obtain the optimal portfolio strategy and mean-variance efficient frontier analytically. The results show that the mean-variance efficient frontier is still a parabola in the mean-variance plane, and the optimal strategies depend not only on the total wealth but also on the stock price. Moreover, some numerical examples are given to analyze the sensitivity of the efficient frontier with respect to the elasticity parameter and to illustrate the results presented in this paper. The numerical results show that the price of risk decreases as the elasticity coefficient increases.

  12. Stabilization Strategies of Supply Networks with Stochastic Switched Topology

    Directory of Open Access Journals (Sweden)

    Shukai Li

    2013-01-01

    Full Text Available In this paper, a dynamical supply networks model with stochastic switched topology is presented, in which the stochastic switched topology is dependent on a continuous time Markov process. The goal is to design the state-feedback control strategies to stabilize the dynamical supply networks. Based on Lyapunov stability theory, sufficient conditions for the existence of state feedback control strategies are given in terms of matrix inequalities, which ensure the robust stability of the supply networks at the stationary states and a prescribed H∞ disturbance attenuation level with respect to the uncertain demand. A numerical example is given to illustrate the effectiveness of the proposed method.

  13. Extinction time of a stochastic predator-prey model by the generalized cell mapping method

    Science.gov (United States)

    Han, Qun; Xu, Wei; Hu, Bing; Huang, Dongmei; Sun, Jian-Qiao

    2018-03-01

    The stochastic response and extinction time of a predator-prey model with Gaussian white noise excitations are studied by the generalized cell mapping (GCM) method based on the short-time Gaussian approximation (STGA). The methods for stochastic response probability density functions (PDFs) and extinction time statistics are developed. The Taylor expansion is used to deal with non-polynomial nonlinear terms of the model for deriving the moment equations with Gaussian closure, which are needed for the STGA in order to compute the one-step transition probabilities. The work is validated with direct Monte Carlo simulations. We have presented the transient responses showing the evolution from a Gaussian initial distribution to a non-Gaussian steady-state one. The effects of the model parameter and noise intensities on the steady-state PDFs are discussed. It is also found that the effects of noise intensities on the extinction time statistics are opposite to the effects on the limit probability distributions of the survival species.

  14. Some continual integrals from gaussian forms

    International Nuclear Information System (INIS)

    Mazmanishvili, A.S.

    1985-01-01

    The result summary of continual integration of gaussian functional type is given. The summary contains 124 continual integrals which are the mathematical expectation of the corresponding gaussian form by the continuum of random trajectories of four types: real-valued Ornstein-Uhlenbeck process, Wiener process, complex-valued Ornstein-Uhlenbeck process and the stochastic harmonic one. The summary includes both the known continual integrals and the unpublished before integrals. Mathematical results of the continual integration carried in the work may be applied in the problem of the theory of stochastic process, approaching to the finding of mean from gaussian forms by measures generated by the pointed stochastic processes

  15. Development of a generalized stochastic model for the analysis of monoenergetic space-time nuclear factor Kinetics

    International Nuclear Information System (INIS)

    Pham, Nhu Viet Ha

    2011-02-01

    To predict the space-time dependent behavior of a nuclear reactor, the conventional space-dependent kinetics equations are widely used for treating the spatial variables. However, the solutions of such deterministic space-dependent kinetics equations, which give only the mean values of the neutron population and the delayed neutron precursor concentrations, do not offer sufficient insight into the actual dynamic processes within a reactor, where the interacting populations vary randomly with space and time. It is also noted that at high power levels, the random behavior of a reactor is negligible but at low power levels, such as at start-up, random fluctuations in population dynamics can be significant. To mathematically describe the evolution of the state of a nuclear reactor using a set of stochastic kinetics equations, the forward stochastic model (FSM) in stochastic kinetics theory is devised through the concept of reactor transition probability and its probability generating function as the spatial domain of a reactor is partitioned into a number of space cells. Nevertheless, the FSM equations for the mean value of neutron and precursor distribution are deterministic-like. Furthermore, the numerical treatment of the FSM equations for the means, variances, and covariances is quite complicated and time-consuming. In the present study, a generalized stochastic model (called the stochastic space-dependent kinetics model or SSKM) based on the FSM and the Its stochastic differential equations was newly developed for the analysis of monoenergetic spacetime nuclear reactor kinetics in one dimension. First, the FSM equations for determining the mean values of neutron and delayed-neutron precursor populations were considered as the deterministic ones without taking into account their variances and covariances. Second, the system of interest was randomized again in the light of the Its stochastic differential equations in order to derive the SSKM. The proposed model

  16. An Innovative Real-time Environment for Unified Deterministic and Stochastic Groundwater Modeling

    Science.gov (United States)

    Li, S.; Liu, Q.

    2003-12-01

    Despite an exponential growth of computational capability over the last two decades-one that has allowed computational science and engineering to become a unique, powerful tool for scientific discovery-the extreme cost of groundwater modeling continues to limit its use. This occurs primarily because the modeling paradigm that has been employed for decades limits our ability to take full advantage of recent developments in computer, communication, graphic, and visualization technologies. In this presentation we introduce an innovative and sophisticated computational environment for groundwater modeling that promises to eliminate the current bottleneck and greatly expand the utility of computational tools for scientific discovery related to groundwater. Based on a set of efficient and robust computational algorithms, the new software system, called Interactive Groundwater (IGW), allows simulating complex flow and transport in aquifers subject to both systematic and "randomly" varying stresses and geological and chemical heterogeneity. Adopting a new paradigm, IGW eliminates a major bottleneck inherent in the traditional fragmented modeling technologies and enables real-time modeling, real-time visualization, real-time analysis, and real-time presentation. IGW functions as a "numerical laboratory" in which a researcher can freely explore in real-time: creating visually an aquifer of desired configurations, interactively imposing desired stresses, and then immediately investigating and visualizing the geology and the processes of flow and contaminant transport and transformation. A modeler can pause to edit at any time and interact on-line with any aspects (e.g., conceptual and numerical representation, boundary conditions, model solvers, and ways of visualization and analysis) of the integrated modeling process; he/she can initiate or stop, whenever needed, particle tracking, plume modeling, subscale modeling, cross-sectional modeling, stochastic modeling, monitoring

  17. Approximate method for stochastic chemical kinetics with two-time scales by chemical Langevin equations

    International Nuclear Information System (INIS)

    Wu, Fuke; Tian, Tianhai; Rawlings, James B.; Yin, George

    2016-01-01

    The frequently used reduction technique is based on the chemical master equation for stochastic chemical kinetics with two-time scales, which yields the modified stochastic simulation algorithm (SSA). For the chemical reaction processes involving a large number of molecular species and reactions, the collection of slow reactions may still include a large number of molecular species and reactions. Consequently, the SSA is still computationally expensive. Because the chemical Langevin equations (CLEs) can effectively work for a large number of molecular species and reactions, this paper develops a reduction method based on the CLE by the stochastic averaging principle developed in the work of Khasminskii and Yin [SIAM J. Appl. Math. 56, 1766–1793 (1996); ibid. 56, 1794–1819 (1996)] to average out the fast-reacting variables. This reduction method leads to a limit averaging system, which is an approximation of the slow reactions. Because in the stochastic chemical kinetics, the CLE is seen as the approximation of the SSA, the limit averaging system can be treated as the approximation of the slow reactions. As an application, we examine the reduction of computation complexity for the gene regulatory networks with two-time scales driven by intrinsic noise. For linear and nonlinear protein production functions, the simulations show that the sample average (expectation) of the limit averaging system is close to that of the slow-reaction process based on the SSA. It demonstrates that the limit averaging system is an efficient approximation of the slow-reaction process in the sense of the weak convergence.

  18. Effects of time delay on stochastic resonance of the stock prices in financial system

    Energy Technology Data Exchange (ETDEWEB)

    Li, Jiang-Cheng [Department of Physics, Yunnan University, Kunming, 650091 (China); Li, Chun [Department of Computer Science, Puer Teachers' College, Puer 665000 (China); Mei, Dong-Cheng, E-mail: meidch@ynu.edu.cn [Department of Physics, Yunnan University, Kunming, 650091 (China)

    2014-06-13

    The effect of time delay on stochastic resonance of the stock prices in finance system was investigated. The time delay is introduced into the Heston model driven by the extrinsic and intrinsic periodic information for stock price. The signal power amplification (SPA) was calculated by numerical simulation. The results indicate that an optimal critical value of delay time maximally enhances the reverse-resonance in the behaviors of SPA as a function of long-run variance of volatility or cross correlation coefficient between noises for both cases of intrinsic and extrinsic periodic information. Moreover, in both cases, being a critical value in the delay time, when the delay time takes value below the critical value, reverse-resonance increases with the delay time increasing, however, when the delay time takes value above the critical value, the reverse-resonance decrease with the delay time increasing. - Highlights: • The effects of delay time on stochastic resonance of the stock prices was investigated. • There is an optimal critical value of delay time maximally enhances the reverse-resonance • The reverse-resonance increases with the delay time increasing as the delay time takes value below the critical value • The reverse-resonance decrease with the delay time increasing as the delay time takes value above the critical value.

  19. Effects of time delay on stochastic resonance of the stock prices in financial system

    International Nuclear Information System (INIS)

    Li, Jiang-Cheng; Li, Chun; Mei, Dong-Cheng

    2014-01-01

    The effect of time delay on stochastic resonance of the stock prices in finance system was investigated. The time delay is introduced into the Heston model driven by the extrinsic and intrinsic periodic information for stock price. The signal power amplification (SPA) was calculated by numerical simulation. The results indicate that an optimal critical value of delay time maximally enhances the reverse-resonance in the behaviors of SPA as a function of long-run variance of volatility or cross correlation coefficient between noises for both cases of intrinsic and extrinsic periodic information. Moreover, in both cases, being a critical value in the delay time, when the delay time takes value below the critical value, reverse-resonance increases with the delay time increasing, however, when the delay time takes value above the critical value, the reverse-resonance decrease with the delay time increasing. - Highlights: • The effects of delay time on stochastic resonance of the stock prices was investigated. • There is an optimal critical value of delay time maximally enhances the reverse-resonance • The reverse-resonance increases with the delay time increasing as the delay time takes value below the critical value • The reverse-resonance decrease with the delay time increasing as the delay time takes value above the critical value

  20. On the contribution of a stochastic background of gravitational radiation to the timing noise of pulsars

    Science.gov (United States)

    Mashhoon, B.

    1982-01-01

    The influence of a stochastic and isotropic background of gravitational radiation on timing measurements of pulsars is investigated, and it is shown that pulsar timing noise may be used to establish a significant upper limit of about 10 to the -10th on the total energy density of very long-wavelength stochastic gravitational waves. This places restriction on the strength of very long wavelength gravitational waves in the Friedmann model, and such a background is expected to have no significant effect on the approximately 3 K electromagnetic background radiation or on the dynamics of a cluster of galaxies.

  1. Exponential Synchronization for Stochastic Neural Networks with Mixed Time Delays and Markovian Jump Parameters via Sampled Data

    Directory of Open Access Journals (Sweden)

    Yingwei Li

    2014-01-01

    Full Text Available The exponential synchronization issue for stochastic neural networks (SNNs with mixed time delays and Markovian jump parameters using sampled-data controller is investigated. Based on a novel Lyapunov-Krasovskii functional, stochastic analysis theory, and linear matrix inequality (LMI approach, we derived some novel sufficient conditions that guarantee that the master systems exponentially synchronize with the slave systems. The design method of the desired sampled-data controller is also proposed. To reflect the most dynamical behaviors of the system, both Markovian jump parameters and stochastic disturbance are considered, where stochastic disturbances are given in the form of a Brownian motion. The results obtained in this paper are a little conservative comparing the previous results in the literature. Finally, two numerical examples are given to illustrate the effectiveness of the proposed methods.

  2. Application of Bayesian inference to stochastic analytic continuation

    International Nuclear Information System (INIS)

    Fuchs, S; Pruschke, T; Jarrell, M

    2010-01-01

    We present an algorithm for the analytic continuation of imaginary-time quantum Monte Carlo data. The algorithm is strictly based on principles of Bayesian statistical inference. It utilizes Monte Carlo simulations to calculate a weighted average of possible energy spectra. We apply the algorithm to imaginary-time quantum Monte Carlo data and compare the resulting energy spectra with those from a standard maximum entropy calculation.

  3. Fault Detection for Wireless Networked Control Systems with Stochastic Switching Topology and Time Delay

    Directory of Open Access Journals (Sweden)

    Pengfei Guo

    2014-01-01

    Full Text Available This paper deals with the fault detection problem for a class of discrete-time wireless networked control systems described by switching topology with uncertainties and disturbances. System states of each individual node are affected not only by its own measurements, but also by other nodes’ measurements according to a certain network topology. As the topology of system can be switched in a stochastic way, we aim to design H∞ fault detection observers for nodes in the dynamic time-delay systems. By using the Lyapunov method and stochastic analysis techniques, sufficient conditions are acquired to guarantee the existence of the filters satisfying the H∞ performance constraint, and observer gains are derived by solving linear matrix inequalities. Finally, an illustrated example is provided to verify the effectiveness of the theoretical results.

  4. A stochastic fractional dynamics model of space-time variability of rain

    Science.gov (United States)

    Kundu, Prasun K.; Travis, James E.

    2013-09-01

    varies in space and time in a highly irregular manner and is described naturally in terms of a stochastic process. A characteristic feature of rainfall statistics is that they depend strongly on the space-time scales over which rain data are averaged. A spectral model of precipitation has been developed based on a stochastic differential equation of fractional order for the point rain rate, which allows a concise description of the second moment statistics of rain at any prescribed space-time averaging scale. The model is thus capable of providing a unified description of the statistics of both radar and rain gauge data. The underlying dynamical equation can be expressed in terms of space-time derivatives of fractional orders that are adjusted together with other model parameters to fit the data. The form of the resulting spectrum gives the model adequate flexibility to capture the subtle interplay between the spatial and temporal scales of variability of rain but strongly constrains the predicted statistical behavior as a function of the averaging length and time scales. We test the model with radar and gauge data collected contemporaneously at the NASA TRMM ground validation sites located near Melbourne, Florida and on the Kwajalein Atoll, Marshall Islands in the tropical Pacific. We estimate the parameters by tuning them to fit the second moment statistics of radar data at the smaller spatiotemporal scales. The model predictions are then found to fit the second moment statistics of the gauge data reasonably well at these scales without any further adjustment.

  5. Rotation in the dynamic factor modeling of multivariate stationary time series.

    NARCIS (Netherlands)

    Molenaar, P.C.M.; Nesselroade, J.R.

    2001-01-01

    A special rotation procedure is proposed for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white

  6. Asymptotics for the conditional-sum-of-squares estimator in multivariate fractional time series models

    DEFF Research Database (Denmark)

    Ørregård Nielsen, Morten

    This paper proves consistency and asymptotic normality for the conditional-sum-of-squares estimator, which is equivalent to the conditional maximum likelihood estimator, in multivariate fractional time series models. The model is parametric and quite general, and, in particular, encompasses...... the multivariate non-cointegrated fractional ARIMA model. The novelty of the consistency result, in particular, is that it applies to a multivariate model and to an arbitrarily large set of admissible parameter values, for which the objective function does not converge uniformly in probablity, thus making...

  7. Time-Weighted Balanced Stochastic Model Reduction

    DEFF Research Database (Denmark)

    Tahavori, Maryamsadat; Shaker, Hamid Reza

    2011-01-01

    A new relative error model reduction technique for linear time invariant (LTI) systems is proposed in this paper. Both continuous and discrete time systems can be reduced within this framework. The proposed model reduction method is mainly based upon time-weighted balanced truncation and a recently...

  8. Stochastic dynamics and control

    CERN Document Server

    Sun, Jian-Qiao; Zaslavsky, George

    2006-01-01

    This book is a result of many years of author's research and teaching on random vibration and control. It was used as lecture notes for a graduate course. It provides a systematic review of theory of probability, stochastic processes, and stochastic calculus. The feedback control is also reviewed in the book. Random vibration analyses of SDOF, MDOF and continuous structural systems are presented in a pedagogical order. The application of the random vibration theory to reliability and fatigue analysis is also discussed. Recent research results on fatigue analysis of non-Gaussian stress proc

  9. Benchmarking the stochastic time-dependent variational approach for excitation dynamics in molecular aggregates

    Energy Technology Data Exchange (ETDEWEB)

    Chorošajev, Vladimir [Department of Theoretical Physics, Faculty of Physics, Vilnius University, Sauletekio 9-III, 10222 Vilnius (Lithuania); Gelzinis, Andrius; Valkunas, Leonas [Department of Theoretical Physics, Faculty of Physics, Vilnius University, Sauletekio 9-III, 10222 Vilnius (Lithuania); Department of Molecular Compound Physics, Center for Physical Sciences and Technology, Sauletekio 3, 10222 Vilnius (Lithuania); Abramavicius, Darius, E-mail: darius.abramavicius@ff.vu.lt [Department of Theoretical Physics, Faculty of Physics, Vilnius University, Sauletekio 9-III, 10222 Vilnius (Lithuania)

    2016-12-20

    Highlights: • The Davydov ansatze can be used for finite temperature simulations with an extension. • The accuracy is high if the system is strongly coupled to the environmental phonons. • The approach can simulate time-resolved fluorescence spectra. - Abstract: Time dependent variational approach is a convenient method to characterize the excitation dynamics in molecular aggregates for different strengths of system-bath interaction a, which does not require any additional perturbative schemes. Until recently, however, this method was only applicable in zero temperature case. It has become possible to extend this method for finite temperatures with the introduction of stochastic time dependent variational approach. Here we present a comparison between this approach and the exact hierarchical equations of motion approach for describing excitation dynamics in a broad range of temperatures. We calculate electronic population evolution, absorption and auxiliary time resolved fluorescence spectra in different regimes and find that the stochastic approach shows excellent agreement with the exact approach when the system-bath coupling is sufficiently large and temperatures are high. The differences between the two methods are larger, when temperatures are lower or the system-bath coupling is small.

  10. Robust synchronization analysis in nonlinear stochastic cellular networks with time-varying delays, intracellular perturbations and intercellular noise.

    Science.gov (United States)

    Chen, Po-Wei; Chen, Bor-Sen

    2011-08-01

    Naturally, a cellular network consisted of a large amount of interacting cells is complex. These cells have to be synchronized in order to emerge their phenomena for some biological purposes. However, the inherently stochastic intra and intercellular interactions are noisy and delayed from biochemical processes. In this study, a robust synchronization scheme is proposed for a nonlinear stochastic time-delay coupled cellular network (TdCCN) in spite of the time-varying process delay and intracellular parameter perturbations. Furthermore, a nonlinear stochastic noise filtering ability is also investigated for this synchronized TdCCN against stochastic intercellular and environmental disturbances. Since it is very difficult to solve a robust synchronization problem with the Hamilton-Jacobi inequality (HJI) matrix, a linear matrix inequality (LMI) is employed to solve this problem via the help of a global linearization method. Through this robust synchronization analysis, we can gain a more systemic insight into not only the robust synchronizability but also the noise filtering ability of TdCCN under time-varying process delays, intracellular perturbations and intercellular disturbances. The measures of robustness and noise filtering ability of a synchronized TdCCN have potential application to the designs of neuron transmitters, on-time mass production of biochemical molecules, and synthetic biology. Finally, a benchmark of robust synchronization design in Escherichia coli repressilators is given to confirm the effectiveness of the proposed methods. Copyright © 2011 Elsevier Inc. All rights reserved.

  11. Stochastic model stationarization by eliminating the periodic term and its effect on time series prediction

    Science.gov (United States)

    Moeeni, Hamid; Bonakdari, Hossein; Fatemi, Seyed Ehsan

    2017-04-01

    Because time series stationarization has a key role in stochastic modeling results, three methods are analyzed in this study. The methods are seasonal differencing, seasonal standardization and spectral analysis to eliminate the periodic effect on time series stationarity. First, six time series including 4 streamflow series and 2 water temperature series are stationarized. The stochastic term for these series obtained with ARIMA is subsequently modeled. For the analysis, 9228 models are introduced. It is observed that seasonal standardization and spectral analysis eliminate the periodic term completely, while seasonal differencing maintains seasonal correlation structures. The obtained results indicate that all three methods present acceptable performance overall. However, model accuracy in monthly streamflow prediction is higher with seasonal differencing than with the other two methods. Another advantage of seasonal differencing over the other methods is that the monthly streamflow is never estimated as negative. Standardization is the best method for predicting monthly water temperature although it is quite similar to seasonal differencing, while spectral analysis performed the weakest in all cases. It is concluded that for each monthly seasonal series, seasonal differencing is the best stationarization method in terms of periodic effect elimination. Moreover, the monthly water temperature is predicted with more accuracy than monthly streamflow. The criteria of the average stochastic term divided by the amplitude of the periodic term obtained for monthly streamflow and monthly water temperature were 0.19 and 0.30, 0.21 and 0.13, and 0.07 and 0.04 respectively. As a result, the periodic term is more dominant than the stochastic term for water temperature in the monthly water temperature series compared to streamflow series.

  12. Pesin’s entropy formula for stochastic flows of diffeomorphisms

    Institute of Scientific and Technical Information of China (English)

    刘培东

    1996-01-01

    Pesin’s entropy formula relating entropy and Lyapunov exponents within the context of random dynamical systems generated by (discrete or continuous) stochastic flows of diffeomorphisms (including solution flows of stochastic differential equations on manifolds) is proved.

  13. Stochastic quantisation: theme and variation

    International Nuclear Information System (INIS)

    Klauder, J.R.; Kyoto Univ.

    1987-01-01

    The paper on stochastic quantisation is a contribution to the book commemorating the sixtieth birthday of E.S. Fradkin. Stochastic quantisation reformulates Euclidean quantum field theory in the language of Langevin equations. The generalised free field is discussed from the viewpoint of stochastic quantisation. An artificial family of highly singular model theories wherein the space-time derivatives are dropped altogether is also examined. Finally a modified form of stochastic quantisation is considered. (U.K.)

  14. Stochastic spin-one massive field

    International Nuclear Information System (INIS)

    Lim, S.C.

    1984-01-01

    Stochastic quantization schemes of Nelson and Parisi and Wu are applied to a spin-one massive field. Unlike the scalar case Nelson's stochastic spin-one massive field cannot be identified with the corresponding euclidean field even if the fourth component of the euclidean coordinate is taken as equal to the real physical time. In the Parisi-Wu quantization scheme the stochastic Proca vector field has a similar property as the scalar field; which has an asymptotically stationary part and a transient part. The large equal-time limit of the expectation values of the stochastic Proca field are equal to the expectation values of the corresponding euclidean field. In the Stueckelberg formalism the Parisi-Wu scheme gives rise to a stochastic vector field which differs from the massless gauge field in that the gauge cannot be fixed by the choice of boundary condition. (orig.)

  15. Continuous local martingales and stochastic integration in UMD Banach spaces

    NARCIS (Netherlands)

    Veraar, M.C.

    2007-01-01

    Recently, van Neerven, Weis and the author, constructed a theory for stochastic integration of UMD Banach space valued processes. Here the authors use a (cylindrical) Brownian motion as an integrator. In this note we show how one can extend these results to the case where the integrator is an

  16. Mean-variance portfolio selection for defined-contribution pension funds with stochastic salary.

    Science.gov (United States)

    Zhang, Chubing

    2014-01-01

    This paper focuses on a continuous-time dynamic mean-variance portfolio selection problem of defined-contribution pension funds with stochastic salary, whose risk comes from both financial market and nonfinancial market. By constructing a special Riccati equation as a continuous (actually a viscosity) solution to the HJB equation, we obtain an explicit closed form solution for the optimal investment portfolio as well as the efficient frontier.

  17. Mean-Variance Portfolio Selection for Defined-Contribution Pension Funds with Stochastic Salary

    OpenAIRE

    Chubing Zhang

    2014-01-01

    This paper focuses on a continuous-time dynamic mean-variance portfolio selection problem of defined-contribution pension funds with stochastic salary, whose risk comes from both financial market and nonfinancial market. By constructing a special Riccati equation as a continuous (actually a viscosity) solution to the HJB equation, we obtain an explicit closed form solution for the optimal investment portfolio as well as the efficient frontier.

  18. Non-fragile robust stabilization and H{sub {infinity}} control for uncertain stochastic nonlinear time-delay systems

    Energy Technology Data Exchange (ETDEWEB)

    Zhang Jinhui [Department of Automatic Control, Beijing Institute of Technology, Beijing 100081 (China)], E-mail: jinhuizhang82@gmail.com; Shi Peng [Faculty of Advanced Technology, University of Glamorgan, Pontypridd CF37 1DL (United Kingdom); ILSCM, School of Science and Engineering, Victoria University, Melbourne, Vic. 8001 (Australia); School of Mathematics and Statistics, University of South Australia, Mawson Lakes, SA 5095 (Australia)], E-mail: pshi@glam.ac.uk; Yang Hongjiu [Department of Automatic Control, Beijing Institute of Technology, Beijing 100081 (China)], E-mail: yanghongjiu@gmail.com

    2009-12-15

    This paper deals with the problem of non-fragile robust stabilization and H{sub {infinity}} control for a class of uncertain stochastic nonlinear time-delay systems. The parametric uncertainties are real time-varying as well as norm bounded. The time-delay factors are unknown and time-varying with known bounds. The aim is to design a memoryless non-fragile state feedback control law such that the closed-loop system is stochastically asymptotically stable in the mean square and the effect of the disturbance input on the controlled output is less than a prescribed level for all admissible parameter uncertainties. New sufficient conditions for the existence of such controllers are presented based on the linear matrix inequalities (LMIs) approach. Numerical example is given to illustrate the effectiveness of the developed techniques.

  19. The Ising Decision Maker: a binary stochastic network for choice response time.

    Science.gov (United States)

    Verdonck, Stijn; Tuerlinckx, Francis

    2014-07-01

    The Ising Decision Maker (IDM) is a new formal model for speeded two-choice decision making derived from the stochastic Hopfield network or dynamic Ising model. On a microscopic level, it consists of 2 pools of binary stochastic neurons with pairwise interactions. Inside each pool, neurons excite each other, whereas between pools, neurons inhibit each other. The perceptual input is represented by an external excitatory field. Using methods from statistical mechanics, the high-dimensional network of neurons (microscopic level) is reduced to a two-dimensional stochastic process, describing the evolution of the mean neural activity per pool (macroscopic level). The IDM can be seen as an abstract, analytically tractable multiple attractor network model of information accumulation. In this article, the properties of the IDM are studied, the relations to existing models are discussed, and it is shown that the most important basic aspects of two-choice response time data can be reproduced. In addition, the IDM is shown to predict a variety of observed psychophysical relations such as Piéron's law, the van der Molen-Keuss effect, and Weber's law. Using Bayesian methods, the model is fitted to both simulated and real data, and its performance is compared to the Ratcliff diffusion model. (c) 2014 APA, all rights reserved.

  20. Real-time forecasting of an epidemic using a discrete time stochastic model: a case study of pandemic influenza (H1N1-2009

    Directory of Open Access Journals (Sweden)

    Nishiura Hiroshi

    2011-02-01

    Full Text Available Abstract Background Real-time forecasting of epidemics, especially those based on a likelihood-based approach, is understudied. This study aimed to develop a simple method that can be used for the real-time epidemic forecasting. Methods A discrete time stochastic model, accounting for demographic stochasticity and conditional measurement, was developed and applied as a case study to the weekly incidence of pandemic influenza (H1N1-2009 in Japan. By imposing a branching process approximation and by assuming the linear growth of cases within each reporting interval, the epidemic curve is predicted using only two parameters. The uncertainty bounds of the forecasts are computed using chains of conditional offspring distributions. Results The quality of the forecasts made before the epidemic peak appears largely to depend on obtaining valid parameter estimates. The forecasts of both weekly incidence and final epidemic size greatly improved at and after the epidemic peak with all the observed data points falling within the uncertainty bounds. Conclusions Real-time forecasting using the discrete time stochastic model with its simple computation of the uncertainty bounds was successful. Because of the simplistic model structure, the proposed model has the potential to additionally account for various types of heterogeneity, time-dependent transmission dynamics and epidemiological details. The impact of such complexities on forecasting should be explored when the data become available as part of the disease surveillance.

  1. Multivariate survival analysis and competing risks

    CERN Document Server

    Crowder, Martin J

    2012-01-01

    Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research.

  2. Stochastic modeling

    CERN Document Server

    Lanchier, Nicolas

    2017-01-01

    Three coherent parts form the material covered in this text, portions of which have not been widely covered in traditional textbooks. In this coverage the reader is quickly introduced to several different topics enriched with 175 exercises which focus on real-world problems. Exercises range from the classics of probability theory to more exotic research-oriented problems based on numerical simulations. Intended for graduate students in mathematics and applied sciences, the text provides the tools and training needed to write and use programs for research purposes. The first part of the text begins with a brief review of measure theory and revisits the main concepts of probability theory, from random variables to the standard limit theorems. The second part covers traditional material on stochastic processes, including martingales, discrete-time Markov chains, Poisson processes, and continuous-time Markov chains. The theory developed is illustrated by a variety of examples surrounding applications such as the ...

  3. Time-dependent solutions for stochastic systems with delays: Perturbation theory and applications to financial physics

    International Nuclear Information System (INIS)

    Frank, T.D.

    2006-01-01

    First-order approximations of time-dependent solutions are determined for stochastic systems perturbed by time-delayed feedback forces. To this end, the theory of delay Fokker-Planck equations is applied in combination with Bayes' theorem. Applications to a time-delayed Ornstein-Uhlenbeck process and the geometric Brownian walk of financial physics are discussed

  4. A comparison of the stochastic and machine learning approaches in hydrologic time series forecasting

    Science.gov (United States)

    Kim, T.; Joo, K.; Seo, J.; Heo, J. H.

    2016-12-01

    Hydrologic time series forecasting is an essential task in water resources management and it becomes more difficult due to the complexity of runoff process. Traditional stochastic models such as ARIMA family has been used as a standard approach in time series modeling and forecasting of hydrological variables. Due to the nonlinearity in hydrologic time series data, machine learning approaches has been studied with the advantage of discovering relevant features in a nonlinear relation among variables. This study aims to compare the predictability between the traditional stochastic model and the machine learning approach. Seasonal ARIMA model was used as the traditional time series model, and Random Forest model which consists of decision tree and ensemble method using multiple predictor approach was applied as the machine learning approach. In the application, monthly inflow data from 1986 to 2015 of Chungju dam in South Korea were used for modeling and forecasting. In order to evaluate the performances of the used models, one step ahead and multi-step ahead forecasting was applied. Root mean squared error and mean absolute error of two models were compared.

  5. MONTE CARLO SIMULATION OF MULTIFOCAL STOCHASTIC SCANNING SYSTEM

    Directory of Open Access Journals (Sweden)

    LIXIN LIU

    2014-01-01

    Full Text Available Multifocal multiphoton microscopy (MMM has greatly improved the utilization of excitation light and imaging speed due to parallel multiphoton excitation of the samples and simultaneous detection of the signals, which allows it to perform three-dimensional fast fluorescence imaging. Stochastic scanning can provide continuous, uniform and high-speed excitation of the sample, which makes it a suitable scanning scheme for MMM. In this paper, the graphical programming language — LabVIEW is used to achieve stochastic scanning of the two-dimensional galvo scanners by using white noise signals to control the x and y mirrors independently. Moreover, the stochastic scanning process is simulated by using Monte Carlo method. Our results show that MMM can avoid oversampling or subsampling in the scanning area and meet the requirements of uniform sampling by stochastically scanning the individual units of the N × N foci array. Therefore, continuous and uniform scanning in the whole field of view is implemented.

  6. Optimal control of switching time in switched stochastic systems with multi-switching times and different costs

    Science.gov (United States)

    Liu, Xiaomei; Li, Shengtao; Zhang, Kanjian

    2017-08-01

    In this paper, we solve an optimal control problem for a class of time-invariant switched stochastic systems with multi-switching times, where the objective is to minimise a cost functional with different costs defined on the states. In particular, we focus on problems in which a pre-specified sequence of active subsystems is given and the switching times are the only control variables. Based on the calculus of variation, we derive the gradient of the cost functional with respect to the switching times on an especially simple form, which can be directly used in gradient descent algorithms to locate the optimal switching instants. Finally, a numerical example is given, highlighting the validity of the proposed methodology.

  7. ℋ∞ constant gain state feedback stabilization of stochastic hybrid systems with Wiener process

    Directory of Open Access Journals (Sweden)

    E. K. Boukas

    2004-01-01

    Full Text Available This paper considers the stabilization problem of the class of continuous-time linear stochastic hybrid systems with Wiener process. The ℋ∞ state feedback stabilization problem is treated. A state feedback controller with constant gain that does not require access to the system mode is designed. LMI-based conditions are developed to design the state feedback controller with constant gain that stochastically stabilizes the studied class of systems and, at the same time, achieve the disturbance rejection of a desired level. The minimum disturbance rejection is also determined. Numerical examples are given to show the usefulness of the proposed results.

  8. Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids.

    Science.gov (United States)

    Ali, S M; Mehmood, C A; Khan, B; Jawad, M; Farid, U; Jadoon, J K; Ali, M; Tareen, N K; Usman, S; Majid, M; Anwar, S M

    2016-01-01

    In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion.

  9. Stochastic Modeling of Usage Patterns in a Web-Based Information System.

    Science.gov (United States)

    Chen, Hui-Min; Cooper, Michael D.

    2002-01-01

    Uses continuous-time stochastic models, mainly based on semi-Markov chains, to derive user state transition patterns, both in rates and in probabilities, in a Web-based information system. Describes search sessions from transaction logs of the University of California's MELVYL library catalog system and discusses sequential dependency. (Author/LRW)

  10. Estimation of non-linear continuous time models for the heat exchange dynamics of building integrated photovoltaic modules

    DEFF Research Database (Denmark)

    Jimenez, M.J.; Madsen, Henrik; Bloem, J.J.

    2008-01-01

    This paper focuses on a method for linear or non-linear continuous time modelling of physical systems using discrete time data. This approach facilitates a more appropriate modelling of more realistic non-linear systems. Particularly concerning advanced building components, convective and radiati...... that a description of the non-linear heat transfer is essential. The resulting model is a non-linear first order stochastic differential equation for the heat transfer of the PV component....... heat interchanges are non-linear effects and represent significant contributions in a variety of components such as photovoltaic integrated facades or roofs and those using these effects as passive cooling strategies, etc. Since models are approximations of the physical system and data is encumbered...

  11. Multivariate time series clustering on geophysical data recorded at Mt. Etna from 1996 to 2003

    Science.gov (United States)

    Di Salvo, Roberto; Montalto, Placido; Nunnari, Giuseppe; Neri, Marco; Puglisi, Giuseppe

    2013-02-01

    Time series clustering is an important task in data analysis issues in order to extract implicit, previously unknown, and potentially useful information from a large collection of data. Finding useful similar trends in multivariate time series represents a challenge in several areas including geophysics environment research. While traditional time series analysis methods deal only with univariate time series, multivariate time series analysis is a more suitable approach in the field of research where different kinds of data are available. Moreover, the conventional time series clustering techniques do not provide desired results for geophysical datasets due to the huge amount of data whose sampling rate is different according to the nature of signal. In this paper, a novel approach concerning geophysical multivariate time series clustering is proposed using dynamic time series segmentation and Self Organizing Maps techniques. This method allows finding coupling among trends of different geophysical data recorded from monitoring networks at Mt. Etna spanning from 1996 to 2003, when the transition from summit eruptions to flank eruptions occurred. This information can be used to carry out a more careful evaluation of the state of volcano and to define potential hazard assessment at Mt. Etna.

  12. Detecting stochastic backgrounds of gravitational waves with pulsar timing arrays

    Science.gov (United States)

    Siemens, Xavier

    2016-03-01

    For the past decade the North American Nanohertz Observatory for Gravitational Waves (NANOGrav) has been using the Green Bank Telescope and the Arecibo Observatory to monitor millisecond pulsars. NANOGrav, along with two other international collaborations, the European Pulsar Timing Array and the Parkes Pulsar Timing Array in Australia, form a consortium of consortia: the International Pulsar Timing Array (IPTA). The goal of the IPTA is to directly detect low-frequency gravitational waves which cause small changes to the times of arrival of radio pulses from millisecond pulsars. In this talk I will discuss the work of NANOGrav and the IPTA, as well as our sensitivity to stochastic backgrounds of gravitational waves. I will show that a detection of the background produced by supermassive black hole binaries is possible by the end of the decade. Supported by the NANOGrav Physics Frontiers Center.

  13. Trajectory averaging for stochastic approximation MCMC algorithms

    KAUST Repository

    Liang, Faming

    2010-10-01

    The subject of stochastic approximation was founded by Robbins and Monro [Ann. Math. Statist. 22 (1951) 400-407]. After five decades of continual development, it has developed into an important area in systems control and optimization, and it has also served as a prototype for the development of adaptive algorithms for on-line estimation and control of stochastic systems. Recently, it has been used in statistics with Markov chain Monte Carlo for solving maximum likelihood estimation problems and for general simulation and optimizations. In this paper, we first show that the trajectory averaging estimator is asymptotically efficient for the stochastic approximation MCMC (SAMCMC) algorithm under mild conditions, and then apply this result to the stochastic approximation Monte Carlo algorithm [Liang, Liu and Carroll J. Amer. Statist. Assoc. 102 (2007) 305-320]. The application of the trajectory averaging estimator to other stochastic approximationMCMC algorithms, for example, a stochastic approximation MLE algorithm for missing data problems, is also considered in the paper. © Institute of Mathematical Statistics, 2010.

  14. Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.

    Science.gov (United States)

    Molenaar, Peter C. M.; Nesselroade, John R.

    2001-01-01

    Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…

  15. ON THE ANISOTROPIC NORM OF DISCRETE TIME STOCHASTIC SYSTEMS WITH STATE DEPENDENT NOISE

    Directory of Open Access Journals (Sweden)

    Isaac Yaesh

    2013-01-01

    Full Text Available The purpose of this paper is to determine conditions for the bound-edness of the anisotropic norm of discrete-time linear stochastic sys-tems with state dependent noise. It is proved that these conditions canbe expressed in terms of the feasibility of a specific system of matrixinequalities.

  16. Time-dependent stochastic inversion in acoustic tomography of the atmosphere with reciprocal sound transmission

    International Nuclear Information System (INIS)

    Vecherin, Sergey N; Ostashev, Vladimir E; Wilson, D Keith; Ziemann, A

    2008-01-01

    Time-dependent stochastic inversion (TDSI) was recently developed for acoustic travel-time tomography of the atmosphere. This type of tomography allows reconstruction of temperature and wind-velocity fields given the location of sound sources and receivers and the travel times between all source–receiver pairs. The quality of reconstruction provided by TDSI depends on the geometry of the transducer array. However, TDSI has not been studied for the geometry with reciprocal sound transmission. This paper is focused on three aspects of TDSI. First, the use of TDSI in reciprocal sound transmission arrays is studied in numerical and physical experiments. Second, efficiency of time-dependent and ordinary stochastic inversion (SI) algorithms is studied in numerical experiments. Third, a new model of noise in the input data for TDSI is developed that accounts for systematic errors in transducer positions. It is shown that (i) a separation of the travel times into temperature and wind-velocity components in tomography with reciprocal transmission does not improve the reconstruction, (ii) TDSI yields a better reconstruction than SI and (iii) the developed model of noise yields an accurate reconstruction of turbulent fields and estimation of errors in the reconstruction

  17. Nonzero-Sum Stochastic Differential Portfolio Games under a Markovian Regime Switching Model

    Directory of Open Access Journals (Sweden)

    Chaoqun Ma

    2015-01-01

    Full Text Available We consider a nonzero-sum stochastic differential portfolio game problem in a continuous-time Markov regime switching environment when the price dynamics of the risky assets are governed by a Markov-modulated geometric Brownian motion (GBM. The market parameters, including the bank interest rate and the appreciation and volatility rates of the risky assets, switch over time according to a continuous-time Markov chain. We formulate the nonzero-sum stochastic differential portfolio game problem as two utility maximization problems of the sum process between two investors’ terminal wealth. We derive a pair of regime switching Hamilton-Jacobi-Bellman (HJB equations and two systems of coupled HJB equations at different regimes. We obtain explicit optimal portfolio strategies and Feynman-Kac representations of the two value functions. Furthermore, we solve the system of coupled HJB equations explicitly in a special case where there are only two states in the Markov chain. Finally we provide comparative statics and numerical simulation analysis of optimal portfolio strategies and investigate the impact of regime switching on optimal portfolio strategies.

  18. Mean-Variance Portfolio Selection for Defined-Contribution Pension Funds with Stochastic Salary

    Directory of Open Access Journals (Sweden)

    Chubing Zhang

    2014-01-01

    Full Text Available This paper focuses on a continuous-time dynamic mean-variance portfolio selection problem of defined-contribution pension funds with stochastic salary, whose risk comes from both financial market and nonfinancial market. By constructing a special Riccati equation as a continuous (actually a viscosity solution to the HJB equation, we obtain an explicit closed form solution for the optimal investment portfolio as well as the efficient frontier.

  19. Mean-Variance Portfolio Selection for Defined-Contribution Pension Funds with Stochastic Salary

    Science.gov (United States)

    Zhang, Chubing

    2014-01-01

    This paper focuses on a continuous-time dynamic mean-variance portfolio selection problem of defined-contribution pension funds with stochastic salary, whose risk comes from both financial market and nonfinancial market. By constructing a special Riccati equation as a continuous (actually a viscosity) solution to the HJB equation, we obtain an explicit closed form solution for the optimal investment portfolio as well as the efficient frontier. PMID:24782667

  20. Stochastic massless fields I: Integer spin

    International Nuclear Information System (INIS)

    Lim, S.C.

    1981-04-01

    Nelson's stochastic quantization scheme is applied to classical massless tensor potential in ''Coulomb'' gauge. The relationship between stochastic potential field in various gauges is discussed using the case of vector potential as an illustration. It is possible to identify the Euclidean tensor potential with the corresponding stochastic field in physical Minkowski space-time. Stochastic quantization of massless fields can also be carried out in terms of field strength tensors. An example of linearized stochastic gravitational field in vacuum is given. (author)

  1. Stochastic modeling of the hypothalamic pulse generator activity.

    Science.gov (United States)

    Camproux, A C; Thalabard, J C; Thomas, G

    1994-11-01

    Luteinizing hormone (LH) is released by the pituitary in discrete pulses. In the monkey, the appearance of LH pulses in the plasma is invariably associated with sharp increases (i.e, volleys) in the frequency of the hypothalamic pulse generator electrical activity, so that continuous monitoring of this activity by telemetry provides a unique means to study the temporal structure of the mechanism generating the pulses. To assess whether the times of occurrence and durations of previous volleys exert significant influence on the timing of the next volley, we used a class of periodic counting process models that specify the stochastic intensity of the process as the product of two factors: 1) a periodic baseline intensity and 2) a stochastic regression function with covariates representing the influence of the past. This approach allows the characterization of circadian modulation and memory range of the process underlying hypothalamic pulse generator activity, as illustrated by fitting the model to experimental data from two ovariectomized rhesus monkeys.

  2. Stochastic modeling for time series InSAR: with emphasis on atmospheric effects

    Science.gov (United States)

    Cao, Yunmeng; Li, Zhiwei; Wei, Jianchao; Hu, Jun; Duan, Meng; Feng, Guangcai

    2018-02-01

    Despite the many applications of time series interferometric synthetic aperture radar (TS-InSAR) techniques in geophysical problems, error analysis and assessment have been largely overlooked. Tropospheric propagation error is still the dominant error source of InSAR observations. However, the spatiotemporal variation of atmospheric effects is seldom considered in the present standard TS-InSAR techniques, such as persistent scatterer interferometry and small baseline subset interferometry. The failure to consider the stochastic properties of atmospheric effects not only affects the accuracy of the estimators, but also makes it difficult to assess the uncertainty of the final geophysical results. To address this issue, this paper proposes a network-based variance-covariance estimation method to model the spatiotemporal variation of tropospheric signals, and to estimate the temporal variance-covariance matrix of TS-InSAR observations. The constructed stochastic model is then incorporated into the TS-InSAR estimators both for parameters (e.g., deformation velocity, topography residual) estimation and uncertainty assessment. It is an incremental and positive improvement to the traditional weighted least squares methods to solve the multitemporal InSAR time series. The performance of the proposed method is validated by using both simulated and real datasets.

  3. Broadcast Abstraction in a Stochastic Calculus for Mobile Networks

    DEFF Research Database (Denmark)

    Song, Lei; Godskesen, Jens Christian

    2012-01-01

    topology constraint. We allow continuous time stochastic behavior of processes running at network nodes, e.g. in order to be able to model randomized protocols. The introduction of group broadcast and an operator to help avoid flooding allows us to define a novel notion of broadcast abstraction. Finally......, we define a weak bisimulation congruence and apply our theory on an example of a leader election protocol....

  4. Anomalous transport in turbulent plasmas and continuous time random walks

    International Nuclear Information System (INIS)

    Balescu, R.

    1995-01-01

    The possibility of a model of anomalous transport problems in a turbulent plasma by a purely stochastic process is investigated. The theory of continuous time random walks (CTRW's) is briefly reviewed. It is shown that a particular class, called the standard long tail CTRW's is of special interest for the description of subdiffusive transport. Its evolution is described by a non-Markovian diffusion equation that is constructed in such a way as to yield exact values for all the moments of the density profile. The concept of a CTRW model is compared to an exact solution of a simple test problem: transport of charged particles in a fluctuating magnetic field in the limit of infinite perpendicular correlation length. Although the well-known behavior of the mean square displacement proportional to t 1/2 is easily recovered, the exact density profile cannot be modeled by a CTRW. However, the quasilinear approximation of the kinetic equation has the form of a non-Markovian diffusion equation and can thus be generated by a CTRW

  5. Multiobjective Output Feedback Control of a Class of Stochastic Hybrid Systems with State-Dependent Noise

    Directory of Open Access Journals (Sweden)

    S. Aberkane

    2007-01-01

    Full Text Available This paper deals with dynamic output feedback control of continuous-time active fault tolerant control systems with Markovian parameters (AFTCSMP and state-dependent noise. The main contribution is to formulate conditions for multiperformance design, related to this class of stochastic hybrid systems, that take into account the problematic resulting from the fact that the controller only depends on the fault detection and isolation (FDI process. The specifications and objectives under consideration include stochastic stability, ℋ2 and ℋ∞ (or more generally, stochastic integral quadratic constraints performances. Results are formulated as matrix inequalities. The theoretical results are illustrated using a classical example from literature.

  6. Stochastic temperature and the Nicolai map

    International Nuclear Information System (INIS)

    Hueffel, H.

    1989-01-01

    Just as standard temperature can be related to the time coordinate of Euclidean space, a new concept of 'stochastic temperature' may be introduced by associating it to the Parisi-Wu time of stochastic quantization. The perturbative equilibrium limit for a self-interacting scalar field is studied, and a 'thermal' mass shift to one loop is shown. In addition one may interpret the underlying stochastic process as a Nicolai map at nonzero 'temperature'. 22 refs. (Author)

  7. 2–stage stochastic Runge–Kutta for stochastic delay differential equations

    Energy Technology Data Exchange (ETDEWEB)

    Rosli, Norhayati; Jusoh Awang, Rahimah [Faculty of Industrial Science and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, 26300, Gambang, Pahang (Malaysia); Bahar, Arifah; Yeak, S. H. [Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor (Malaysia)

    2015-05-15

    This paper proposes a newly developed one-step derivative-free method, that is 2-stage stochastic Runge-Kutta (SRK2) to approximate the solution of stochastic delay differential equations (SDDEs) with a constant time lag, r > 0. General formulation of stochastic Runge-Kutta for SDDEs is introduced and Stratonovich Taylor series expansion for numerical solution of SRK2 is presented. Local truncation error of SRK2 is measured by comparing the Stratonovich Taylor expansion of the exact solution with the computed solution. Numerical experiment is performed to assure the validity of the method in simulating the strong solution of SDDEs.

  8. A stochastic model for quantum measurement

    International Nuclear Information System (INIS)

    Budiyono, Agung

    2013-01-01

    We develop a statistical model of microscopic stochastic deviation from classical mechanics based on a stochastic process with a transition probability that is assumed to be given by an exponential distribution of infinitesimal stationary action. We apply the statistical model to stochastically modify a classical mechanical model for the measurement of physical quantities reproducing the prediction of quantum mechanics. The system+apparatus always has a definite configuration at all times, as in classical mechanics, fluctuating randomly following a continuous trajectory. On the other hand, the wavefunction and quantum mechanical Hermitian operator corresponding to the physical quantity arise formally as artificial mathematical constructs. During a single measurement, the wavefunction of the whole system+apparatus evolves according to a Schrödinger equation and the configuration of the apparatus acts as the pointer of the measurement so that there is no wavefunction collapse. We will also show that while the outcome of each single measurement event does not reveal the actual value of the physical quantity prior to measurement, its average in an ensemble of identical measurements is equal to the average of the actual value of the physical quantity prior to measurement over the distribution of the configuration of the system. (paper)

  9. Analysis of dispatching rules in a stochastic dynamic job shop manufacturing system with sequence-dependent setup times

    Science.gov (United States)

    Sharma, Pankaj; Jain, Ajai

    2014-12-01

    Stochastic dynamic job shop scheduling problem with consideration of sequence-dependent setup times are among the most difficult classes of scheduling problems. This paper assesses the performance of nine dispatching rules in such shop from makespan, mean flow time, maximum flow time, mean tardiness, maximum tardiness, number of tardy jobs, total setups and mean setup time performance measures viewpoint. A discrete event simulation model of a stochastic dynamic job shop manufacturing system is developed for investigation purpose. Nine dispatching rules identified from literature are incorporated in the simulation model. The simulation experiments are conducted under due date tightness factor of 3, shop utilization percentage of 90% and setup times less than processing times. Results indicate that shortest setup time (SIMSET) rule provides the best performance for mean flow time and number of tardy jobs measures. The job with similar setup and modified earliest due date (JMEDD) rule provides the best performance for makespan, maximum flow time, mean tardiness, maximum tardiness, total setups and mean setup time measures.

  10. Probability and stochastic modeling

    CERN Document Server

    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...

  11. Path to Stochastic Stability: Comparative Analysis of Stochastic Learning Dynamics in Games

    KAUST Repository

    Jaleel, Hassan

    2018-04-08

    Stochastic stability is a popular solution concept for stochastic learning dynamics in games. However, a critical limitation of this solution concept is its inability to distinguish between different learning rules that lead to the same steady-state behavior. We address this limitation for the first time and develop a framework for the comparative analysis of stochastic learning dynamics with different update rules but same steady-state behavior. We present the framework in the context of two learning dynamics: Log-Linear Learning (LLL) and Metropolis Learning (ML). Although both of these dynamics have the same stochastically stable states, LLL and ML correspond to different behavioral models for decision making. Moreover, we demonstrate through an example setup of sensor coverage game that for each of these dynamics, the paths to stochastically stable states exhibit distinctive behaviors. Therefore, we propose multiple criteria to analyze and quantify the differences in the short and medium run behavior of stochastic learning dynamics. We derive and compare upper bounds on the expected hitting time to the set of Nash equilibria for both LLL and ML. For the medium to long-run behavior, we identify a set of tools from the theory of perturbed Markov chains that result in a hierarchical decomposition of the state space into collections of states called cycles. We compare LLL and ML based on the proposed criteria and develop invaluable insights into the comparative behavior of the two dynamics.

  12. On solutions of neutral stochastic delay Volterra equations with singular kernels

    Directory of Open Access Journals (Sweden)

    Xiaotai Wu

    2012-08-01

    Full Text Available In this paper, existence, uniqueness and continuity of the adapted solutions for neutral stochastic delay Volterra equations with singular kernels are discussed. In addition, continuous dependence on the initial date is also investigated. Finally, stochastic Volterra equation with the kernel of fractional Brownian motion is studied to illustrate the effectiveness of our results.

  13. time series modeling of daily abandoned calls in a call centre

    African Journals Online (AJOL)

    DJFLEX

    Models for evaluating and predicting the short periodic time series in daily ... Ugwuowo (2006) proposed asymmetric angular- linear multivariate regression models, ..... Using the parameter estimates in Table 3, the fitted Fourier series model is ..... For the SARIMA model with the stochastic component also being white noise, ...

  14. Stochastic dynamics and irreversibility

    CERN Document Server

    Tomé, Tânia

    2015-01-01

    This textbook presents an exposition of stochastic dynamics and irreversibility. It comprises the principles of probability theory and the stochastic dynamics in continuous spaces, described by Langevin and Fokker-Planck equations, and in discrete spaces, described by Markov chains and master equations. Special concern is given to the study of irreversibility, both in systems that evolve to equilibrium and in nonequilibrium stationary states. Attention is also given to the study of models displaying phase transitions and critical phenomema both in thermodynamic equilibrium and out of equilibrium. These models include the linear Glauber model, the Glauber-Ising model, lattice models with absorbing states such as the contact process and those used in population dynamic and spreading of epidemic, probabilistic cellular automata, reaction-diffusion processes, random sequential adsorption and dynamic percolation. A stochastic approach to chemical reaction is also presented.The textbook is intended for students of ...

  15. Predicting population extinction or disease outbreaks with stochastic models

    Directory of Open Access Journals (Sweden)

    Linda J. S. Allen

    2017-01-01

    Full Text Available Models of exponential growth, logistic growth and epidemics are common applications in undergraduate differential equation courses. The corresponding stochastic models are not part of these courses, although when population sizes are small their behaviour is often more realistic and distinctly different from deterministic models. For example, the randomness associated with births and deaths may lead to population extinction even in an exponentially growing population. Some background in continuous-time Markov chains and applications to populations, epidemics and cancer are presented with a goal to introduce this topic into the undergraduate mathematics curriculum that will encourage further investigation into problems on conservation, infectious diseases and cancer therapy. MATLAB programs for graphing sample paths of stochastic models are provided in the Appendix.

  16. Structured Modeling and Analysis of Stochastic Epidemics with Immigration and Demographic Effects.

    Science.gov (United States)

    Baumann, Hendrik; Sandmann, Werner

    2016-01-01

    Stochastic epidemics with open populations of variable population sizes are considered where due to immigration and demographic effects the epidemic does not eventually die out forever. The underlying stochastic processes are ergodic multi-dimensional continuous-time Markov chains that possess unique equilibrium probability distributions. Modeling these epidemics as level-dependent quasi-birth-and-death processes enables efficient computations of the equilibrium distributions by matrix-analytic methods. Numerical examples for specific parameter sets are provided, which demonstrates that this approach is particularly well-suited for studying the impact of varying rates for immigration, births, deaths, infection, recovery from infection, and loss of immunity.

  17. Stochastic Analysis with Financial Applications

    CERN Document Server

    Kohatsu-Higa, Arturo; Sheu, Shuenn-Jyi

    2011-01-01

    Stochastic analysis has a variety of applications to biological systems as well as physical and engineering problems, and its applications to finance and insurance have bloomed exponentially in recent times. The goal of this book is to present a broad overview of the range of applications of stochastic analysis and some of its recent theoretical developments. This includes numerical simulation, error analysis, parameter estimation, as well as control and robustness properties for stochastic equations. This book also covers the areas of backward stochastic differential equations via the (non-li

  18. Compositional Modelling of Stochastic Hybrid Systems

    NARCIS (Netherlands)

    Strubbe, S.N.

    2005-01-01

    In this thesis we present a modelling framework for compositional modelling of stochastic hybrid systems. Hybrid systems consist of a combination of continuous and discrete dynamics. The state space of a hybrid system is hybrid in the sense that it consists of a continuous component and a discrete

  19. An Optogenetic Platform for Real-Time, Single-Cell Interrogation of Stochastic Transcriptional Regulation.

    Science.gov (United States)

    Rullan, Marc; Benzinger, Dirk; Schmidt, Gregor W; Milias-Argeitis, Andreas; Khammash, Mustafa

    2018-05-17

    Transcription is a highly regulated and inherently stochastic process. The complexity of signal transduction and gene regulation makes it challenging to analyze how the dynamic activity of transcriptional regulators affects stochastic transcription. By combining a fast-acting, photo-regulatable transcription factor with nascent RNA quantification in live cells and an experimental setup for precise spatiotemporal delivery of light inputs, we constructed a platform for the real-time, single-cell interrogation of transcription in Saccharomyces cerevisiae. We show that transcriptional activation and deactivation are fast and memoryless. By analyzing the temporal activity of individual cells, we found that transcription occurs in bursts, whose duration and timing are modulated by transcription factor activity. Using our platform, we regulated transcription via light-driven feedback loops at the single-cell level. Feedback markedly reduced cell-to-cell variability and led to qualitative differences in cellular transcriptional dynamics. Our platform establishes a flexible method for studying transcriptional dynamics in single cells. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

  20. Time step rescaling recovers continuous-time dynamical properties for discrete-time Langevin integration of nonequilibrium systems.

    Science.gov (United States)

    Sivak, David A; Chodera, John D; Crooks, Gavin E

    2014-06-19

    When simulating molecular systems using deterministic equations of motion (e.g., Newtonian dynamics), such equations are generally numerically integrated according to a well-developed set of algorithms that share commonly agreed-upon desirable properties. However, for stochastic equations of motion (e.g., Langevin dynamics), there is still broad disagreement over which integration algorithms are most appropriate. While multiple desiderata have been proposed throughout the literature, consensus on which criteria are important is absent, and no published integration scheme satisfies all desiderata simultaneously. Additional nontrivial complications stem from simulating systems driven out of equilibrium using existing stochastic integration schemes in conjunction with recently developed nonequilibrium fluctuation theorems. Here, we examine a family of discrete time integration schemes for Langevin dynamics, assessing how each member satisfies a variety of desiderata that have been enumerated in prior efforts to construct suitable Langevin integrators. We show that the incorporation of a novel time step rescaling in the deterministic updates of position and velocity can correct a number of dynamical defects in these integrators. Finally, we identify a particular splitting (related to the velocity Verlet discretization) that has essentially universally appropriate properties for the simulation of Langevin dynamics for molecular systems in equilibrium, nonequilibrium, and path sampling contexts.

  1. Stochastic Stabilityfor Contracting Lorenz Maps and Flows

    Science.gov (United States)

    Metzger, R. J.

    In a previous work [M], we proved the existence of absolutely continuous invariant measures for contracting Lorenz-like maps, and constructed Sinai-Ruelle-Bowen measures f or the flows that generate them. Here, we prove stochastic stability for such one-dimensional maps and use this result to prove that the corresponding flows generating these maps are stochastically stable under small diffusion-type perturbations, even though, as shown by Rovella [Ro], they are persistent only in a measure theoretical sense in a parameter space. For the one-dimensional maps we also prove strong stochastic stability in the sense of Baladi and Viana[BV].

  2. The stochastic goodwill problem

    OpenAIRE

    Marinelli, Carlo

    2003-01-01

    Stochastic control problems related to optimal advertising under uncertainty are considered. In particular, we determine the optimal strategies for the problem of maximizing the utility of goodwill at launch time and minimizing the disutility of a stream of advertising costs that extends until the launch time for some classes of stochastic perturbations of the classical Nerlove-Arrow dynamics. We also consider some generalizations such as problems with constrained budget and with discretionar...

  3. Phase locking of a seven-channel continuous wave fibre laser system by a stochastic parallel gradient algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Volkov, M V; Garanin, S G; Dolgopolov, Yu V; Kopalkin, A V; Kulikov, S M; Sinyavin, D N; Starikov, F A; Sukharev, S A; Tyutin, S V; Khokhlov, S V; Chaparin, D A [Russian Federal Nuclear Center ' All-Russian Research Institute of Experimental Physics' , Sarov, Nizhnii Novgorod region (Russian Federation)

    2014-11-30

    A seven-channel fibre laser system operated by the master oscillator – multichannel power amplifier scheme is the phase locked using a stochastic parallel gradient algorithm. The phase modulators on lithium niobate crystals are controlled by a multichannel electronic unit with the microcontroller processing signals in real time. The dynamic phase locking of the laser system with the bandwidth of 14 kHz is demonstrated, the time of phasing is 3 – 4 ms. (fibre and integrated-optical structures)

  4. Stochastic dynamics of spatial effects in fragmentation of clusters

    International Nuclear Information System (INIS)

    Salinas-Rodriguez, E.; Rodriguez, R.F.; Zamora, J.M.

    1991-01-01

    We use a stochastic approach to study the effects of spatial in homogeneities in the kinetics of a fragmentation model which occurs in cluster breakup and polymer degradation. The analytical form of the cluster size distribution function is obtained for both the discrete and continuous limits. From it we calculate numerically the average size and volume of the clusters, their total concentration and the total scattering of the dispersion in both limits. The influence of spatial effects is explicitly shown in the last two quantities. From our description the equations for the equal-time and the two times density correlation functions are also derived in the continuous limit. Finally, the perspectives and limitations of our approach are discussed (Author)

  5. A stochastic model for neutron simulation considering the spectrum and nuclear properties with continuous dependence of energy

    International Nuclear Information System (INIS)

    Camargo, Dayana Queiroz de

    2011-01-01

    This thesis has developed a stochastic model to simulate the neutrons transport in a heterogeneous environment, considering continuous neutron spectra and the nuclear properties with its continuous dependence on energy. This model was implemented using Monte Carlo method for the propagation of neutrons in different environment. Due to restrictions with respect to the number of neutrons that can be simulated in reasonable computational processing time introduced the variable control volume along the (pseudo-) periodic boundary conditions in order to overcome this problem. The choice of class physical Monte Carlo is due to the fact that it can decompose into simpler constituents the problem of solve a transport equation. The components may be treated separately, these are the propagation and interaction while respecting the laws of energy conservation and momentum, and the relationships that determine the probability of their interaction. We are aware of the fact that the problem approached in this thesis is far from being comparable to building a nuclear reactor, but this discussion the main target was to develop the Monte Carlo model, implement the code in a computer language that allows extensions of modular way. This study allowed a detailed analysis of the influence of energy on the neutron population and its impact on the life cycle of neutrons. From the results, even for a simple geometrical arrangement, we can conclude the need to consider the energy dependence, i.e. an spectral effective multiplication factor should be introduced each energy group separately. (author)

  6. A Stochastic Theory for Deep Bed Filtration Accounting for Dispersion and Size Distributions

    DEFF Research Database (Denmark)

    Shapiro, Alexander; Bedrikovetsky, P. G.

    2010-01-01

    We develop a stochastic theory for filtration of suspensions in porous media. The theory takes into account particle and pore size distributions, as well as the random character of the particle motion, which is described in the framework of the theory of continuous-time random walks (CTRW...

  7. A note on "Multicriteria adaptive paths in stochastic, time-varying networks"

    DEFF Research Database (Denmark)

    Pretolani, Daniele; Nielsen, Lars Relund; Andersen, Kim Allan

    In a recent paper, Opasanon and Miller-Hooks study multicriteria adaptive paths in stochastic time-varying networks. They propose a label correcting algorithm for finding the full set of efficient strategies. In this note we show that their algorithm is not correct, since it is based on a property...... that does not hold in general. Opasanon and Miller-Hooks also propose an algorithm for solving a parametric problem. We give a simplified algorithm which is linear in the input size....

  8. The Limit Behavior of a Stochastic Logistic Model with Individual Time-Dependent Rates

    Directory of Open Access Journals (Sweden)

    Yilun Shang

    2013-01-01

    Full Text Available We investigate a variant of the stochastic logistic model that allows individual variation and time-dependent infection and recovery rates. The model is described as a heterogeneous density dependent Markov chain. We show that the process can be approximated by a deterministic process defined by an integral equation as the population size grows.

  9. Stochastic simulation modeling to determine time to detect Bovine Viral Diarrhea antibodies in bulk tank milk

    DEFF Research Database (Denmark)

    Foddai, Alessandro; Enøe, Claes; Krogh, Kaspar

    2014-01-01

    A stochastic simulation model was developed to estimate the time from introduction ofBovine Viral Diarrhea Virus (BVDV) in a herd to detection of antibodies in bulk tank milk(BTM) samples using three ELISAs. We assumed that antibodies could be detected, after afixed threshold prevalence of seroco......A stochastic simulation model was developed to estimate the time from introduction ofBovine Viral Diarrhea Virus (BVDV) in a herd to detection of antibodies in bulk tank milk(BTM) samples using three ELISAs. We assumed that antibodies could be detected, after afixed threshold prevalence......, which was the most efficient ELISA, could detect antibodiesin the BTM of a large herd 280 days (95% prediction interval: 218; 568) after a transientlyinfected (TI) milking cow has been introduced into the herd. The estimated time to detectionafter introduction of one PI calf was 111 days (44; 605...

  10. CONTINUOUS MODELING OF FOREIGN EXCHANGE RATE OF USD VERSUS TRY

    Directory of Open Access Journals (Sweden)

    Yakup Arı

    2011-01-01

    Full Text Available This study aims to construct continuous-time autoregressive (CAR model and continuous-time GARCH (COGARCH model from discrete time data of foreign exchange rate of United States Dollar (USD versus Turkish Lira (TRY. These processes are solutions to stochastic differential equation Lévy-driven processes. We have shown that CAR(1 and COGARCH(1,1 processes are proper models to represent foreign exchange rate of USD and TRY for different periods of time February 2002- June 2010.

  11. Constructing networks from a dynamical system perspective for multivariate nonlinear time series.

    Science.gov (United States)

    Nakamura, Tomomichi; Tanizawa, Toshihiro; Small, Michael

    2016-03-01

    We describe a method for constructing networks for multivariate nonlinear time series. We approach the interaction between the various scalar time series from a deterministic dynamical system perspective and provide a generic and algorithmic test for whether the interaction between two measured time series is statistically significant. The method can be applied even when the data exhibit no obvious qualitative similarity: a situation in which the naive method utilizing the cross correlation function directly cannot correctly identify connectivity. To establish the connectivity between nodes we apply the previously proposed small-shuffle surrogate (SSS) method, which can investigate whether there are correlation structures in short-term variabilities (irregular fluctuations) between two data sets from the viewpoint of deterministic dynamical systems. The procedure to construct networks based on this idea is composed of three steps: (i) each time series is considered as a basic node of a network, (ii) the SSS method is applied to verify the connectivity between each pair of time series taken from the whole multivariate time series, and (iii) the pair of nodes is connected with an undirected edge when the null hypothesis cannot be rejected. The network constructed by the proposed method indicates the intrinsic (essential) connectivity of the elements included in the system or the underlying (assumed) system. The method is demonstrated for numerical data sets generated by known systems and applied to several experimental time series.

  12. New Results on Passivity Analysis of Stochastic Neural Networks with Time-Varying Delay and Leakage Delay

    Directory of Open Access Journals (Sweden)

    YaJun Li

    2015-01-01

    Full Text Available The passivity problem for a class of stochastic neural networks systems (SNNs with varying delay and leakage delay has been further studied in this paper. By constructing a more effective Lyapunov functional, employing the free-weighting matrix approach, and combining with integral inequality technic and stochastic analysis theory, the delay-dependent conditions have been proposed such that SNNs are asymptotically stable with guaranteed performance. The time-varying delay is divided into several subintervals and two adjustable parameters are introduced; more information about time delay is utilised and less conservative results have been obtained. Examples are provided to illustrate the less conservatism of the proposed method and simulations are given to show the impact of leakage delay on stability of SNNs.

  13. Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis

    Science.gov (United States)

    Wang, Ting; Plecháč, Petr

    2017-12-01

    Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.

  14. Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis.

    Science.gov (United States)

    Wang, Ting; Plecháč, Petr

    2017-12-21

    Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.

  15. A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series

    Science.gov (United States)

    Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D.

    2011-01-01

    Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…

  16. On Discrete Time Control of Continuous Time Systems

    DEFF Research Database (Denmark)

    Poulsen, Niels Kjølstad

    This report is meant as a supplement or an extension to the material used in connection to or after the courses Stochastic Adaptive Control (02421) and Static and Dynamic Optimization (02711) given at the department Department of Informatics and Mathematical Modelling, The Technical University...

  17. Stochastic cooling

    International Nuclear Information System (INIS)

    Bisognano, J.; Leemann, C.

    1982-03-01

    Stochastic cooling is the damping of betatron oscillations and momentum spread of a particle beam by a feedback system. In its simplest form, a pickup electrode detects the transverse positions or momenta of particles in a storage ring, and the signal produced is amplified and applied downstream to a kicker. The time delay of the cable and electronics is designed to match the transit time of particles along the arc of the storage ring between the pickup and kicker so that an individual particle receives the amplified version of the signal it produced at the pick-up. If there were only a single particle in the ring, it is obvious that betatron oscillations and momentum offset could be damped. However, in addition to its own signal, a particle receives signals from other beam particles. In the limit of an infinite number of particles, no damping could be achieved; we have Liouville's theorem with constant density of the phase space fluid. For a finite, albeit large number of particles, there remains a residue of the single particle damping which is of practical use in accumulating low phase space density beams of particles such as antiprotons. It was the realization of this fact that led to the invention of stochastic cooling by S. van der Meer in 1968. Since its conception, stochastic cooling has been the subject of much theoretical and experimental work. The earliest experiments were performed at the ISR in 1974, with the subsequent ICE studies firmly establishing the stochastic cooling technique. This work directly led to the design and construction of the Antiproton Accumulator at CERN and the beginnings of p anti p colliding beam physics at the SPS. Experiments in stochastic cooling have been performed at Fermilab in collaboration with LBL, and a design is currently under development for a anti p accumulator for the Tevatron

  18. Robust H∞ Filtering for Uncertain Neutral Stochastic Systems with Markovian Jumping Parameters and Time Delay

    Directory of Open Access Journals (Sweden)

    Yajun Li

    2015-01-01

    Full Text Available This paper deals with the robust H∞ filter design problem for a class of uncertain neutral stochastic systems with Markovian jumping parameters and time delay. Based on the Lyapunov-Krasovskii theory and generalized Finsler Lemma, a delay-dependent stability condition is proposed to ensure not only that the filter error system is robustly stochastically stable but also that a prescribed H∞ performance level is satisfied for all admissible uncertainties. All obtained results are expressed in terms of linear matrix inequalities which can be easily solved by MATLAB LMI toolbox. Numerical examples are given to show that the results obtained are both less conservative and less complicated in computation.

  19. Quantitative genetic variance and multivariate clines in the Ivyleaf morning glory, Ipomoea hederacea.

    Science.gov (United States)

    Stock, Amanda J; Campitelli, Brandon E; Stinchcombe, John R

    2014-08-19

    Clinal variation is commonly interpreted as evidence of adaptive differentiation, although clines can also be produced by stochastic forces. Understanding whether clines are adaptive therefore requires comparing clinal variation to background patterns of genetic differentiation at presumably neutral markers. Although this approach has frequently been applied to single traits at a time, we have comparatively fewer examples of how multiple correlated traits vary clinally. Here, we characterize multivariate clines in the Ivyleaf morning glory, examining how suites of traits vary with latitude, with the goal of testing for divergence in trait means that would indicate past evolutionary responses. We couple this with analysis of genetic variance in clinally varying traits in 20 populations to test whether past evolutionary responses have depleted genetic variance, or whether genetic variance declines approaching the range margin. We find evidence of clinal differentiation in five quantitative traits, with little evidence of isolation by distance at neutral loci that would suggest non-adaptive or stochastic mechanisms. Within and across populations, the traits that contribute most to population differentiation and clinal trends in the multivariate phenotype are genetically variable as well, suggesting that a lack of genetic variance will not cause absolute evolutionary constraints. Our data are broadly consistent theoretical predictions of polygenic clines in response to shallow environmental gradients. Ecologically, our results are consistent with past findings of natural selection on flowering phenology, presumably due to season-length variation across the range. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  20. Optimal Compensation with Hidden Action and Lump-Sum Payment in a Continuous-Time Model

    International Nuclear Information System (INIS)

    Cvitanic, Jaksa; Wan, Xuhu; Zhang Jianfeng

    2009-01-01

    We consider a problem of finding optimal contracts in continuous time, when the agent's actions are unobservable by the principal, who pays the agent with a one-time payoff at the end of the contract. We fully solve the case of quadratic cost and separable utility, for general utility functions. The optimal contract is, in general, a nonlinear function of the final outcome only, while in the previously solved cases, for exponential and linear utility functions, the optimal contract is linear in the final output value. In a specific example we compute, the first-best principal's utility is infinite, while it becomes finite with hidden action, which is increasing in value of the output. In the second part of the paper we formulate a general mathematical theory for the problem. We apply the stochastic maximum principle to give necessary conditions for optimal contracts. Sufficient conditions are hard to establish, but we suggest a way to check sufficiency using non-convex optimization

  1. Distributed synthesis in continuous time

    DEFF Research Database (Denmark)

    Hermanns, Holger; Krčál, Jan; Vester, Steen

    2016-01-01

    We introduce a formalism modelling communication of distributed agents strictly in continuous-time. Within this framework, we study the problem of synthesising local strategies for individual agents such that a specified set of goal states is reached, or reached with at least a given probability....... The flow of time is modelled explicitly based on continuous-time randomness, with two natural implications: First, the non-determinism stemming from interleaving disappears. Second, when we restrict to a subclass of non-urgent models, the quantitative value problem for two players can be solved in EXPTIME....... Indeed, the explicit continuous time enables players to communicate their states by delaying synchronisation (which is unrestricted for non-urgent models). In general, the problems are undecidable already for two players in the quantitative case and three players in the qualitative case. The qualitative...

  2. Effects of stochastic noise on dynamical decoupling procedures

    Energy Technology Data Exchange (ETDEWEB)

    Bernad, Jozsef Zsolt; Frydrych, Holger; Alber, Gernot [Institut fuer Angewandte Physik, Technische Universitaet Darmstadt, D-64289 Darmstadt (Germany)

    2013-07-01

    Dynamical decoupling is a well-established technique to protect quantum systems from unwanted influences of their environment by exercising active control. It has been used experimentally to drastically increase the lifetime of qubit states in various implementations. The efficiency of different dynamical decoupling schemes defines the lifetime. However, errors in control operations always limit this efficiency. We propose a stochastic model as a possible description of imperfect control pulses and discuss the impact of this kind of error on different decoupling schemes. In the limit of continuous control, i.e. if the number of pulses N → ∞, we derive a stochastic differential equation for the evolution of the density operator of the controlled system and its environment. In the context of this modified time evolution we discuss possibilities of protecting qubit states against environmental noise.

  3. Homogenization of the stochastic Navier–Stokes equation with a stochastic slip boundary condition

    KAUST Repository

    Bessaih, Hakima

    2015-11-02

    The two-dimensional Navier–Stokes equation in a perforated domain with a dynamical slip boundary condition is considered. We assume that the dynamic is driven by a stochastic perturbation on the interior of the domain and another stochastic perturbation on the boundaries of the holes. We consider a scaling (ᵋ for the viscosity and 1 for the density) that will lead to a time-dependent limit problem. However, the noncritical scaling (ᵋ, β > 1) is considered in front of the nonlinear term. The homogenized system in the limit is obtained as a Darcy’s law with memory with two permeabilities and an extra term that is due to the stochastic perturbation on the boundary of the holes. The nonhomogeneity on the boundary contains a stochastic part that yields in the limit an additional term in the Darcy’s law. We use the two-scale convergence method after extending the solution with 0 inside the holes to pass to the limit. By Itô stochastic calculus, we get uniform estimates on the solution in appropriate spaces. Due to the stochastic integral, the pressure that appears in the variational formulation does not have enough regularity in time. This fact made us rely only on the variational formulation for the passage to the limit on the solution. We obtain a variational formulation for the limit that is solution of a Stokes system with two pressures. This two-scale limit gives rise to three cell problems, two of them give the permeabilities while the third one gives an extra term in the Darcy’s law due to the stochastic perturbation on the boundary of the holes.

  4. Stochastic processes

    CERN Document Server

    Borodin, Andrei N

    2017-01-01

    This book provides a rigorous yet accessible introduction to the theory of stochastic processes. A significant part of the book is devoted to the classic theory of stochastic processes. In turn, it also presents proofs of well-known results, sometimes together with new approaches. Moreover, the book explores topics not previously covered elsewhere, such as distributions of functionals of diffusions stopped at different random times, the Brownian local time, diffusions with jumps, and an invariance principle for random walks and local times. Supported by carefully selected material, the book showcases a wealth of examples that demonstrate how to solve concrete problems by applying theoretical results. It addresses a broad range of applications, focusing on concrete computational techniques rather than on abstract theory. The content presented here is largely self-contained, making it suitable for researchers and graduate students alike.

  5. Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory

    KAUST Repository

    Richtarik, Peter; Taká č, Martin

    2017-01-01

    We develop a family of reformulations of an arbitrary consistent linear system into a stochastic problem. The reformulations are governed by two user-defined parameters: a positive definite matrix defining a norm, and an arbitrary discrete or continuous distribution over random matrices. Our reformulation has several equivalent interpretations, allowing for researchers from various communities to leverage their domain specific insights. In particular, our reformulation can be equivalently seen as a stochastic optimization problem, stochastic linear system, stochastic fixed point problem and a probabilistic intersection problem. We prove sufficient, and necessary and sufficient conditions for the reformulation to be exact. Further, we propose and analyze three stochastic algorithms for solving the reformulated problem---basic, parallel and accelerated methods---with global linear convergence rates. The rates can be interpreted as condition numbers of a matrix which depends on the system matrix and on the reformulation parameters. This gives rise to a new phenomenon which we call stochastic preconditioning, and which refers to the problem of finding parameters (matrix and distribution) leading to a sufficiently small condition number. Our basic method can be equivalently interpreted as stochastic gradient descent, stochastic Newton method, stochastic proximal point method, stochastic fixed point method, and stochastic projection method, with fixed stepsize (relaxation parameter), applied to the reformulations.

  6. Stochastic Reformulations of Linear Systems: Algorithms and Convergence Theory

    KAUST Repository

    Richtarik, Peter

    2017-06-04

    We develop a family of reformulations of an arbitrary consistent linear system into a stochastic problem. The reformulations are governed by two user-defined parameters: a positive definite matrix defining a norm, and an arbitrary discrete or continuous distribution over random matrices. Our reformulation has several equivalent interpretations, allowing for researchers from various communities to leverage their domain specific insights. In particular, our reformulation can be equivalently seen as a stochastic optimization problem, stochastic linear system, stochastic fixed point problem and a probabilistic intersection problem. We prove sufficient, and necessary and sufficient conditions for the reformulation to be exact. Further, we propose and analyze three stochastic algorithms for solving the reformulated problem---basic, parallel and accelerated methods---with global linear convergence rates. The rates can be interpreted as condition numbers of a matrix which depends on the system matrix and on the reformulation parameters. This gives rise to a new phenomenon which we call stochastic preconditioning, and which refers to the problem of finding parameters (matrix and distribution) leading to a sufficiently small condition number. Our basic method can be equivalently interpreted as stochastic gradient descent, stochastic Newton method, stochastic proximal point method, stochastic fixed point method, and stochastic projection method, with fixed stepsize (relaxation parameter), applied to the reformulations.

  7. Stochastic process corrosion growth models for pipeline reliability

    International Nuclear Information System (INIS)

    Bazán, Felipe Alexander Vargas; Beck, André Teófilo

    2013-01-01

    Highlights: •Novel non-linear stochastic process corrosion growth model is proposed. •Corrosion rate modeled as random Poisson pulses. •Time to corrosion initiation and inherent time-variability properly represented. •Continuous corrosion growth histories obtained. •Model is shown to precisely fit actual corrosion data at two time points. -- Abstract: Linear random variable corrosion models are extensively employed in reliability analysis of pipelines. However, linear models grossly neglect well-known characteristics of the corrosion process. Herein, a non-linear model is proposed, where corrosion rate is represented as a Poisson square wave process. The resulting model represents inherent time-variability of corrosion growth, produces continuous growth and leads to mean growth at less-than-one power of time. Different corrosion models are adjusted to the same set of actual corrosion data for two inspections. The proposed non-linear random process corrosion growth model leads to the best fit to the data, while better representing problem physics

  8. pth moment exponential stability of stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays.

    Science.gov (United States)

    Wang, Fen; Chen, Yuanlong; Liu, Meichun

    2018-02-01

    Stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays play an increasingly important role in the design and implementation of neural network systems. Under the framework of Filippov solutions, the issues of the pth moment exponential stability of stochastic memristor-based BAM neural networks are investigated. By using the stochastic stability theory, Itô's differential formula and Young inequality, the criteria are derived. Meanwhile, with Lyapunov approach and Cauchy-Schwarz inequality, we derive some sufficient conditions for the mean square exponential stability of the above systems. The obtained results improve and extend previous works on memristor-based or usual neural networks dynamical systems. Four numerical examples are provided to illustrate the effectiveness of the proposed results. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Stochastic quantization and gravity

    International Nuclear Information System (INIS)

    Rumpf, H.

    1984-01-01

    We give a preliminary account of the application of stochastic quantization to the gravitational field. We start in Section I from Nelson's formulation of quantum mechanics as Newtonian stochastic mechanics and only then introduce the Parisi-Wu stochastic quantization scheme on which all the later discussion will be based. In Section II we present a generalization of the scheme that is applicable to fields in physical (i.e. Lorentzian) space-time and treat the free linearized gravitational field in this manner. The most remarkable result of this is the noncausal propagation of conformal gravitons. Moreover the concept of stochastic gauge-fixing is introduced and a complete discussion of all the covariant gauges is given. A special symmetry relating two classes of covariant gauges is exhibited. Finally Section III contains some preliminary remarks on full nonlinear gravity. In particular we argue that in contrast to gauge fields the stochastic gravitational field cannot be transformed to a Gaussian process. (Author)

  10. Entropy Production in Stochastics

    Directory of Open Access Journals (Sweden)

    Demetris Koutsoyiannis

    2017-10-01

    Full Text Available While the modern definition of entropy is genuinely probabilistic, in entropy production the classical thermodynamic definition, as in heat transfer, is typically used. Here we explore the concept of entropy production within stochastics and, particularly, two forms of entropy production in logarithmic time, unconditionally (EPLT or conditionally on the past and present having been observed (CEPLT. We study the theoretical properties of both forms, in general and in application to a broad set of stochastic processes. A main question investigated, related to model identification and fitting from data, is how to estimate the entropy production from a time series. It turns out that there is a link of the EPLT with the climacogram, and of the CEPLT with two additional tools introduced here, namely the differenced climacogram and the climacospectrum. In particular, EPLT and CEPLT are related to slopes of log-log plots of these tools, with the asymptotic slopes at the tails being most important as they justify the emergence of scaling laws of second-order characteristics of stochastic processes. As a real-world application, we use an extraordinary long time series of turbulent velocity and show how a parsimonious stochastic model can be identified and fitted using the tools developed.

  11. Structured Modeling and Analysis of Stochastic Epidemics with Immigration and Demographic Effects.

    Directory of Open Access Journals (Sweden)

    Hendrik Baumann

    Full Text Available Stochastic epidemics with open populations of variable population sizes are considered where due to immigration and demographic effects the epidemic does not eventually die out forever. The underlying stochastic processes are ergodic multi-dimensional continuous-time Markov chains that possess unique equilibrium probability distributions. Modeling these epidemics as level-dependent quasi-birth-and-death processes enables efficient computations of the equilibrium distributions by matrix-analytic methods. Numerical examples for specific parameter sets are provided, which demonstrates that this approach is particularly well-suited for studying the impact of varying rates for immigration, births, deaths, infection, recovery from infection, and loss of immunity.

  12. A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series

    Directory of Open Access Journals (Sweden)

    Charmaine eDemanuele

    2015-10-01

    Full Text Available Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from fMRI blood oxygenation level dependent (BOLD time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC, but not in the primary visual cortex (V1. Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel

  13. Discrete stochastic analogs of Erlang epidemic models.

    Science.gov (United States)

    Getz, Wayne M; Dougherty, Eric R

    2018-12-01

    Erlang differential equation models of epidemic processes provide more realistic disease-class transition dynamics from susceptible (S) to exposed (E) to infectious (I) and removed (R) categories than the ubiquitous SEIR model. The latter is itself is at one end of the spectrum of Erlang SE[Formula: see text]I[Formula: see text]R models with [Formula: see text] concatenated E compartments and [Formula: see text] concatenated I compartments. Discrete-time models, however, are computationally much simpler to simulate and fit to epidemic outbreak data than continuous-time differential equations, and are also much more readily extended to include demographic and other types of stochasticity. Here we formulate discrete-time deterministic analogs of the Erlang models, and their stochastic extension, based on a time-to-go distributional principle. Depending on which distributions are used (e.g. discretized Erlang, Gamma, Beta, or Uniform distributions), we demonstrate that our formulation represents both a discretization of Erlang epidemic models and generalizations thereof. We consider the challenges of fitting SE[Formula: see text]I[Formula: see text]R models and our discrete-time analog to data (the recent outbreak of Ebola in Liberia). We demonstrate that the latter performs much better than the former; although confining fits to strict SEIR formulations reduces the numerical challenges, but sacrifices best-fit likelihood scores by at least 7%.

  14. Global output feedback stabilisation of stochastic high-order feedforward nonlinear systems with time-delay

    Science.gov (United States)

    Zhang, Kemei; Zhao, Cong-Ran; Xie, Xue-Jun

    2015-12-01

    This paper considers the problem of output feedback stabilisation for stochastic high-order feedforward nonlinear systems with time-varying delay. By using the homogeneous domination theory and solving several troublesome obstacles in the design and analysis, an output feedback controller is constructed to drive the closed-loop system globally asymptotically stable in probability.

  15. Simple stochastic simulation.

    Science.gov (United States)

    Schilstra, Maria J; Martin, Stephen R

    2009-01-01

    Stochastic simulations may be used to describe changes with time of a reaction system in a way that explicitly accounts for the fact that molecules show a significant degree of randomness in their dynamic behavior. The stochastic approach is almost invariably used when small numbers of molecules or molecular assemblies are involved because this randomness leads to significant deviations from the predictions of the conventional deterministic (or continuous) approach to the simulation of biochemical kinetics. Advances in computational methods over the three decades that have elapsed since the publication of Daniel Gillespie's seminal paper in 1977 (J. Phys. Chem. 81, 2340-2361) have allowed researchers to produce highly sophisticated models of complex biological systems. However, these models are frequently highly specific for the particular application and their description often involves mathematical treatments inaccessible to the nonspecialist. For anyone completely new to the field to apply such techniques in their own work might seem at first sight to be a rather intimidating prospect. However, the fundamental principles underlying the approach are in essence rather simple, and the aim of this article is to provide an entry point to the field for a newcomer. It focuses mainly on these general principles, both kinetic and computational, which tend to be not particularly well covered in specialist literature, and shows that interesting information may even be obtained using very simple operations in a conventional spreadsheet.

  16. Continuous Markovian Logics

    DEFF Research Database (Denmark)

    Mardare, Radu Iulian; Cardelli, Luca; Larsen, Kim Guldstrand

    2012-01-01

    Continuous Markovian Logic (CML) is a multimodal logic that expresses quantitative and qualitative properties of continuous-time labelled Markov processes with arbitrary (analytic) state-spaces, henceforth called continuous Markov processes (CMPs). The modalities of CML evaluate the rates...... of the exponentially distributed random variables that characterize the duration of the labeled transitions of a CMP. In this paper we present weak and strong complete axiomatizations for CML and prove a series of metaproperties, including the finite model property and the construction of canonical models. CML...... characterizes stochastic bisimilarity and it supports the definition of a quantified extension of the satisfiability relation that measures the "compatibility" between a model and a property. In this context, the metaproperties allows us to prove two robustness theorems for the logic stating that one can...

  17. Parametric sensitivity analysis for stochastic molecular systems using information theoretic metrics

    Energy Technology Data Exchange (ETDEWEB)

    Tsourtis, Anastasios, E-mail: tsourtis@uoc.gr [Department of Mathematics and Applied Mathematics, University of Crete, Crete (Greece); Pantazis, Yannis, E-mail: pantazis@math.umass.edu; Katsoulakis, Markos A., E-mail: markos@math.umass.edu [Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts 01003 (United States); Harmandaris, Vagelis, E-mail: harman@uoc.gr [Department of Mathematics and Applied Mathematics, University of Crete, and Institute of Applied and Computational Mathematics (IACM), Foundation for Research and Technology Hellas (FORTH), GR-70013 Heraklion, Crete (Greece)

    2015-07-07

    In this paper, we present a parametric sensitivity analysis (SA) methodology for continuous time and continuous space Markov processes represented by stochastic differential equations. Particularly, we focus on stochastic molecular dynamics as described by the Langevin equation. The utilized SA method is based on the computation of the information-theoretic (and thermodynamic) quantity of relative entropy rate (RER) and the associated Fisher information matrix (FIM) between path distributions, and it is an extension of the work proposed by Y. Pantazis and M. A. Katsoulakis [J. Chem. Phys. 138, 054115 (2013)]. A major advantage of the pathwise SA method is that both RER and pathwise FIM depend only on averages of the force field; therefore, they are tractable and computable as ergodic averages from a single run of the molecular dynamics simulation both in equilibrium and in non-equilibrium steady state regimes. We validate the performance of the extended SA method to two different molecular stochastic systems, a standard Lennard-Jones fluid and an all-atom methane liquid, and compare the obtained parameter sensitivities with parameter sensitivities on three popular and well-studied observable functions, namely, the radial distribution function, the mean squared displacement, and the pressure. Results show that the RER-based sensitivities are highly correlated with the observable-based sensitivities.

  18. Development of Fast-Time Stochastic Airport Ground and Runway Simulation Model and Its Traffic Analysis

    Directory of Open Access Journals (Sweden)

    Ryota Mori

    2015-01-01

    Full Text Available Airport congestion, in particular congestion of departure aircraft, has already been discussed by other researches. Most solutions, though, fail to account for uncertainties. Since it is difficult to remove uncertainties of the operations in the real world, a strategy should be developed assuming such uncertainties exist. Therefore, this research develops a fast-time stochastic simulation model used to validate various methods in order to decrease airport congestion level under existing uncertainties. The surface movement data is analyzed first, and the uncertainty level is obtained. Next, based on the result of data analysis, the stochastic simulation model is developed. The model is validated statistically and the characteristics of airport operation under existing uncertainties are investigated.

  19. Automated Flight Routing Using Stochastic Dynamic Programming

    Science.gov (United States)

    Ng, Hok K.; Morando, Alex; Grabbe, Shon

    2010-01-01

    Airspace capacity reduction due to convective weather impedes air traffic flows and causes traffic congestion. This study presents an algorithm that reroutes flights in the presence of winds, enroute convective weather, and congested airspace based on stochastic dynamic programming. A stochastic disturbance model incorporates into the reroute design process the capacity uncertainty. A trajectory-based airspace demand model is employed for calculating current and future airspace demand. The optimal routes minimize the total expected traveling time, weather incursion, and induced congestion costs. They are compared to weather-avoidance routes calculated using deterministic dynamic programming. The stochastic reroutes have smaller deviation probability than the deterministic counterpart when both reroutes have similar total flight distance. The stochastic rerouting algorithm takes into account all convective weather fields with all severity levels while the deterministic algorithm only accounts for convective weather systems exceeding a specified level of severity. When the stochastic reroutes are compared to the actual flight routes, they have similar total flight time, and both have about 1% of travel time crossing congested enroute sectors on average. The actual flight routes induce slightly less traffic congestion than the stochastic reroutes but intercept more severe convective weather.

  20. Quantum stochastics

    CERN Document Server

    Chang, Mou-Hsiung

    2015-01-01

    The classical probability theory initiated by Kolmogorov and its quantum counterpart, pioneered by von Neumann, were created at about the same time in the 1930s, but development of the quantum theory has trailed far behind. Although highly appealing, the quantum theory has a steep learning curve, requiring tools from both probability and analysis and a facility for combining the two viewpoints. This book is a systematic, self-contained account of the core of quantum probability and quantum stochastic processes for graduate students and researchers. The only assumed background is knowledge of the basic theory of Hilbert spaces, bounded linear operators, and classical Markov processes. From there, the book introduces additional tools from analysis, and then builds the quantum probability framework needed to support applications to quantum control and quantum information and communication. These include quantum noise, quantum stochastic calculus, stochastic quantum differential equations, quantum Markov semigrou...

  1. Estimation of parameter sensitivities for stochastic reaction networks

    KAUST Repository

    Gupta, Ankit

    2016-01-07

    Quantification of the effects of parameter uncertainty is an important and challenging problem in Systems Biology. We consider this problem in the context of stochastic models of biochemical reaction networks where the dynamics is described as a continuous-time Markov chain whose states represent the molecular counts of various species. For such models, effects of parameter uncertainty are often quantified by estimating the infinitesimal sensitivities of some observables with respect to model parameters. The aim of this talk is to present a holistic approach towards this problem of estimating parameter sensitivities for stochastic reaction networks. Our approach is based on a generic formula which allows us to construct efficient estimators for parameter sensitivity using simulations of the underlying model. We will discuss how novel simulation techniques, such as tau-leaping approximations, multi-level methods etc. can be easily integrated with our approach and how one can deal with stiff reaction networks where reactions span multiple time-scales. We will demonstrate the efficiency and applicability of our approach using many examples from the biological literature.

  2. The appreciation of stochastic motion in particle accelerators

    International Nuclear Information System (INIS)

    Symon, Keith; Sessler, Andrew

    2003-01-01

    A description is given of the analytic and numerical work, performed from July 1955 through August 1956, so as to develop, and then study, the process of making intense proton beams, suitable for colliding beams. It is shown how this investigation led, in a most natural way, to the realization that stochasticity can arise in a simple Hamiltonian system. Furthermore, the criterion for the onset of stochasticity was understood, and carefully studied, in two different situations. The first situation was the proposed (and subsequently used) ''stacking process'' for developing an intense beam, where stochasticity occurs as additional particles are added to the intense circulating beam. The second situation occurs when one seeks to develop ''stochastic accelerators'' in which particles are accelerated (continuously) by a collection of radio frequency systems. It was in the last connection that the well-known criterion for stochasticity, resonance overlap, was obtained

  3. Language Emptiness of Continuous-Time Parametric Timed Automata

    DEFF Research Database (Denmark)

    Benes, Nikola; Bezdek, Peter; Larsen, Kim Guldstrand

    2015-01-01

    Parametric timed automata extend the standard timed automata with the possibility to use parameters in the clock guards. In general, if the parameters are real-valued, the problem of language emptiness of such automata is undecidable even for various restricted subclasses. We thus focus on the case...... where parameters are assumed to be integer-valued, while the time still remains continuous. On the one hand, we show that the problem remains undecidable for parametric timed automata with three clocks and one parameter. On the other hand, for the case with arbitrary many clocks where only one......-time semantics only. To the best of our knowledge, this is the first positive result in the case of continuous-time and unbounded integer parameters, except for the rather simple case of single-clock automata....

  4. Stochastic models: theory and simulation.

    Energy Technology Data Exchange (ETDEWEB)

    Field, Richard V., Jr.

    2008-03-01

    Many problems in applied science and engineering involve physical phenomena that behave randomly in time and/or space. Examples are diverse and include turbulent flow over an aircraft wing, Earth climatology, material microstructure, and the financial markets. Mathematical models for these random phenomena are referred to as stochastic processes and/or random fields, and Monte Carlo simulation is the only general-purpose tool for solving problems of this type. The use of Monte Carlo simulation requires methods and algorithms to generate samples of the appropriate stochastic model; these samples then become inputs and/or boundary conditions to established deterministic simulation codes. While numerous algorithms and tools currently exist to generate samples of simple random variables and vectors, no cohesive simulation tool yet exists for generating samples of stochastic processes and/or random fields. There are two objectives of this report. First, we provide some theoretical background on stochastic processes and random fields that can be used to model phenomena that are random in space and/or time. Second, we provide simple algorithms that can be used to generate independent samples of general stochastic models. The theory and simulation of random variables and vectors is also reviewed for completeness.

  5. Monte Carlo simulation of fully Markovian stochastic geometries

    International Nuclear Information System (INIS)

    Lepage, Thibaut; Delaby, Lucie; Malvagi, Fausto; Mazzolo, Alain

    2010-01-01

    The interest in resolving the equation of transport in stochastic media has continued to increase these last years. For binary stochastic media it is often assumed that the geometry is Markovian, which is never the case in usual environments. In the present paper, based on rigorous mathematical theorems, we construct fully two-dimensional Markovian stochastic geometries and we study their main properties. In particular, we determine a percolation threshold p c , equal to 0.586 ± 0.0015 for such geometries. Finally, Monte Carlo simulations are performed through these geometries and the results compared to homogeneous geometries. (author)

  6. A data driven nonlinear stochastic model for blood glucose dynamics.

    Science.gov (United States)

    Zhang, Yan; Holt, Tim A; Khovanova, Natalia

    2016-03-01

    The development of adequate mathematical models for blood glucose dynamics may improve early diagnosis and control of diabetes mellitus (DM). We have developed a stochastic nonlinear second order differential equation to describe the response of blood glucose concentration to food intake using continuous glucose monitoring (CGM) data. A variational Bayesian learning scheme was applied to define the number and values of the system's parameters by iterative optimisation of free energy. The model has the minimal order and number of parameters to successfully describe blood glucose dynamics in people with and without DM. The model accounts for the nonlinearity and stochasticity of the underlying glucose-insulin dynamic process. Being data-driven, it takes full advantage of available CGM data and, at the same time, reflects the intrinsic characteristics of the glucose-insulin system without detailed knowledge of the physiological mechanisms. We have shown that the dynamics of some postprandial blood glucose excursions can be described by a reduced (linear) model, previously seen in the literature. A comprehensive analysis demonstrates that deterministic system parameters belong to different ranges for diabetes and controls. Implications for clinical practice are discussed. This is the first study introducing a continuous data-driven nonlinear stochastic model capable of describing both DM and non-DM profiles. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

  7. Stochastic synaptic plasticity with memristor crossbar arrays

    KAUST Repository

    Naous, Rawan

    2016-11-01

    Memristive devices have been shown to exhibit slow and stochastic resistive switching behavior under low-voltage, low-current operating conditions. Here we explore such mechanisms to emulate stochastic plasticity in memristor crossbar synapse arrays. Interfaced with integrate-and-fire spiking neurons, the memristive synapse arrays are capable of implementing stochastic forms of spike-timing dependent plasticity which parallel mean-rate models of stochastic learning with binary synapses. We present theory and experiments with spike-based stochastic learning in memristor crossbar arrays, including simplified modeling as well as detailed physical simulation of memristor stochastic resistive switching characteristics due to voltage and current induced filament formation and collapse. © 2016 IEEE.

  8. Stochastic synaptic plasticity with memristor crossbar arrays

    KAUST Repository

    Naous, Rawan; Al-Shedivat, Maruan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N.

    2016-01-01

    Memristive devices have been shown to exhibit slow and stochastic resistive switching behavior under low-voltage, low-current operating conditions. Here we explore such mechanisms to emulate stochastic plasticity in memristor crossbar synapse arrays. Interfaced with integrate-and-fire spiking neurons, the memristive synapse arrays are capable of implementing stochastic forms of spike-timing dependent plasticity which parallel mean-rate models of stochastic learning with binary synapses. We present theory and experiments with spike-based stochastic learning in memristor crossbar arrays, including simplified modeling as well as detailed physical simulation of memristor stochastic resistive switching characteristics due to voltage and current induced filament formation and collapse. © 2016 IEEE.

  9. Using random walk in models specified by stochastic differential equations to determine the best expression for the bacterial growth rate

    DEFF Research Database (Denmark)

    method allows us to develop a new expression for the growth rate. The method is based on the stochastic continuous-discrete time state-space model, with a continuous-time state equation (a stochastic differential equation, SDE) combined with a discrete-time measurement equation. In our study the SDE...... described by Kristensen et. al [2]. The resulting time series allows us graphically to inspect the functional dependence of the growth rate on the substrate content. From the method described above we find three new plausible expressions for μ(S). Therefore we apply the likelihood-ratio test to compare...... for the Monod expression. Thus, the method was applied to successfully determine a significant better expression for the substrate dependent growth expression, and we find the method generally applicable for model development. References [1] Kristensen NR, Madsen H, Jørgensen, SB (2004) A method for systematic...

  10. On stochastic differential equations with random delay

    International Nuclear Information System (INIS)

    Krapivsky, P L; Luck, J M; Mallick, K

    2011-01-01

    We consider stochastic dynamical systems defined by differential equations with a uniform random time delay. The latter equations are shown to be equivalent to deterministic higher-order differential equations: for an nth-order equation with random delay, the corresponding deterministic equation has order n + 1. We analyze various examples of dynamical systems of this kind, and find a number of unusual behaviors. For instance, for the harmonic oscillator with random delay, the energy grows as exp((3/2) t 2/3 ) in reduced units. We then investigate the effect of introducing a discrete time step ε. At variance with the continuous situation, the discrete random recursion relations thus obtained have intrinsic fluctuations. The crossover between the fluctuating discrete problem and the deterministic continuous one as ε goes to zero is studied in detail on the example of a first-order linear differential equation

  11. A continuous-time/discrete-time mixed audio-band sigma delta ADC

    International Nuclear Information System (INIS)

    Liu Yan; Hua Siliang; Wang Donghui; Hou Chaohuan

    2011-01-01

    This paper introduces a mixed continuous-time/discrete-time, single-loop, fourth-order, 4-bit audio-band sigma delta ADC that combines the benefits of continuous-time and discrete-time circuits, while mitigating the challenges associated with continuous-time design. Measurement results show that the peak SNR of this ADC reaches 100 dB and the total power consumption is less than 30 mW. (semiconductor integrated circuits)

  12. Reliability-based Dynamic Network Design with Stochastic Networks

    NARCIS (Netherlands)

    Li, H.

    2009-01-01

    Transportation systems are stochastic and dynamic systems. The road capacities and the travel demand are fluctuating from time to time within a day and at the same time from day to day. For road users, the travel time and travel costs experienced over time and space are stochastic, thus desire

  13. Coarse-grained stochastic processes and kinetic Monte Carlo simulators for the diffusion of interacting particles

    Science.gov (United States)

    Katsoulakis, Markos A.; Vlachos, Dionisios G.

    2003-11-01

    We derive a hierarchy of successively coarse-grained stochastic processes and associated coarse-grained Monte Carlo (CGMC) algorithms directly from the microscopic processes as approximations in larger length scales for the case of diffusion of interacting particles on a lattice. This hierarchy of models spans length scales between microscopic and mesoscopic, satisfies a detailed balance, and gives self-consistent fluctuation mechanisms whose noise is asymptotically identical to the microscopic MC. Rigorous, detailed asymptotics justify and clarify these connections. Gradient continuous time microscopic MC and CGMC simulations are compared under far from equilibrium conditions to illustrate the validity of our theory and delineate the errors obtained by rigorous asymptotics. Information theory estimates are employed for the first time to provide rigorous error estimates between the solutions of microscopic MC and CGMC, describing the loss of information during the coarse-graining process. Simulations under periodic boundary conditions are used to verify the information theory error estimates. It is shown that coarse-graining in space leads also to coarse-graining in time by q2, where q is the level of coarse-graining, and overcomes in part the hydrodynamic slowdown. Operation counting and CGMC simulations demonstrate significant CPU savings in continuous time MC simulations that vary from q3 for short potentials to q4 for long potentials. Finally, connections of the new coarse-grained stochastic processes to stochastic mesoscopic and Cahn-Hilliard-Cook models are made.

  14. Stochastic processes dominate during boreal bryophyte community assembly.

    Science.gov (United States)

    Fenton, Nicole J; Bergeron, Yves

    2013-09-01

    Why are plant species found in certain locations and not in others? The study of community assembly rules has attempted to answer this question, and many studies articulate the historic dichotomy of deterministic (predictable niches) vs. stochastic (random or semi-random processes). The study of successional sequences to determine whether they converge, as would be expected by deterministic theory, or diverge, as stochastic theory would suggest, has been one method used to investigate this question. In this article we ask the question: Do similar boreal bryophyte communities develop in the similar habitat created by convergent succession after fires of different severities? Or do the stochastic processes generated by fires of different severity lead to different communities? Specifically we predict that deterministic structure will be more important for large forest-floor species than stochastic processes, and that the inverse will be true for small bryophyte species. We used multivariate regression trees and model selection to determine the relative weight of structure (forest structure, substrates, soil structure) and processes (fire severity) for two groups of bryophyte species sampled in 12 sites (seven high-severity and five low-severity fires). Contrary to our first hypothesis, processes were as important for large forest-floor bryophytes as for small pocket species. Fire severity, its interaction with the quality of available habitat, and its impact on the creation of biological legacies played dominant roles in determining community structure. In this study, sites with nearly identical forest structure, generated via convergent succession after high- and low-severity fire, were compared to see whether these sites supported similar bryophyte communities. While similar to some degree, both the large forest-floor species and the pocket species differed after high-severity fire compared to low-severity fire. This result suggests that the "how," or process of

  15. Stochastic Resonance-Like and Resonance Suppression-Like Phenomena in a Bistable System with Time Delay and Additive Noise

    International Nuclear Information System (INIS)

    Shu Chang-Zheng; Nie Lin-Ru; Zhou Zhong-Rao

    2012-01-01

    Stochastic resonance (SR)-like and resonance suppression (RS)-like phenomena in a time-delayed bistable system driven by additive white noise are investigated by means of stochastic simulations of the power spectrum, the quality factor of the power spectrum, and the mean first-passage time (MFPT) of the system. The calculative results indicate that: (i) as the system is driven by a small periodic signal, the quality factor as a function delay time exhibits a maximal value at smaller noise intensities, i.e., an SR-like phenomenon. With the increment in additive noise intensity, the extremum gradually disappears and the quality factor decreases monotonously with delay time. (ii) As the additive noise intensity is smaller, the curve of the MFPT with respect to delay time displays a peak, i.e., an RS-like phenomenon. At higher levels of noise, however, the non-monotonic behavior is lost. (general)

  16. Universality in stochastic exponential growth.

    Science.gov (United States)

    Iyer-Biswas, Srividya; Crooks, Gavin E; Scherer, Norbert F; Dinner, Aaron R

    2014-07-11

    Recent imaging data for single bacterial cells reveal that their mean sizes grow exponentially in time and that their size distributions collapse to a single curve when rescaled by their means. An analogous result holds for the division-time distributions. A model is needed to delineate the minimal requirements for these scaling behaviors. We formulate a microscopic theory of stochastic exponential growth as a Master Equation that accounts for these observations, in contrast to existing quantitative models of stochastic exponential growth (e.g., the Black-Scholes equation or geometric Brownian motion). Our model, the stochastic Hinshelwood cycle (SHC), is an autocatalytic reaction cycle in which each molecular species catalyzes the production of the next. By finding exact analytical solutions to the SHC and the corresponding first passage time problem, we uncover universal signatures of fluctuations in exponential growth and division. The model makes minimal assumptions, and we describe how more complex reaction networks can reduce to such a cycle. We thus expect similar scalings to be discovered in stochastic processes resulting in exponential growth that appear in diverse contexts such as cosmology, finance, technology, and population growth.

  17. Memory effects on stochastic resonance

    Science.gov (United States)

    Neiman, Alexander; Sung, Wokyung

    1996-02-01

    We study the phenomenon of stochastic resonance (SR) in a bistable system with internal colored noise. In this situation the system possesses time-dependent memory friction connected with noise via the fluctuation-dissipation theorem, so that in the absence of periodic driving the system approaches the thermodynamic equilibrium state. For this non-Markovian case we find that memory usually suppresses stochastic resonance. However, for a large memory time SR can be enhanced by the memory.

  18. RES: Regularized Stochastic BFGS Algorithm

    Science.gov (United States)

    Mokhtari, Aryan; Ribeiro, Alejandro

    2014-12-01

    RES, a regularized stochastic version of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method is proposed to solve convex optimization problems with stochastic objectives. The use of stochastic gradient descent algorithms is widespread, but the number of iterations required to approximate optimal arguments can be prohibitive in high dimensional problems. Application of second order methods, on the other hand, is impracticable because computation of objective function Hessian inverses incurs excessive computational cost. BFGS modifies gradient descent by introducing a Hessian approximation matrix computed from finite gradient differences. RES utilizes stochastic gradients in lieu of deterministic gradients for both, the determination of descent directions and the approximation of the objective function's curvature. Since stochastic gradients can be computed at manageable computational cost RES is realizable and retains the convergence rate advantages of its deterministic counterparts. Convergence results show that lower and upper bounds on the Hessian egeinvalues of the sample functions are sufficient to guarantee convergence to optimal arguments. Numerical experiments showcase reductions in convergence time relative to stochastic gradient descent algorithms and non-regularized stochastic versions of BFGS. An application of RES to the implementation of support vector machines is developed.

  19. 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

  20. Improved result on stability analysis of discrete stochastic neural networks with time delay

    International Nuclear Information System (INIS)

    Wu Zhengguang; Su Hongye; Chu Jian; Zhou Wuneng

    2009-01-01

    This Letter investigates the problem of exponential stability for discrete stochastic time-delay neural networks. By defining a novel Lyapunov functional, an improved delay-dependent exponential stability criterion is established in terms of linear matrix inequality (LMI) approach. Meanwhile, the computational complexity of the newly established stability condition is reduced because less variables are involved. Numerical example is given to illustrate the effectiveness and the benefits of the proposed method.