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Semi-Dirichlet Forms and Markov Processes

wide class of Markov processes and analytic theory which do not possess the dual Markov processes. In particular, under the semi-Dirichlet form;

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Symmetric Markov Processes, Time Change, and Boundary Theory (LMS-35)

Dirichlet spaces, and the important roles such advances play in the boundary theory of symmetric Markov processes. This volume is an ideal resource;

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Dirichlet Forms and Symmetric Markov Processes

book addresses to researchers and graduate students who wish to comprehend the area of Dirichlet forms and symmetric Markov processes.;

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Semi-Markov Processes

Semi-Markov Processes: Applications in System Reliability and Maintenance is a modern view of discrete state space and continuous time;

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Introduction to Hidden Semi-Markov Models

undergraduates and graduates. The authors then introduce semi-Markov chains and hidden semi-Markov chains, before developing related estimation and;

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Statistical Topics and Stochastic Models for Dependent Data with Applications

, autoregressive and piecewise deterministic Markov models. The material is divided into three parts corresponding to: (i) Markov and semi-Markov processes;

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Pseudo Differential Operators & Markov Processes

-parameter operator semigroups with emphasis on Feller semigroups and Lp-sub-Markovian semigroups. In addition, Dirichlet forms are treated. The book;

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Random Motions in Markov and SemiMarkov Random Environments 2

part contains applications of Markov and semi-Markov motions in mathematical finance. It includes applications of telegraph processes;

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Markov Renewal and Piecewise Deterministic Processes

deterministic functions of CSMPs (Completed Semi-Markov Processes), i.e. semi-Markov processes completed to become Markov processes. This remark leads;

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Stochastic Systems In Merging Phase Space

integral functionals, increment processes and impulsive processes. All these systems are switched by Markov and semi-Markov processes whose phase;

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Stochastic Analysis and Applications in Physics

the mathematics of Markov processes and the physics of quantum systems. The deterministic-stochastic interface is addressed, as are Euclidean;

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Asymptotic Analyses for Complex Evolutionary Systems with Markov and Semi-Markov Switching Using Approximation Schemes

This book analyzes stochastic evolutionary models under the impulse of diffusion, as well as Markov and semi-Markov switches. Models are;

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Hyperfinite Dirichlet Forms and Stochastic Processes

in spirit to the discrete classical formulation of Dirichlet space theory by Beurling and Deny (1958). The discrete infinitesimal setup makes it;

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Probability And Stochastic Processes

for random variables and processes: Bernoulli, multinomial, exponential, Gamma, Beta, Dirichlet, Poisson, Gaussian, Chi2, ordered variables;

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Probability And Stochastic Processes

for random variables and processes: Bernoulli, multinomial, exponential, Gamma, Beta, Dirichlet, Poisson, Gaussian, Chi2, ordered variables;

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Mathematical Physics at Saint-Flour

Gross, Leonard: Thermodynamics, statistical mechanics, and random fields.-Foellmer, Hans: Random fields and diffusion processes.- Nelson;

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Stochastic Analysis for Gaussian Random Processes and Fields

Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert space methods to study deep analytic;

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Stochastic Analysis for Gaussian Random Processes and Fields

Stochastic Analysis for Gaussian Random Processes and Fields: With Applications presents Hilbert space methods to study deep analytic;

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Modern Trends in Controlled Stochastic Processes:

methods for Markov and semi-Markov decision processes, optimal stopping of Markov processes, stochastic games, problems with partial information;

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Markov Chains and Decision Processes for Engineers and Managers

Markov decision processes in a single volume, Markov Chains and Decision Processes for Engineers and Managers supplies a highly detailed;

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Markov Chains and Decision Processes for Engineers and Managers

Markov decision processes in a single volume, Markov Chains and Decision Processes for Engineers and Managers supplies a highly detailed;

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New Computational Methods in Power System Reliability

simulation and with random processes methods, Semi-Markov and Markov reward models and genetic algorithm. The book can be considered as complementary;

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New Computational Methods in Power System Reliability

simulation and with random processes methods, Semi-Markov and Markov reward models and genetic algorithm. The book can be considered as complementary;

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Introduction to Stochastic Models

as physics, engineering, biology and genetics, economics and social sciences. It covers Markov and semi-Markov models, as well as their;

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Basic Stochastic Processes

This book presents basic stochastic processes, stochastic calculus including Levy processes on one hand, and Markov and Semi Markov models;

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Visual Analysis of Behaviour

, hidden Markov models, human silhouette shapes, latent Dirichlet allocation, local binary patterns, locality preserving projection, and Markov;

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Modeling, Analysis, Design and Control of Stochastic Systems

, cumulative processes and semi-Markov processes. All the material is illustrated with many examples, and the book emphasises numerical answers to the;

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