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Markov Random Flights

Markov Random Flights is the first systematic presentation of the theory of Markov random flights in the Euclidean spaces of different;

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Random Motions in Markov and SemiMarkov Random Environments 2

This book is the second of two volumes on random motions in Markov and semi-Markov random environments. This second volume focuses on high;

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Random Motions in Markov and SemiMarkov Random Environments 1

This book is the first of two volumes on random motions in Markov and semi-Markov random environments. This first volume focuses on;

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Stochastic Analysis for Gaussian Random Processes and Fields

singularity of Gaussian random fields (including a generalization of the Girsanov theorem), the book concludes with the Markov property of Gaussian;

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Stochastic Analysis for Gaussian Random Processes and Fields

of Gaussian random fields (including a generalization of the Girsanov theorem), the book concludes with the Markov property of Gaussian random fields;

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Markov-modulated Processes And Semiregenerative Phenomena

The book presents a coherent treatment of Markov random walks and Markov additive processes together with their applications. Part I;

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Markov Chains

Markov chains are central to the understanding of random processes. This is not only because they pervade the applications of random;

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Statistical Inference for Piecewise-deterministic Markov Processes

Piecewise-deterministic Markov processes form a class of stochastic models with a sizeable scope of applications: biology, insurance;

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A First Course in Probability and Markov Chains

in Probability and Markov Chains presents an introduction to the basic elements in probability and focuses on two main areas. The first part explores;

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Topics in the Constructive Theory of Countable Markov Chains

Markov chains are an important idea, related to random walks, which crops up widely in applied stochastic analysis. They are used, for;

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Topics in the Constructive Theory of Countable Markov Chains

Markov chains are an important idea, related to random walks, which crops up widely in applied stochastic analysis. They are used, for;

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Markov Random Field Modeling in Image Analysis

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables;

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Probability An Introduction 2Nd

' students in cognate fields. It is suitable for a first course in probability, plus a follow-up course in random processes including Markov chains;

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Studies in the Theory of Random Processes

limit theorems for Markov processes. Specialists, researchers, and students in the field of probability will find it a source of important;

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Hidden Markov Models In Finance

A number of methodologies have been employed to provide decision making solutions globalized markets. Hidden Markov Models in Finance;

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Random Dynamical Systems

of irreducible Markov processes, especially Markov chains, is surveyed in Chapter 2. Equilibrium and long run stability of a dynamical system in which;

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Random Dynamical Systems

of irreducible Markov processes, especially Markov chains, is surveyed in Chapter 2. Equilibrium and long run stability of a dynamical system in which;

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Gaussian Markov Random Fields

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to;

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Probability for Engineering, Mathematics, and Science, International Edition

characterize random variables, because this knowledge is essential in performing parametric statistical analysis. Explanations include the why as;

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Gaussian Markov Random Fields

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to;

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Hybrid Random Fields

of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these;

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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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Stability and Stabilization of Nonlinear Systems with Random Structures

in the direct Lyapunov's method. The analysis focuses on dynamic systems with random Markov parameters. This high-level research text;

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Markov Logic

templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its;

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Finite Mathematics Mod & Aplicati Set

; Simplex Methods; Application Models; Set and Probability Relationships; Random Variables and Probability Distributions; Markov Chains;

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Graphical Models for Machine Learning and Digital Communication

learning, data compression, and channel coding. Using probabilistic structures such as Bayesian belief networks and Markov random fields, he;

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