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Partially Observed Markov Decision Proce

social learning, adaptive radars and sequential detection), this book focuses on the conceptual foundations of partially observed Markov decision;

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Markov Decision Processes in Artificial Intelligence

Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as;

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Bayesian Networks and Decision Graphs

, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov;

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Probabilistic Graphical Models

models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical;

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Probabilistic Graphical Models

models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical;

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Markov Decision Processes

Examines several fundamentals concerning the manner in which Markov decision problems may be properly formulated and the determination;

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Markov Chains and Decision Processes for Engineers and Managers

service systems. However, most books on Markov chains or decision processes are often either highly theoretical, with few examples, or highly;

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Markov Chains and Decision Processes for Engineers and Managers

service systems. However, most books on Markov chains or decision processes are often either highly theoretical, with few examples, or highly;

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Competitive Markov Decision Processes

This book is intended as a text covering the central concepts and techniques of Competitive Markov Decision Processes. It is an attempt to;

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Competitive Markov Decision Processes

This book is intended as a text covering the central concepts and techniques of Competitive Markov Decision Processes. It is an attempt to;

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Selected Topics on Continuous-Time Controlled Markov Chains and Markov Games

This book concerns continuous-time controlled Markov chains, also known as continuous-time Markov decision processes. They form a class;

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Sequential Change Detection and Hypothesis Testing

, that is, when the observed data is dependent and non-identically distributed. Previous work has primarily focused on changepoint detection with;

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Sequential Change Detection and Hypothesis Testing

, that is, when the observed data is dependent and non-identically distributed. Previous work has primarily focused on changepoint detection with;

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Nonlinear Financial Econometrics

This book proposes new methods to value equity and model the Markowitz efficient frontier using Markov switching models and provide new;

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Nonlinear Financial Econometrics

This book proposes new methods to value equity and model the Markowitz efficient frontier using Markov switching models and provide new;

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Markov Chains

. The chapter then covers the basic theories and algorithms for hidden Markov models (HMMs) and Markov decision processes (MDPs). Chapter 2;

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Semi-Markov Processes

behind constructing Semi-Markov (SM) models and SM decision models in the field of reliability and maintenance Provides numerous;

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Mixture and Hidden Markov Models with R

This book discusses mixture and hidden Markov models for modeling behavioral data. Mixture and hidden Markov models are statistical models;

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Quantitative Evaluation of Systems

submissions. They are organized in topical sections entitled: Markov processes; tools; sampling, inference, and optimization methods; Markov decision;

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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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Introduction to Discrete Event Systems

and decentralized supervisory control of partially-observed systems timed models, including timed automata and hybrid automata stochastic;

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Handbook of Markov Decision Processes

Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter;

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Handbook of Markov Decision Processes

Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter;

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Incomparable Values

. This book argues, to the contrary, that values are often partially ordered and hence frequently incomparable. Proceeding logically from a;

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Elements Of Stochastic Modelling

of probability theory and then covers the following topics: Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory;

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Continuous-Time Markov Decision Processes

This book offers a systematic and rigorous treatment of continuous-time Markov decision processes, covering both theory and possible;

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Continuous-Time Markov Decision Processes

This book offers a systematic and rigorous treatment of continuous-time Markov decision processes, covering both theory and possible;

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