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Simulation-Based Optimization

special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite;

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Simulation-Based Algorithms for Markov Decision Processes

Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics;

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Dynamic and Stochastic Multi-Project Planning

. To obtain optimal and near optimal scheduling policies new models and algorithms are proposed based on the theory of Markov decision;

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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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Approximate Iterative Algorithms

in functional analysis and probability theory. Extensive applications to Markov decision processes are presented. This volume is intended for;

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Approximate Iterative Algorithms

in functional analysis and probability theory. Extensive applications to Markov decision processes are presented. This volume is intended for;

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Perfect Simulation

, and other areas into a unified framework. You will discover the mechanisms behind creating perfect simulation algorithms for solving an array;

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Markov Chains

in marketing management. The authors present an approach based on Markov decision processes for the calculation of CLV using real data. Chapter 6;

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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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Mean Field Simulation for Monte Carlo Integration

. Mean Field Simulation for Monte Carlo Integration presents the first comprehensive and modern mathematical treatment of mean field particle;

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Mean Field Simulation for Monte Carlo Integration

. Mean Field Simulation for Monte Carlo Integration presents the first comprehensive and modern mathematical treatment of mean field particle;

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Inference in Hidden Markov Models

This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics;

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American-Type Options

The book gives a systematical presentation of stochastic approximation methods for discrete time Markov price processes. Advanced methods;

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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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Planning with markov Decision Processes

Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with;

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Monte Carlo Simulation for the Pharmaceutical Industry

Markov decision process, simulations in classical and adaptive trials, and various challenges in clinical trial management and execution. The;

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Monte Carlo Simulation for the Pharmaceutical Industry

Markov decision process, simulations in classical and adaptive trials, and various challenges in clinical trial management and execution. The;

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Planning Algorithms

theory, Markov decision processes, and information spaces, which are the 'configuration spaces' of all sensor-based planning problems. The last;

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Dependability of Networked Computer based Systems

variety of dependability modeling methods: stochastic processes, Markov and semi-Markov models, response-time distribution, stochastic;

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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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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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Decision Making in Service Industries

efficiently performed for better design of service systems by using probabilistic algorithms as well as hybrid and simulation-based approaches;

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Decision Making in Service Industries

efficiently performed for better design of service systems by using probabilistic algorithms as well as hybrid and simulation-based approaches;

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American-Type Options

functions for discrete time Markov price processes. Advanced methods combining backward recurrence algorithms for computing of option rewards and;

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Hands-On Simulation Modeling with Python

bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop;

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