Описание: This book introduces a new Markov chain optimization method with braking the detailed balance. It develops a quantum Monte Carlo method for nonconserved particles and combines it with the excitation level analysis.
Описание: "This book is concerned with a probabilistic approach for image analysis, mostly from the Bayesian point of view, and the important Markov chain Monte Carlo methods commonly used....This book will be useful, especially to researchers with a strong background in probability and an interest in image analysis.
Автор: Brooks, Steve Название: Handbook of Markov Chain Monte Carlo ISBN: 1420079417 ISBN-13(EAN): 9781420079418 Издательство: Taylor&Francis Рейтинг: Цена: 19906.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Gamerman, Dani. Название: Markov Chain Monte Carlo ISBN: 1584885874 ISBN-13(EAN): 9781584885870 Издательство: Taylor&Francis Рейтинг: Цена: 15312.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Incorporating changes in theory and highlighting various applications, this book presents a comprehensive introduction to the methods of Markov Chain Monte Carlo (MCMC) simulation technique. It incorporates the developments in MCMC, including reversible jump, slice sampling, bridge sampling, path sampling, multiple-try, and delayed rejection.
Автор: Graham Название: Markov Chains - Analytic and Monte Carlo Computations ISBN: 1118517075 ISBN-13(EAN): 9781118517079 Издательство: Wiley Рейтинг: Цена: 13456.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Markov Chains: Analytic and Monte Carlo Computations introduces the main notions related to Markov chains and provides explanations on how to characterize, simulate, and recognize them.
Описание: Primarily an introduction to the theory of stochastic processes at the undergraduate or beginning graduate level, the primary objective of this book is to initiate students in the art of stochastic modelling. Researchers and students in these areas as well as in physics, biology and the social sciences will find this book of interest.
Описание: Primarily an introduction to the theory of stochastic processes at the undergraduate or beginning graduate level, the primary objective of this book is to initiate students in the art of stochastic modelling. Researchers and students in these areas as well as in physics, biology and the social sciences will find this book of interest.
Автор: J. Keilson Название: Markov Chain Models — Rarity and Exponentiality ISBN: 0387904050 ISBN-13(EAN): 9780387904054 Издательство: Springer Рейтинг: Цена: 13275.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: in failure time distributions for systems modeled by finite chains. This introductory chapter attempts to provide an over- view of the material and ideas covered. The presentation is loose and fragmentary, and should be read lightly initially. Subsequent perusal from time to time may help tie the mat- erial together and provide a unity less readily obtainable otherwise. The detailed presentation begins in Chapter 1, and some readers may prefer to begin there directly. O.l. Time-Reversibility and Spectral Representation. Continuous time chains may be discussed in terms of discrete time chains by a uniformizing procedure ( 2.l) that simplifies and unifies the theory and enables results for discrete and continuous time to be discussed simultaneously. Thus if N(t) is any finite Markov chain in continuous time governed by transition rates vmn one may write for pet) = Pmn(t)] - P N(t) = n I N(O) = m] pet) = exp -vt(I - a )] (0.1.1) v where v > Max r v ' and mn m n law 1 - v-I * Hence N(t) where is governed r vmn Nk = NK(t) n K(t) is a Poisson process of rate v indep- by a ' and v dent of N - k Time-reversibility ( 1.3, 2.4, 2.S) is important for many reasons. A) The only broad class of tractable chains suitable for stochastic models is the time-reversible class.
Автор: Banisch Sven Название: Markov Chain Aggregation for Agent-Based Models ISBN: 3319796917 ISBN-13(EAN): 9783319796918 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This self-contained text develops a Markov chain approach that makes the rigorous analysis of a class of microscopic models that specify the dynamics of complex systems at the individual level possible.
Автор: Rubinstein Reuven Y. Название: Simulation and the Monte Carlo Method ISBN: 1118632168 ISBN-13(EAN): 9781118632161 Издательство: Wiley Рейтинг: Цена: 17416.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Simulation and the Monte Carlo Method, Third Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simulation since the publication of the classic First Edition over more than a quarter of a century ago.
Название: Stochastic Simulation and Monte Carlo Methods ISBN: 3642393624 ISBN-13(EAN): 9783642393624 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book combines advanced mathematical tools, theoretical analysis of stochastic numerical methods, and practical issues at a high level, so as to provide optimal results on the accuracy of Monte Carlo simulations of stochastic processes.
Автор: Del Moral Название: Mean Field Simulation for Monte Carlo Integration ISBN: 1138198730 ISBN-13(EAN): 9781138198739 Издательство: Taylor&Francis Рейтинг: Цена: 7961.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
In the last three decades, there has been a dramatic increase in the use of interacting particle methods as a powerful tool in real-world applications of Monte Carlo simulation in computational physics, population biology, computer sciences, and statistical machine learning. Ideally suited to parallel and distributed computation, these advanced particle algorithms include nonlinear interacting jump diffusions; quantum, diffusion, and resampled Monte Carlo methods; Feynman-Kac particle models; genetic and evolutionary algorithms; sequential Monte Carlo methods; adaptive and interacting Markov chain Monte Carlo models; bootstrapping methods; ensemble Kalman filters; and interacting particle filters.
Mean Field Simulation for Monte Carlo Integration presents the first comprehensive and modern mathematical treatment of mean field particle simulation models and interdisciplinary research topics, including interacting jumps and McKean-Vlasov processes, sequential Monte Carlo methodologies, genetic particle algorithms, genealogical tree-based algorithms, and quantum and diffusion Monte Carlo methods.
Along with covering refined convergence analysis on nonlinear Markov chain models, the author discusses applications related to parameter estimation in hidden Markov chain models, stochastic optimization, nonlinear filtering and multiple target tracking, stochastic optimization, calibration and uncertainty propagations in numerical codes, rare event simulation, financial mathematics, and free energy and quasi-invariant measures arising in computational physics and population biology.
This book shows how mean field particle simulation has revolutionized the field of Monte Carlo integration and stochastic algorithms. It will help theoretical probability researchers, applied statisticians, biologists, statistical physicists, and computer scientists work better across their own disciplinary boundaries.
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