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Monte Carlo methods /

Zhu, Song Chun.

Monte Carlo methods /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Monte Carlo methods // Adrian Barbu, Song-Chun Zhu.
作者:	Barbu, Adrian G.,
其他作者:	Zhu, Song Chun.
出版者:	Singapore :Springer, : c2020.,
面頁冊數:	xvi, 422 p. :ill. (some col.), ports. (chiefly col.) ; : 25 cm.;
標題:	Monte Carlo method. -
ISBN:	9811329702

Monte Carlo methods /
Barbu, Adrian G.,1971-

Monte Carlo methods /Adrian Barbu, Song-Chun Zhu. - Singapore :Springer,c2020. - xvi, 422 p. :ill. (some col.), ports. (chiefly col.) ;25 cm.

Includes bibliographical references and index.

This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.--

ISBN: 9811329702Subjects--Topical Terms:

672188
Monte Carlo method.

LC Class. No.: QA298 / .B37 2020

Dewey Class. No.: 519.282

Monte Carlo methods /
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504 $a Includes bibliographical references and index.
520 # $a This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.-- $c Provided by publisher.
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