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Monte Carlo methods

Barbu, Adrian.

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Monte Carlo methods

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Monte Carlo methods/ by Adrian Barbu, Song-Chun Zhu.
作者:	Barbu, Adrian.
其他作者:	Zhu, Song-Chun.
出版者:	Singapore :Springer Singapore : : 2020.,
面頁冊數:	xvi, 422 p. :ill., digital ;24 cm.
內容註:	1 Introduction to Monte Carlo Methods -- 2 Sequential Monte Carlo -- 3 Markov Chain Monte Carlo - the Basics -- 4 Metropolis Methods and Variants -- 5 Gibbs Sampler and its Variants -- 6 Cluster Sampling Methods -- 7 Convergence Analysis of MCMC -- 8 Data Driven Markov Chain Monte Carlo -- 9 Hamiltonian and Langevin Monte Carlo -- 10 Learning with Stochastic Gradient -- 11 Mapping the Energy Landscape.
Contained By:	Springer eBooks
標題:	Monte Carlo method. -
電子資源:	https://doi.org/10.1007/978-981-13-2971-5
ISBN:	9789811329715

Monte Carlo methods
Barbu, Adrian.

Monte Carlo methods[electronic resource] /by Adrian Barbu, Song-Chun Zhu. - Singapore :Springer Singapore :2020. - xvi, 422 p. :ill., digital ;24 cm.

1 Introduction to Monte Carlo Methods -- 2 Sequential Monte Carlo -- 3 Markov Chain Monte Carlo - the Basics -- 4 Metropolis Methods and Variants -- 5 Gibbs Sampler and its Variants -- 6 Cluster Sampling Methods -- 7 Convergence Analysis of MCMC -- 8 Data Driven Markov Chain Monte Carlo -- 9 Hamiltonian and Langevin Monte Carlo -- 10 Learning with Stochastic Gradient -- 11 Mapping the Energy Landscape.

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: 9789811329715

Standard No.: 10.1007/978-981-13-2971-5doiSubjects--Topical Terms:

551308
Monte Carlo method.

LC Class. No.: QA298 / .B373 2020

Dewey Class. No.: 519.282

Monte Carlo methods
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505 0 $a 1 Introduction to Monte Carlo Methods -- 2 Sequential Monte Carlo -- 3 Markov Chain Monte Carlo - the Basics -- 4 Metropolis Methods and Variants -- 5 Gibbs Sampler and its Variants -- 6 Cluster Sampling Methods -- 7 Convergence Analysis of MCMC -- 8 Data Driven Markov Chain Monte Carlo -- 9 Hamiltonian and Langevin Monte Carlo -- 10 Learning with Stochastic Gradient -- 11 Mapping the Energy Landscape.
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.
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