Monte Carlo methods / Adrian Barbu, Song-Chun Zhu.

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Monte Carlo methods / Adrian Barbu, Song-Chun Zhu.

9789811329715
9811329710
9789811329708
9811329702
9789811329722
9811329729

Singapore : Springer, [2020]

English

1 online resource (xvi, 422 pages) : illustrations.

10.1007/978-981-13-2971-5 doi

QA298

519.282

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.-- Provided by publisher.

Includes bibliographical references and index.

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Online resource; title from PDF title page (SpringerLink, viewed March 26, 2020).

Print version: 9789811329708

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