Monte Carlo Methods (Hardcover, 1st ed. 2020)

Adrian Barbu, Song-Chun Zhu

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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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Release date:	February 2020
First published:	2020
Authors:	Adrian Barbu • Song-Chun Zhu
Dimensions:	240 x 168mm (L x W)
Format:	Hardcover
Pages:	422
Edition:	1st ed. 2020
ISBN-13:	978-981-13-2970-8
Categories:	Books > Science & Mathematics > Mathematics > Numerical analysis Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > General theory of computing > Mathematical theory of computation Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision Promotions
LSN:	981-13-2970-2
Barcode:	9789811329708