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