| 书目名称 | Monte Carlo Methods | | 编辑 | Adrian Barbu,Song-Chun Zhu | | 视频video | | | 概述 | A Monte Carlo textbook suitable for students and researchers in the areas of computer vision, machine learning, robotics, artificial intelligence, graphics, etc.An easy to understand textbook featurin | | 图书封面 |  | | 描述 | .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.. | | 出版日期 | Textbook 2020 | | 关键词 | Monte Carlo Methods; Sequential Monte Carlo; Markov Chain Monte Carlo; Metropolis-Hastings; Gibbs Sample | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-13-2971-5 | | isbn_ebook | 978-981-13-2971-5 | | copyright | Springer Nature Singapore Pte Ltd. 2020 |
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发表于 2025-3-21 18:55:23
