نقد و بررسی اجمالیخرید کتاب Monte Carlo Methods
by Adrian Barbu(Author), Song-Chun Zhu(Author)
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.
About the Author
Adrian Barbu received his PhD in Mathematics from Ohio State University in 2000 and his PhD in Computer Science from the University of California, Los Angeles in 2005. His research interests are in machine learning, computer vision and medical imaging. He received the 2011 Thomas A. Edison Patent Award with his co-authors from Siemens for their work on Marginal Space Learning. In 2007 he joined the Statistics Department at Florida State University, first as an assistant professor, and since 2013 as an associate professor.
Product details
- Publisher : Springer; 1st ed. 2020 edition (February 25, 2020)
- Language: : English
- Hardcover : 438 pages
- ISBN-10 : 9811329702
- ISBN-13 : 978-۹۸۱۱۳۲۹۷۰۸
- Item Weight : 2.۲۸ pounds
- Dimensions : 6.۷۸ x 1.08 x 9.57 inches
- # Counting & Numeration
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