| Dewey Class |
518 |
| Title |
Monte Carlo Methods ([EBook]) / by Adrian Barbu, Song-Chun Zhu. |
| Author |
Barbu, Adrian |
| Added Personal Name |
Zhu, Song-Chun |
| Other name(s) |
SpringerLink (Online service) |
| Edition statement |
1st ed. 2020. |
| Publication |
Singapore : : Springer Singapore : : Imprint: Springer, , 2020. |
| Physical Details |
XVI, 422 p. 250 illus., 185 illus. in color. : online resource. |
| ISBN |
9789811329715 |
| Summary Note |
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.: |
| Contents note |
1 Introduction to Monte Carlo Methods -- 2 Sequential Monte Carlo -- 3 Markov Chain Monte Carlo - the Basics -- 4 Metropolis Methods and Variants -- 5 Gibbs Sampler and its Variants -- 6 Cluster Sampling Methods -- 7 Convergence Analysis of MCMC -- 8 Data Driven Markov Chain Monte Carlo -- 9 Hamiltonian and Langevin Monte Carlo -- 10 Learning with Stochastic Gradient -- 11 Mapping the Energy Landscape. |
| Mode of acces to digital resource |
Digital book. Cham Springer Nature 2020. - Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format |
| System details note |
- Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). |
| Internet Site |
https://doi.org/10.1007/978-981-13-2971-5 |
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