Name
Abstract | ||
|---|---|---|
This paper presents an overview of Markov Chain Monte Carlo (MCMC) methods for statistical inference and applications. The article begins by describing ordinary Monte Carlo methods, which in principle has the same goals as the MCMC but can hardly be implemented in practice. Following that basic Markov Chain Monte Carlo is discussed, which is founded on the Hastings algorithm and includes Metropolis method and the Gibbs sampler as special cases. Finally, various special applications of Markov Chain Monte Carlo methods are briefly mentioned and some recent development of MCMC are covered in final remarks section. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/978-3-7908-1782-9_49 | HYBRID INFORMATION SYSTEMS |
Keywords | Field | DocType |
markov chain monte carlo,statistical inference | Monte Carlo method in statistical physics,Markov chain mixing time,Monte Carlo method,Markov chain Monte Carlo,Computer science,Particle filter,Hybrid Monte Carlo,Algorithm,Parallel tempering,Monte Carlo molecular modeling | Conference |
ISSN | Citations | PageRank |
1615-3871 | 0 | 0.34 |
References | Authors | |
2 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Bo | 1 | 0 | 0.34 |
| Chin Teck Chai | 2 | 5 | 1.77 |