A Brief Tour of Bayesian Sampling Methods
Unlike in the past, the modern Bayesian analyst has many options for approximating intractable posterior distributions. This chapter briefly summarizes the class of posterior sampling methods known as Markov chain Monte Carlo, a type of dependent sampling strategy. Varieties of algorithms exist for...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book Chapter |
| Language: | English |
| Published: |
IntechOpen
2020
|
| Subjects: | |
| Online Access: | https://www.intechopen.com/chapters/71603 |
| Tags: |
No Tags, Be the first to tag this record!
|
| Summary: | Unlike in the past, the modern Bayesian analyst has many options for approximating intractable posterior distributions. This chapter briefly summarizes the class of posterior sampling methods known as Markov chain Monte Carlo, a type of dependent sampling strategy. Varieties of algorithms exist for constructing chains, and we review some of them here. Such methods are quite flexible and are now used routinely, even for relatively complicated statistical models. In addition, extensions of the algorithms have been developed for various goals. General-purpose software is currently also available to automate the construction of samplers, freeing the analyst to focus on model formulation and inference. |
|---|---|
| ISBN: | 978-1-83880-385-8 |
| DOI: | 10.5772/intechopen.91451 |