Fearnhead, Paul (2008) Computational Methods for Complex Stochastic Systems: A Review of Some Alternatives to MCMC. Statistics and Computing, 18 (2). pp. 151-171. ISSN 0960-3174
This is the latest version of this item.
|PDF (Adams_review_final.pdf) |
Download (427Kb) | Preview
We consider analysis of complex stochastic models based upon partial information. MCMC and reversible jump MCMC are often the methods of choice for such problems, but in some situations they can be difficult to implement; and suffer from problems such as poor mixing, and the difficulty of diagnosing convergence. Here we review three alternatives to MCMC methods: importance sampling, the forward-backward algorithm, and sequential Monte Carlo (SMC). We discuss how to design good proposal densities for importance sampling, show some of the range of models for which the forward-backward algorithm can be applied, and show how resampling ideas from SMC can be used to improve the efficiency of the other two methods. We demonstrate these methods on a range of examples, including estimating the transition density of a diffusion and of a discrete-state continuous-time Markov chain; inferring structure in population genetics; and segmenting genetic divergence data.
|Journal or Publication Title:||Statistics and Computing|
|Uncontrolled Keywords:||Diffusions ; Forward-Backward Algorithm ; Importance Sampling ; Missing Data ; Particle Filter ; Population Genetics|
|Subjects:||Q Science > QA Mathematics|
|Departments:||Faculty of Science and Technology > Mathematics and Statistics|
|Deposited By:||Prof Paul Fearnhead|
|Deposited On:||18 Apr 2008 10:00|
|Last Modified:||20 Jan 2016 01:11|
Available Versions of this Item
- Computational Methods for Complex Stochastic Systems: A Review of Some Alternatives to MCMC. (deposited 06 Dec 2007)
- Computational Methods for Complex Stochastic Systems: A Review of Some Alternatives to MCMC. (deposited 18 Apr 2008 10:00)[Currently Displayed]
Actions (login required)