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Exact Sampling for Bayesian Inference: Unbounded State Spaces
| Content Provider | Semantic Scholar |
|---|---|
| Author | Murdoch, Duncan J. |
| Copyright Year | 2000 |
| Abstract | Propp and Wilson 10, 11] described a protocol called coupling from the past (CFTP) for exact sampling from the steady-state distribution of a Markov chain Monte Carlo (MCMC) process. In it a past time is identiied from which the paths of coupled Markov chains starting at every possible state would have coalesced into a single value by the present time; this value is then a sample from the steady-state distribution. Foss and Tweedie 3] pointed out that for CFTP to work, the underlying Markov chain must be uniformly ergodic. Unfortunately, most of the chains in common use in Bayesian inference are not, when the state space is unbounded. However, this does not mean that CFTP can't be used; in this paper we present three modiications. The rst is a simple change to the chain to induce uniform ergodic-ity. The second (more extensively discussed in 4]) is a modiication to CFTP due to Kendall 6] that makes use of random bounds on particular realizations of the chain. Finally, the last method attempts to make use of Meng's 7] multistage coupler to address the problem. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://fisher.stats.uwo.ca/faculty/murdoch/research/papers/unbound.ps |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Acoustic coupler Bayesian network Coupler Device Component Coupling from the past Ergodic theory Ergodicity Inference Markov chain Monte Carlo Monte Carlo method Multistage amplifier Sampling (signal processing) Sampling - Surgical action Spaces State space Steady state |
| Content Type | Text |
| Resource Type | Article |
