Abstract
Markov chain Monte Carlo (MCMC) methods asymptotically sample from complex probability distributions. The pseudo-marginal MCMC framework only requires an unbiased estimator of the unnormalized probability distribution function to construct a Markov chain. However, the resulting chains are harder to tune to a target distribution than conventional MCMC, and the types of updates available are limited. We describe a general way to clamp and update the random numbers used in a pseudo-marginal method’s unbiased estimator. In this framework we can use slice sampling and other adaptive methods.We obtain more robust Markov chains,which often mix more quickly.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 19th International Conference on Artificial Intelligence and Statistics 2016 |
| Place of Publication | Cadiz, Spain |
| Publisher | PMLR |
| Pages | 911-919 |
| Number of pages | 9 |
| Volume | 51 |
| Publication status | Published - May 2016 |
| Event | 19th International Conference on Artificial Intelligence and Statistics - Cadiz, Spain Duration: 9 May 2016 → 11 May 2016 https://www.aistats.org/aistats2016/ |
Conference
| Conference | 19th International Conference on Artificial Intelligence and Statistics |
|---|---|
| Abbreviated title | AI and Statistics 2016 |
| Country/Territory | Spain |
| City | Cadiz |
| Period | 9/05/16 → 11/05/16 |
| Internet address |
Fingerprint
Dive into the research topics of 'Pseudo-Marginal Slice Sampling'. Together they form a unique fingerprint.Profiles
-
Iain Murray
- School of Informatics - Personal Chair of Machine Learning and Inference
- Institute for Adaptive and Neural Computation
- Data Science and Artificial Intelligence
Person: Academic: Research Active