Towards Automatic Model Comparison: An Adaptive Sequential Monte Carlo Approach
By Yan Zhou, Adam M. Johansen, John A. D. Aston. 2013
61 citations recorded by the source corpus.
Model comparison for the purposes of selection, averaging and validation is a problem found throughout statistics. Within the Bayesian paradigm, these problems all require the calculation of the posterior probabilities of models within a particular class. Substantial progress has been made in recent years, but difficulties remain in the implementation of existing schemes. This paper presents adaptive sequential Monte Carlo (\smc) sampling strategies to characterise the posterior distribution of a collection of models, as well as the parameters of those models. Both a simple product estimator and a combination of \smc and a path sampling estimator are considered and existing theoretical results are extended to include the path sampling variant. A novel approach to the automatic specification of distributions within \smc algorithms is presented and shown to outperform the state of the art in this area. The performance of the proposed strategies is demonstrated via an extensive empirical study. Comparisons with state of the art algorithms show that the proposed algorithms are always competitive, and often substantially superior to alternative techniques, at equal computational cost and considerably less application-specific implementation effort.
Subfields: Artificial Intelligence, Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Chris J. Oates
- Mark A. Girolami
- Theodore Papamarkou
- Paris Perdikaris
- Zoubin Ghahramani
- Michael Betancourt
- Jon Cockayne
- Andrew Gelman
In Statistics and Probability
- Chris J. Oates
- Mark A. Girolami
- Theodore Papamarkou
- Qiang Liu
- Michael Betancourt
- Lester W. Mackey
- Jackson Gorham
- Jon Cockayne
Citations in the corpus
- The Controlled Thermodynamic Integral for Bayesian Model Comparison (2014, background): “, 2000), sequential Monte Carlo (Zhou et al., 2013), annealed importance sampling (Neal, 2001), reversible-jump Markov chain Monte Carlo (MCMC; Green, 1995) and also again approximate Bayesian computation (Toni et al.”