Importance Nested Sampling and the MultiNest Algorithm
University of Cambridge · Health Data Research UK · +1 more institution
Abstract
Bayesian inference involves two main computational challenges. First, in estimating the parameters of some model for the data, the posterior distribution may well be highly multi-modal: a regime in which the convergence to stationarity of traditional Markov Chain Monte Carlo (MCMC) techniques becomes incredibly slow. Second, in selecting between a set of competing models the necessary estimation of the Bayesian evidence for each is, by definition, a (possibly high-dimensional) integration over the entire parameter space; again this can be a daunting computational task, although new Monte Carlo (MC) integration algorithms offer solutions of ever increasing efficiency. Nested sampling (NS) is one such…
Citation impact
- FWCI
- 102.54
- Percentile
- 100%
- References
- 84
Authors
4Topics & keywords
- Markov chain Monte Carlo
- Computer science
- Algorithm
- Bayesian inference
- Bayesian probability
- Sampling (signal processing)
- Gibbs sampling
- Monte Carlo method