Sequential Monte Carlo without likelihoods

• DOI: 10.1073/pnas.0607208104
• Publication Date: 2007-02-02T22:47:54Z
• Authors: S. A. Sisson; Y. Fan; Mark M. Tanaka
• Keywords: Rejection sampling

Recent new methods in Bayesian simulation have provided ways of evaluating posterior distributions the presence analytically or computationally intractable likelihood functions. Despite representing a substantial methodological advance, existing based on rejection sampling Markov chain Monte Carlo can be highly inefficient and accordingly require far more iterations than may practical to implement. Here we propose sequential sampler that convincingly overcomes these inefficiencies. We demonstrate its implementation through an epidemiological study transmission rate tuberculosis.