Annealed Important Sampling for Models with Latent Variables

Understand

This paper is concerned with Bayesian inference when the likelihood is analytically intractable but can be unbiasedly estimated.

• We propose an annealed importance sampling procedure for estimating expectations with respect to the posterior.
• The proposed algorithm is useful in cases where finding a good proposal density is challenging, and when estimates of the marginal likelihood are required.
• The effect of likelihood estimation is investigated, and the results provide guidelines on how to set up the precision of the likelihood estimation in order to optimally implement the procedure.