ABC in Sydney
I’ve been invited to give two seminars this week at UNSW. Full details available here:
1:30pm, Thursday July 3: Intro to approximate Bayesian computation (ABC)
4:10pm, Friday July 4: Pre-processing for ABC-SMC with undirected graphical models
ABC is a useful method for Bayesian inference with intractable likelihoods, but existing algorithms require a large number of simulations of pseudo-data from the generative model. The computational cost of these simulations can be prohibitive for high-dimensional data. In this talk I will show that the scalability of ABC-SMC can be improved by performing a pre-computation step before model fitting. The output of this pre-computation can be reused across multiple datasets. I illustrate the approach using undirected graphical models, including the hidden Potts model for image analysis of large datasets.