Speaker
Chad Shafer
(CMU)
Description
This lecture will provide an overview of approximate Bayesian computation
(ABC), starting with a focus on the motivation for the procedure, including when it
makes sense to utilize it. The resulting approximation can be thought of as
calculating the posterior under a contaminated data set; this interpretation
provides a useful context for the procedure. The main challenge to using the
approach is the computational difficulties, so the lecture will cover
algorithms that ease this burden, including consideration of sequential Monte Carlo
approaches, and the importance of careful choice of summary statistics.
