This lecture will provide a broad 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 f
or the procedure. The main challenge to using the approach is the
computational difficulties, and the lecture will survey algorithms
that ease this burden, including consideration of sequential Monte Carlo approaches, and the importance of careful choice of summary statistics.
The seminar will be done remote only, using ZOOM for this event, link to join the seminar:
M. Girone, M. Elsing, L. Moneta, M. Pierini
Event co-organised with the PHYSTAT Committee