Another week starts today. The Information Age is pleased to again share a post from the blog Xi’an’s Og, a blog by a professional statistician. One of today’s posts from that blog featured another conference (event) presentations within the field of statistical inference and one its techniques called ABC (approximate Bayesian Computation). The content went like this:
nd another exciting and animated [last] day of ABC’ory [and practice]! Kyle Cranmer exposed adensity ratio density estimation approach I had not seen before [and will comment here soon]. Patrick Muchmore talked about unbiased estimators of Gaussian and non-Gaussian densities in elliptically contoured distributions which allows for running pseudo-MCMC than ABC. This reminded me of using the same tool [for those distributions can be expressed as mixtures of normals] in my PhD thesis, if for completely different purposes. In his talk, including a presentation of an ABC blackbox platform called ELFI, Samuel Kaski did translate statistical inference as inverse reinforcement learning: I hope this does not catch! In the afternoon, Dennis Prangle gave us the intuition behind his rare event ABC, which is not estimating rare events by ABC but rather using rare event simulation to improve ABC. [A paper I will a.s. comment here soon as well!] And Scott Sisson concluded the day and the week with his views on ABC for high dimensions.
While being obviously biased as the organiser of the workshop, I nonetheless feel it was a wonderful meeting with just the right number of participants to induce interactions and discussions during and around the talk, as well as preserve some time for pairwise interactions. Like all other workshops I contributed to in BIRS along the years.
The former first paragraph caught my attention for the relationship of ABC with inverse reinforcement learning (rof robotics relevance). Some paths of further research that might be worthwhile to check in the future. Today that is it. The week started with some technical difficulties, unfortunately.