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insufficient statistics for ABC model choice

October 17, 2014

[Here is a revised version of my comments on the paper by Julien Stoehr, Pierre Pudlo, and Lionel Cucala, now to appear [both paper and comments] in Statistics and Computing special MCMSki 4 issue.] Approximate Bayesian computation techniques are 2000’s successors of MCMC methods as handling new models where MCMC algorithms are at a loss, […]

ABC model choice via random forests [expanded]

October 1, 2014

Today, we arXived a second version of our paper on ABC model choice with random forests. Or maybe [A]BC model choice with random forests. Since the random forest is built on a simulation from the prior predictive and no further approximation is used in the process. Except for the computation of the posterior [predictive] error […]

ABC@NIPS: call for papers

September 9, 2014

In connection with the previous announcement of ABC in Montréal, a call for papers that came out today: NIPS 2014 Workshop: ABC in Montreal December 12, 2014 Montréal, Québec, Canada Approximate Bayesian computation (ABC) or likelihood-free (LF) methods have developed mostly beyond the radar of the machine learning community, but are important tools for a […]

ABC model choice by random forests [guest post]

August 11, 2014

[Dennis Prangle sent me his comments on our ABC model choice by random forests paper. Here they are! And I appreciate very much contributors commenting on my paper or others, so please feel free to join.] This paper proposes a new approach to likelihood-free model choice based on random forest classifiers. These are fit to […]

ABC in Sydney [guest post #2]

July 24, 2014

[Here is a second guest post on the ABC in Sydney workshop, written by Chris Drovandi] First up Dennis Prangle presented his recent work on “Lazy ABC”, which can speed up ABC by potentially abandoning model simulations early that do not look promising. Dennis introduces a continuation probability to ensure that the target distribution of […]

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