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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 […]

ABC in Sydney [guest post]

July 18, 2014

[Scott Sisson sent me this summary of the ABC in Sydney meeting that took place two weeks ago.] Following on from ABC in Paris (2009), ABC in London (2011) and ABC in Rome (2013), the fourth instalment of the international workshops in Approximate Bayesian Computation (ABC) was held at UNSW in Sydney on 3rd-4th July […]

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