This website keeps track of developments in approximate Bayesian computation (ABC) (a.k.a. likelihood-free), a class of computational statistical methods for Bayesian inference under intractable likelihoods. The site is meant to be a resource both for biologists and statisticians who want to learn more about ABC and related methods. Recent publications are under Publications 2012. A comprehensive list of publications can be found under Literature. If you are unfamiliar with ABC methods see the Introduction. Navigate using the menu to learn more.

Contact :

This website is maintained by Erkan Buzbas. For further information, contributions or corrections please e-mail erkanb(at)uidaho.edu



NIPS 2015 Workshop: ABC in Montreal.
December 11, 2015, Montreal, Quebec, Canada
(See Below NIPS 2014, format and requirements are mostly unchanged, visit the website for details.)

NIPS 2014 Workshop: ABC in Montreal.
December 12 or 13, 2014, Montreal, Quebec, 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 large segment of the scientific community. This is particularly true for systems and population biology, computational psychology, computational chemistry, etc. Recent work has both applied machine learning models and algorithms to general ABC inference (NN, forests, GPs) and ABC inference to machine learning (e.g. using computer graphics to solve computer vision using ABC). In general, however, there is significant room for collaboration between the two communities.

The workshop will consist of invited and contributed talks, poster spotlights, and a poster session. Rather than a panel discussion we will encourage open discussion between the speakers and the audience!

Examples of topics of interest in the workshop include (but are not limited to):
Applications of ABC to machine learning, e.g., computer vision, inverse problems
ABC in Systems Biology, Computational Science, etc
ABC Reinforcement Learning
Machine learning simulator models, e.g., NN models of simulation responses, GPs etc.
Selection of sufficient statistics
Online and post-hoc error
ABC with very expensive simulations and acceleration methods (surrogate modeling, choice of design/simulation points)
ABC with probabilistic programming
Posterior evaluation of scientific problems/interaction with scientists
Post-computational error assessment
Impact on resulting ABC inference
ABC for model selection

We invite submissions in NIPS 2014 format with a maximum of 4 pages, excluding references. Anonymity is not required. Relevant works that have been recently published or presented elsewhere are allowed, provided that previous publications are explicitly acknowledged. Please submit papers in PDF format abcinmontreal@gmail.com .

This workshop has been endorsed by ISBA. As part of their sponsorship, ISBA will be awarding a limited number of travel awards to PhD students and young researchers. The organizing committee may nominate particularly strong submissions for this award.

In addition to the general ISBA endorsement, ABC in Montreal has been endorsed by the BayesComp section of ISBA.

Important Dates:
Submission Deadline: October 9, 2014
Author Notification: October 26, 2014
Workshop: December 12 or 13, 2014

Invited Speakers:
Michael Blum, Laboratoire TIMC-IMAG
Juliane Liepe, Imperial College London
Vikash Mansinghka, MIT
Frank Wood, Oxford

Neil Lawrence, University of Sheffield
Ted Meeds, University of Amsterdam
Christian Robert, Université Paris-Dauphine
Max Welling, University of Amsterdam
Richard Wilkinson, University of Nottingham

The organizers can be contacted at abcinmontreal@gmail.com.


ABC in Sydney.
3-4 July 2014. Fourth ABC meeting in the series after Paris, London, and Rome.


Jan 6-8, 2014.
There is an Invited Session on ABC.


ABC in Rome.
May 30-31, 2013.
Third meeting on ABC.


Mathematical and Computational Evolutionary Biology (MCEB), Montpellier, France.
June 18-22, 2012.
Some talks on ABC and related developments.


ISBA 2012 Conference, Kyoto, Japan.
June 25-29, 2012.
Two special topic sessions on ABC and likelihood-free methods:

Title:Approximate Bayesian Computation (ABC) : Likelihood-free Bayesian inference I
Organizer: Christian Robert (Université Paris Dauphine, France) & Scott Sisson (University of New South Wales, Australia)
Speakers: Michael Stumpf (University College London, UK)
Dennis Prangle (Lancaster University, UK)
Chris Drovandi (Queensland University of Technology, Australia)
Judith Rousseau (Université Paris Dauphine, France)
Title:Approximate Bayesian Computation (ABC) : Likelihood-free Bayesian inference II
Organizer: Christian Robert (Université Paris Dauphine, France) & Scott Sisson (University of New South Wales, Australia)
Speakers: David Nott (National University of Singapore, Singapore)
Michael Blum (CNRS Université Joseph Fourier Grenoble, France)
Jean-Michel Marin (Université Montpellier 2, France)
Scott Sisson (University of New South Wales, Australia)

Confronting Intractability in Statistical Inference
16-19 April 2012, University of Bristol, UK

In the last few years two one day meetings were held on advances in ABC and Likelihood-free methods:


May 5, 2011. ABC in London, Imperial College. Information about the meeting is here and slides of the talks are uploaded to nature preceedings, here.


June 26, 2009. ABC in Paris, Université Paris Dauphine. Information about the meeting and slides of most of the talks can be found here.