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ABC in Montreal
Ted Meeds · Michael Gutmann · Dennis Prangle · Jean-Michel Marin · Richard Everitt

Fri Dec 11 05:30 AM -- 03:30 PM (PST) @ 511 a
Event URL: https://sites.google.com/site/abcinmontreal/ »

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 and diverse segment of the scientific community. This is particularly true for systems and population biology, computational neuroscience, computer vision, healthcare sciences, but also many others.

Interaction between the ABC and machine learning community has recently started and contributed to important advances. In general, however, there is still significant room for more intense interaction and collaboration. Our workshop aims at being a place for this to happen.

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.

Author Information

Ted Meeds (University of Amsterdam)
Michael Gutmann (University of Helsinki)
Dennis Prangle (Reading University)
Jean-Michel Marin (University of Montpellier 2)
Richard Everitt (Reading University)

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