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.
8:30 8:40 Opening remarks, winner ISBA travel award
8:40 9:20 Mark Beaumont, ABC and Population Genetics: any lessons for big data?
9:20 10:00 Brandon Turner, Applications of Likelihood-free Bayesian Methods in Cognitive Science
10:30 11:10 David Nott, Uses of ABC in prior choice and Bayesian model checking
11:10 11:35 Mijung Park, K2-ABC: ABC with Kernel Embeddings
11:35 12:00 Kenji Fukumizu, Kernel Mean Particle Filter with Intractable Likelihoods
14:30 15:10 Rob Deardon, ABC-based inference for epidemic models with uncertain underlying contact networks
15:10 15:50 Oksana Chkrebtii, Approximate Bayesian Computation for Inference on the Introduction and Spread Patterns of Invasive Species
16:30 17:00 Wentao Li, On the Asymptotic Behavior of ABC
17:00 17:40 Iain Murray, ABC as Learning
https://sites.google.com/site/abcinmontreal/
