The third season of the [now monthly] One World ABC Seminar is starting! With talks by
30 September: Matias Quiroz28 October: Michael Gutmann25 November: Clara Grazian27 January: Rafael Izbicki
The next One World ABC seminar will take place on Thursday at 11:30, UK time, and will broadcast a talk by Jakob Macke on Simulation-based inference for neuroscience. Here is the abstract
Neuroscience research makes extensive use of mechanistic models of neural dynamics — these models are often implemented through numerical simulators, requiring the use of simulation-based approaches to statistical inference. I will talk about our recent work on developing simulation based inference-methods using flexible density estimators parameterised with neural networks, our efforts on benchmarking these approaches, and applications to modelling problems in neuroscience.
Remember you need to register beforehand to receive the access code!
The One World ABC seminar will resume its talks on ABC methods with a talk on Thursday, 25 March, 12:30CET, by Mijung Park, from the Max Planck Institute for Intelligent Systems, on the exciting topic of producing differential privacy by ABC. (Talks will take place on a monthly basis.)
For the last One World ABC seminar of the year 2020, this coming Thursday, Matti Vihola is speaking from Finland on his recent Biometrika paper “On the use of ABC-MCMC with inflated tolerance and post-correction”. To attend the talk, all is required is a registration on the seminar webpage.
The Markov chain Monte Carlo (MCMC) implementation of ABC is often sensitive to the tolerance parameter: low tolerance leads to poor mixing and large tolerance entails excess bias. We propose an approach that involves using a relatively large tolerance for the MCMC sampler to ensure sufficient mixing, and post-processing of the output which leads to estimators for a range of finer tolerances. We introduce an approximate confidence interval for the related post-corrected estimators and propose an adaptive ABC-MCMC algorithm, which finds a balanced tolerance level automatically based on acceptance rate optimization. Our experiments suggest that post-processing-based estimators can perform better than direct MCMC targeting a fine tolerance, that our confidence intervals are reliable, and that our adaptive algorithm can lead to reliable inference with little user specification.
My friend and coauthor from Melbourne is giving the One World ABC seminar tomorrow. He will be talking at 10:30 UK time, 11:30 Brussels time, and 20:30 Melbourne time! On Robust and Efficient Approximate Bayesian Computation: A Minimum Distance Approach. Be on time!
