Inspired by the "One World Probability SeminarLink opens in a new window", in April 2020 we decided to run the One World Approximate Bayesian Computation (ABC) Seminar, a (now) monthly series of seminars that will take place on Zoom on Thursdays, typically 9.30am or 1.30pm [UK time]. The idea is to gather members and disseminate results and innovation during these weeks and months under lockdown.

The focus of this seminar series is not limited to ABC. Indeed, it spans over likelihood-free inference, ML related techniques, Approximate Bayesian Inference, etc. Feel free to contact any of the organisers if you want to suggest yourself or someone else for a talk.

All webinars will be held on Zoom, with a link shared on the email sent via the mailing list. So if you are interested in the ABC world seminar and would like to hear from us monthly about the announced speaker, title and abstract and, most importantly, be able to join the talk, please register hereLink opens in a new window.

Season 4 -- 2022/2023

2nd February, 1.30pm UK time: Joel Dyer (University of Oxford)

Title: Approximate Bayesian Computation with Path Signatures
Abstract: Simulation models often lack tractable likelihood functions, making likelihood-free inference methods indispensable. Approximate Bayesian computation (ABC) generates likelihood-free posterior samples by comparing simulated and observed data through some distance measure, but existing approaches are often poorly suited to time series simulators, for example due to an independent and identically distributed data assumption. In this talk, we will discuss our work on the use of path signatures in ABC as a means to handling the sequential nature of time series data of different kinds. We will begin by discussing popular approaches to ABC and how they may be extended to time series simulators. We will then introduce path signatures, and discuss how signatures naturally lead to two instances of ABC for time series simulators. Finally, we will demonstrate that the resulting signature-based ABC procedures can produce competitive Bayesian parameter inference for simulators generating univariate, multivariate, irregularly spaced, and even non-Euclidean sequences.

Reference: J. Dyer, P. Cannon, S. M Schmon (2022). Approximate Bayesian Computation with Path Signatures. arXiv preprint 2106.12555Link opens in a new window

	Speaker	Title	Abstract	Slides	Video
02.02.2023 1.30pm	Joel DyerLink opens in a new window	Approximate Bayesian Computation with Path SignaturesLink opens in a new window	AbstractLink opens in a new window
24.11.2022 1.30pm UK time	Yuexi WangLink opens in a new window	Adversarial Bayesian SimulationLink opens in a new window	AbstractLink opens in a new window	SlidesLink opens in a new window	VideoLink opens in a new window
20.10.2022, 2.30pm UK time	Radu CraiuLink opens in a new window	Bayesian Computation Strategies for Big Data and Intractable Models	AbstractLink opens in a new window	SlidesLink opens in a new window	VideoLink opens in a new window
29.09.2022, 1.30pm Uk time	Till HoffmannLink opens in a new window	Minimizing the Expected Posterior Entropy Yields Optimal Summary StatisticsLink opens in a new window	AbstractLink opens in a new window	SlidesLink opens in a new window	VideoLink opens in a new window

Date	Speakers	Title	Abstract	Slides	Video
16.07.2020	Ruth BakerLink opens in a new window	Multi-fidelity Approximate Bayesian computationLink opens in a new window	AbstractLink opens in a new window	SlidesLink opens in a new window	VideoLink opens in a new window
02.07.2020	Chris DrovandiLink opens in a new window	Improving Bayesian Synthetic Likelihood via TransformationsLink opens in a new window	AbstractLink opens in a new window	SlidesLink opens in a new window	VideoLink opens in a new window
18.06.2020	Hien Duy NguyenLink opens in a new window	Approximate Bayesian computation via the energy statisticLink opens in a new window	AbstractLink opens in a new window	SlidesLink opens in a new window	VideoLink opens in a new window
04.06.2020	Irene TubikanecLink opens in a new window	Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEsLink opens in a new window	AbstractLink opens in a new window	SlidesLink opens in a new window	VideoLink opens in a new window
21.05.2020	Gael MartinLink opens in a new window	Focused Bayesian PredictionLink opens in a new window	AbstractLink opens in a new window	SlidesLink opens in a new window	VideoLink opens in a new window
07.05.2020	Umberto PicchiniLink opens in a new window	Stratified sampling and bootstrapping for approximate Bayesian computationLink opens in a new window	AbstractLink opens in a new window	SlidesLink opens in a new window	VideoLink opens in a new window
23.04.2020	Ivis Kerama Link opens in a new windowand Richard EverittLink opens in a new window	Rare event ABC-SMC^2Link opens in a new window	AbstractLink opens in a new window	SlidesLink opens in a new window	VideoLink opens in a new window
09.04.2020	Dennis PrangleLink opens in a new window	Distilling importance samplingLink opens in a new window	AbstractLink opens in a new window	SlidesLink opens in a new window	VideoLink opens in a new window