This seminar series explores “post-Bayesian” methods, which extend beyond traditional Bayesian inference to address its limitations in modern machine learning settings.

The seminar will run fortnightly from mid-February onwards. The first iteration of the series will be broken down into three ‘chapters’ consisting of between 4-6 talks in each chapter. Each chapter will focus on a different set of post-Bayesian ideas: generalised Bayes (led by Jeremias Knoblauch), predictive resampling-based ideas like Martingale posteriors (led by Edwin Fong), and PAC-Bayes (led by Pierre Alquier). To make this useful for the entire community, the talks in each chapter will seek to cover some key aspects of literature conducted under that chapter.

For more information, please visit the seminar series’ web page or register in the seminar’s mailing list.