A new Bayesian framework called PIML combines machine learning analysis of electrical impedance signals with clinical prior ...

This Primer examines Skilling’s nested sampling algorithm for Bayesian inference and, more broadly, multidimensional integration. The principles of nested sampling are summarize ...

The open-source VIPR framework standardizes machine-learning inference for ill-posed inverse problems in reflectometry and ...

Bayesian inference offers a principled mechanism for updating beliefs by combining prior distributions with observed data via Bayes’s theorem. Central to this approach is the specification of a ...

We develop novel methods to make Bayesian inference more efficient, scalable, and practical. This includes work on variational methods, Monte Carlo algorithms, and techniques for handling complex ...

This course is available on the MSc in Applied Social Data Science, MSc in Data Science, MSc in Econometrics and Mathematical Economics, MSc in Financial Statistics, MSc in Financial Statistics ...