Luigi Acerbi develops new methods for approximate Bayesian inference that are both tools for artificial intelligence and models of human intelligence. “We develop new “smart” machine learning methods, ...

Bayesian inference provides a robust framework for combining prior knowledge with new evidence to update beliefs about uncertain quantities. In the context of statistical inverse problems, this ...

We estimate by Bayesian inference the mixed conditional heteroskedasticity model of Haas et al. (2004a Journal of Financial Econometrics 2, 211–50). We construct a Gibbs sampler algorithm to compute ...

RIKEN AIP houses more than 40 research teams ranging from fundamentals of machine learning and optimization, applications in medicine, materials, and disaster, to analysis of ethics and social impact ...

The Department of Computer Science, Faculty of Science, University of Helsinki invites applications for a Postdoctoral Researcher in Probabilistic Machine Learning and Amortized Inference. The is an ...

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

To meet the Basel II regulatory requirements for the advanced measurement approaches, the bank’s internal model must include the use of internal data, relevant external data, scenario analysis and ...