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, ...

Experimental data and mathematical models are beginning to take equal billing in systems biology. Experimental observations without a framework in which to link them offer researchers only limited ...

We show how the notion ofmessage passing can be used to streamline the algebra and computer coding for fast approximate inference in large Bayesian semiparametric regression models. In particular, ...

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 ...

AbstractThe neutral theory of biodiversity challenges the classical niche-based view of ecological communities, where species attributes and environmental conditions jointly determine community ...

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 ...