Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

Bayesian experimental design is a fast growing area of research with many real‐world
applications. As computational power has increased over the years, so has the development …

Abstract We present Sequential Neural Likelihood (SNL), a new method for Bayesian
inference in simulator models, where the likelihood is intractable but simulating data from …

Estimating the parameters of mathematical models is a common problem in almost all
branches of science. However, this problem can prove notably difficult when processes and …

Complexity science and systems thinking are increasingly recognized as relevant
paradigms for studying systems where biology, psychology, and socioenvironmental factors …

Having the ability to work with complex models can be highly beneficial. However, complex
models often have intractable likelihoods, so methods that involve evaluation of the …

We develop, discuss, and compare several inference techniques to constrain theory
parameters in collider experiments. By harnessing the latent-space structure of particle …

The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

We introduce a novel statistical calibration framework for physical models, relying on
probabilistic embedding of model discrepancy error within the model. For clarity of …

Bayesian inference methods typically require a considerable amount of computation time in
the calculation of forward models. This limitation restricts the application of Bayesian …