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 …

How can a machine learn from experience? Probabilistic modelling provides a framework
for understanding what learning is, and has therefore emerged as one of the principal …

The IceCube Neutrino Observatory has established the existence of a high-energy all-sky
neutrino flux of astrophysical origin. This discovery was made using events interacting within …

Many domains of science have developed complex simulations to describe phenomena of
interest. While these simulations provide high-fidelity models, they are poorly suited for …

As the world becomes increasingly complex, so do the statistical models required to analyse
the challenging problems ahead. For the very first time in a single volume, the Handbook of …

How can one perform Bayesian inference on stochastic simulators with intractable
likelihoods? A recent approach is to learn the posterior from adaptively proposed …

We describe NIMBLE, a system for programming statistical algorithms for general model
structures within R. NIMBLE is designed to meet three challenges: flexible model …

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 …

Generative adversarial networks (GANs) provide an algorithmic framework for constructing
generative models with several appealing properties: they do not require a likelihood …

Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …