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 …

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 …

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 …

Approximate Bayesian computation (ABC) constitutes a class of computational methods
rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function …

We propose a framework for general Bayesian inference. We argue that a valid update of a
prior belief distribution to a posterior can be made for parameters which are connected to …

Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in
many other branches of science. It provides a principled framework for dealing with …

Partially observed Markov process (POMP) models, also known as hidden Markov models
or state space models, are ubiquitous tools for time series analysis. The R package pomp …

Abstract Motivation: Approximate Bayesian computation (ABC) methods provide an
elaborate approach to Bayesian inference on complex models, including model choice. Both …

Markov chain Monte Carlo methods are often deemed too computationally intensive to be of
any practical use for big data applications, and in particular for inference on datasets …