Normalizing flows provide a general mechanism for defining expressive probability
distributions, only requiring the specification of a (usually simple) base distribution and a …

As population genomic datasets grow in size, researchers are faced with the daunting task
of making sense of a flood of information. To keep pace with this explosion of data …

We have addressed the geophysical problem of obtaining an elastic model of the
subsurface from recorded normal-incidence seismic data using convolutional neural …

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 …

Ecology has joined a world of big data. Two complementary frameworks define big data:
data that exceed the analytical capacities of individuals or disciplines or the “Four Vs” axes …

Motivation: DIYABC is a software package for a comprehensive analysis of population
history using approximate Bayesian computation on DNA polymorphism data. Version 2.0 …

History Markov chain Monte Carlo (MCMC) methods, in which plausible values for unknown
quantities are simulated from their appropriate probability distribution, have revolutionised …

High-throughput sequencing technologies are revolutionizing the life sciences. The past 12
months have seen a burst of genome sequences from non-model organisms, in each case …

Connectivity is classically considered an emergent property of landscapes encapsulating
individuals' flows across space. However, its operational use requires a precise …

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