Computer scientists have long recognized that naive implementations of algorithms often
result in a paralyzing degree of redundant computation. More sophisticated implementations …

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 …

We combine amortized neural posterior estimation with importance sampling for fast and
accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian …

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 …

Autoregressive models are among the best performing neural density estimators. We
describe an approach for increasing the flexibility of an autoregressive model, based on …

We introduce a model that learns to convert simple hand drawings into graphics programs
written in a subset of\LaTeX.~ The model combines techniques from deep learning and …

This book is a graduate-level introduction to probabilistic programming. It not only provides a
thorough background for anyone wishing to use a probabilistic programming system, but …

We provide theoretical and empirical evidence that using tighter evidence lower bounds
(ELBOs) can be detrimental to the process of learning an inference network by reducing the …

Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a
stochastic simulator and inferring posterior distributions from model-simulations. To improve …

Many statistical models can be simulated forwards but have intractable likelihoods.
Approximate Bayesian Computation (ABC) methods are used to infer properties of these …