Variational inference with α-divergences has been widely used in modern probabilistic
machine learning. Compared to Kullback-Leibler (KL) divergence, a major advantage of …

Stein variational gradient decent (SVGD) has been shown to be a powerful approximate
inference algorithm for complex distributions. However, the standard SVGD requires …

The Rosenbrock function is a ubiquitous benchmark problem in numerical optimization, and
variants have been proposed to test the performance of Markov chain Monte Carlo …

Filtering in nonlinear state-space models is known to be a challenging task due to the
posterior distribution being either intractable or expressed in a complex form. One of the …

We propose a novel adaptive importance sampling algorithm which incorporates Stein
variational gradient decent algorithm (SVGD) with importance sampling (IS). Our algorithm …

Markov chain Monte Carlo (MCMC) is a key algorithm in computational statistics, and as
datasets grow larger and models grow more complex, many popular MCMC algorithms …

Summary Measure transport is a rich area in applied mathematics that involves the
construction of deterministic transformations–known as transport maps–between probability …

Abstract Markov chain Monte Carlo (MCMC) is a key algorithm in computational statistics,
and as datasets grow larger and models grow more complex, many popular MCMC …

The Rosenbrock function is an ubiquitous benchmark problem for numerical optimisation,
and variants have been proposed to test the performance of Markov Chain Monte Carlo …

In many applications, Bayesian inverse problems can give rise to probability distributions
which contain complexities due to the Hessian varying greatly across parameter space. This …