A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

Many problems in the physical sciences, machine learning, and statistical inference
necessitate sampling from a high-dimensional, multimodal probability distribution. Markov …

Abstract Deep Gaussian processes (DGPs) are increasingly popular as predictive models in
machine learning for their nonstationary flexibility and ability to cope with abrupt regime …

We present a general-purpose method to train Markov chain Monte Carlo kernels,
parameterized by deep neural networks, that converge and mix quickly to their target …

Transfer operators such as the Perron–Frobenius or Koopman operator play an important
role in the global analysis of complex dynamical systems. The eigenfunctions of these …

Bayesian Modeling and Computation in Python aims to help beginner Bayesian
practitioners to become intermediate modelers. It uses a hands on approach with PyMC3 …

Recently there have been increasing interests in learning and inference with implicit
distributions (ie, distributions without tractable densities). To this end, we develop a gradient …

Complicated generative models often result in a situation where computing the likelihood of
observed data is intractable, while simulating from the conditional density given a parameter …

Abstract We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptive
MCMC algorithm based on Hamiltonian Monte Carlo (HMC). On target densities where …

Feature attribution for kernel methods is often heuristic and not individualised for each
prediction. To address this, we turn to the concept of Shapley values (SV), a coalition game …