971 citations · 3k across the 74 of their papers we have counts for
6 papers · 2 filters
Probabilistic Integration: A Role in Statistical Computation?
François-Xavier Briol, Chris. J. Oates, Mark Girolami +2
A research frontier has emerged in scientific computation, wherein numerical error is regarded as a source of epistemic uncertainty that can be modelled. This raises several statis…
Fast Two-Sample Testing with Analytic Representations of Probability Measures
Kacper Chwialkowski, Aaditya Ramdas, Dino Sejdinovic +1
We propose a class of nonparametric two-sample tests with a cost linear in the sample size. Two tests are given, both based on an ensemble of distances between analytic functions r…
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone +2
We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptive MCMC algorithm based on Hamiltonian Monte Carlo (HMC). On target densities where classical HMC is not an o…
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages
Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess +4
We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node,…
Unbiased Bayes for Big Data: Paths of Partial Posteriors
Heiko Strathmann, Dino Sejdinovic, Mark Girolami
A key quantity of interest in Bayesian inference are expectations of functions with respect to a posterior distribution. Markov Chain Monte Carlo is a fundamental tool to consisten…
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings
Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic
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 para…