An increasingly important building block of large scale machine learning...
Monte Carlo methods - such as Markov chain Monte Carlo (MCMC) and piecew...
Motivated by a real-world application in cardiology, we develop an algor...
This paper introduces a new principled approach for offline policy
optim...
We propose two novel unbiased estimators of the integral
∫_[0,1]^sf(u) d...
In the context of state-space models, backward smoothing algorithms rely...
We develop a (nearly) unbiased particle filtering algorithm for a specif...
This paper is concerned with online filtering of discretely observed
non...
We consider particle filters with weakly informative observations (or
`p...
Particle smoothers are SMC (Sequential Monte Carlo) algorithms designed ...
We study different variants of the Gibbs sampler algorithm from the
pers...
We propose cube thinning, a novel method for compressing the output of a...
Markov chain Monte Carlo (MCMC) methods to sample from a probability
dis...
A standard way to move particles in a SMC sampler is to apply several st...
Sequential Monte Carlo (SMC) samplers form an attractive alternative to ...
The Metropolis-Hastings algorithm allows one to sample asymptotically fr...
A statistical model is said to be un-normalised when its likelihood func...
ABC (approximate Bayesian computation) is a general approach for dealing...
We present a case-study demonstrating the usefulness of Bayesian hierarc...
The PAC-Bayesian approach is a powerful set of techniques to derive non-...
We develop a scoring and classification procedure based on the PAC-Bayes...
We consider the inverse reinforcement learning problem, that is, the pro...
Many models of interest in the natural and social sciences have no
close...