334 citations · 2.2k across the 37 of their papers we have counts for
9 papers · 1 filter
A determinant-free method to simulate the parameters of large Gaussian fields
Louis Ellam, Heiko Strathmann, Mark Girolami +1
We propose a determinant-free approach for simulation-based Bayesian inference in high-dimensional Gaussian models. We introduce auxiliary variables with covariance equal to the in…
Differentiation of the Cholesky decomposition
Iain Murray
We review strategies for differentiating matrix-based computations, and derive symbolic and algorithmic update rules for differentiating expressions containing the Cholesky decompo…
Pseudo-Marginal Slice Sampling
Iain Murray, Matthew M. Graham
Markov chain Monte Carlo (MCMC) methods asymptotically sample from complex probability distributions. The pseudo-marginal MCMC framework only requires an unbiased estimator of the…
Parallel MCMC with Generalized Elliptical Slice Sampling
Robert Nishihara, Iain Murray, Ryan P. Adams
Probabilistic models are conceptually powerful tools for finding structure in data, but their practical effectiveness is often limited by our ability to perform inference in them.…
MCMC for doubly-intractable distributions
Iain Murray, Zoubin Ghahramani, David MacKay
Markov Chain Monte Carlo (MCMC) algorithms are routinely used to draw samples from distributions with intractable normalization constants. However, standard MCMC algorithms do not…
Driving Markov chain Monte Carlo with a dependent random stream
Iain Murray, Lloyd T. Elliott
Markov chain Monte Carlo is a widely-used technique for generating a dependent sequence of samples from complex distributions. Conventionally, these methods require a source of ind…