David J.C. MacKay
Elliptical Slice Sampling.
Tue, 01/05/2010 - 16:01 — SomNambulAuthors: Ryan Prescott Adams, Iain Murray, David J.C. MacKay
Subjects: Computation
AbstractMany probabilistic models introduce strong dependencies between variables
using a latent multivariate Gaussian distribution or a Gaussian process. We
present a new Markov chain Monte Carlo algorithm for performing inference in
models with multivariate Gaussian priors. Its key properties are: 1) it has
simple, generic code applicable to many models, 2) it has no free parameters,
3) it works well for a variety of Gaussian process based models.Nonparametric Bayesian Density Modeling with Gaussian Processes.
Fri, 12/25/2009 - 16:01 — SomNambulAuthors: Ryan Prescott Adams, Iain Murray, David J.C. MacKay
Subjects: Computation
AbstractWe present the Gaussian process density sampler (GPDS), an exchangeable
generative model for use in nonparametric Bayesian density estimation. Samples
drawn from the GPDS are consistent with exact, independent samples from a
distribution defined by a density that is a transformation of a function drawn
from a Gaussian process prior. Our formulation allows us to infer an unknown
density from data using Markov chain Monte Carlo, which gives samples from the
posterior distribution over density functions and from the predictive
distribution on data space. We describe two such MCMC methods.
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