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- David A. Knowles, Zoubin Ghahramani
- ArXiv
- 2010

A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data Y is modeled as a linear superposition, G, of a potentially infinite number of hidden factors, X. The Indianâ€¦ (More)

- David A. Knowles, Zoubin Ghahramani
- ICA
- 2007

A nonparametric Bayesian extension of Independent Components Analysis (ICA) is proposed where observed data Y is modelled as a linear superposition, G, of a potentially infinite number of hiddenâ€¦ (More)

- Tim Salimans, David A. Knowles
- ArXiv
- 2012

We propose a general algorithm for approximating nonstandard Bayesian posterior distributions. The algorithm minimizes the Kullback-Leibler divergence of an approximating distribution to theâ€¦ (More)

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the nonparametric flexibility ofâ€¦ (More)

Latent variable models for network data extract a summary of the relational structure underlying an observed network. The simplest possible models subdivide nodes of the network into clusters; theâ€¦ (More)

- Yang I. Li, Bryce van de Geijn, +5 authors Jonathan K. Pritchard
- Science
- 2016

Noncoding variants play a central role in the genetics of complex traits, but we still lack a full understanding of the molecular pathways through which they act. We quantified the contribution ofâ€¦ (More)

- David A. Knowles, Tom Minka
- NIPS
- 2011

Variational Message Passing (VMP) is an algorithmic implementation of the Variational Bayes (VB) method which applies only in the special case of conjugate exponential family models. We propose anâ€¦ (More)

- Daniel A Glass, Ana ViÃ±uela, +19 authors Tim D. Spector
- Genome Biology
- 2012

Previous studies have demonstrated that gene expression levels change with age. These changes are hypothesized to influence the aging rate of an individual. We analyzed gene expression changes withâ€¦ (More)

Nonparametric Bayesian models provide a framework for flexi bl probabilistic modelling of complex datasets. Unfortunately, the high-di mensional averages required for Bayesian methods can be slow,â€¦ (More)

WHAT: a probabilistic model to infer binary latent variables that preserve neighbourhood structure of the data â€¢ WHY: to perform a nearest neighbour search for the purpose of retrieval â€¢ WHEN: inâ€¦ (More)