Slice sampling covariance hyperparameters of latent
author: Iain Murray,
School of Informatics, University of Edinburgh
published: Jan. 12, 2011, recorded: December 2010, views: 9794
published: Jan. 12, 2011, recorded: December 2010, views: 9794
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Description
The Gaussian process (GP) is a popular way to specify dependencies between random variables in a probabilistic model. In the Bayesian framework the covariance structure can be specified using unknown hyperparameters. Integrating over these hyperparameters considers different possible explanations for the data when making predictions. This integration is often performed using Markov chain Monte Carlo (MCMC) sampling. However, with non-Gaussian observations standard hyperparameter sampling approaches require careful tuning and may converge slowly. In this paper we present a slice sampling approach that requires little tuning while mixing well in both strong- and weak-data regimes.
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The slides don't all (currently) transition at the correct times. If this hasn't been fixed, you can copy and paste the following code into your location bar (tested in Firefox 3.6). I hope this makes the talk easier to follow!
javascript:void(function(){var new_times=[0,6000,12000,25000,35000,84000,121000,181000,282000,370000,442000,526000,582000,644000,676000,717000,752000,775000,826000,862000,867000,914000,949000];sync={next:null};sync=sync.next={time: new_times[0],slide: slides[191581],idx: 0, title:"",next:0};syncs=sync;for (i=1;i<23;++i) sync=sync.next={time:new_times[i],slide:slides[191581+i],idx:i,title:"",next:0};}());
The slide transition times have now been fixed, so ignore my previous comment. Thanks Ana from videolectures!
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