Efficient Sampling for Gaussian Process Inference using Control Variables

By Michalis K. Titsias, Neil D. Lawrence, Magnus Rattray. 2008

51 citations recorded by the source corpus.

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Citations in the corpus

Gaussian Process Structural Equation Models with Latent Variables (2010, background): “The conditional density p ( ) is known to be sharply peaked for moderate sizes of M (at the order of hundreds) (Titsias et al., 2009), which may cause mixing problems for the Markov chain.”
Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes (2008, methodology): “Possible extensions to this method include more efficient sampling exploiting control variable methods [17], embedding characteristics of a dynamical system in the design of covariance functions and application of our method to models involving partial differential equations.”
Elliptical slice sampling (2009, methodology): “Several authors have previously addressed the issue of sampling from models containing strongly correlated Gaussians, notably the recent work of Titsias et al. (2009). In this paper we provide a technique called elliptical slice sampling that is simpler and often faster than other methods, while als”
Slice sampling covariance hyperparameters of latent Gaussian models (2010, background): “Recent work has focused on transition operators for updating the latent variables f given data and a fixed covariance Σθ [6, 1].”
Bayesian Gaussian Process Latent Variable Model (2010, methodology): “A potential strategy would be to use the control points idea used to speed up MCMC in GPs (Titsias et al., 2009) in order to encode the variational posterior, effectively decoupling these correlations and speeding convergence of the optimizer.”