English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Kernel methods have revolutionized the fields of pattern recognition andmachine learning. Their success, however, critically depends on the choice ofkernel parameters. Using Gaussian process (GP) classification as a workingexample, this paper focuses on Bayesian inference of covariance (kernel)parameters using Markov chain Monte Carlo (MCMC) methods. The motivation isthat, compared to standard optimization of kernel parameters, they have beensystematically demonstrated to be superior in quantifying uncertainty inpredictions. Recently, the Pseudo-Marginal MCMC approach has been proposed as apractical inference tool for GP models. In particular, it amounts in replacingthe analytically intractable marginal likelihood by an unbiased estimateobtainable by approximate methods and importance sampling. After discussing thepotential drawbacks in employing importance sampling, this paper proposes theapplication of annealed importance sampling. The results empiricallydemonstrate that compared to importance sampling, annealed importance samplingcan reduce the variance of the estimate of the marginal likelihoodexponentially in the number of data at a computational cost that scales onlypolynomially. The results on real data demonstrate that employing annealedimportance sampling in the Pseudo-Marginal MCMC approach represents a stepforward in the development of fully automated exact inference engines for GPmodels.

Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMC