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Bayesian Nonparametric Methods

Bayesian nonparametric methods are a family of flexible Bayesian models in which model complexity is not fixed in advance but grows automatically with the data. The two most widely used members are the Dirichlet Process Mixture (DPM), which clusters observations without pre-specifying the number of clusters, and Gaussian Process (GP) regression, which places a prior directly over functions and performs regression or classification without committing to a parametric form. Both frameworks were formalised in the Bayesian nonparametric literature, with the canonical GP treatment given by Rasmussen and Williams (2006).

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Bayesian Nonparametric Methods (Dirichlet Process / Gaussian Process)

Taxonomisk metodpost · bayesian / bayesian

Rasmussen, C.E. & Williams, C.K.I. (2006). Gaussian Processes for Machine Learning. MIT Press. · ISBN 978-0262182539
Müller, P. & Quintana, F.A. (2004). Nonparametric Bayesian Data Analysis. Statistical Science, 19(1), 95–110. · DOI 10.1214/088342304000000017

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Same method familyBayesian Regressionmachine-suggested · Relational suggestion, not evidence.See alsoGaussian Processmachine-suggested · Relational suggestion, not evidence.Same method familyMCMCmachine-suggested · Relational suggestion, not evidence.