Posterior consistency of logistic Gaussian process priors in density estimation

2007 ◽  
Vol 137 (1) ◽  
pp. 34-42 ◽  
Author(s):  
Surya T. Tokdar ◽  
Jayanta K. Ghosh
Keyword(s):  
Gaussian Process ◽  
Density Estimation ◽  
Posterior Consistency

Least square and Gaussian process for image based microalgal density estimation

2022 ◽  
Vol 193 ◽  
pp. 106678
Author(s):  
Linh Nguyen ◽  
Dung K. Nguyen ◽  
Truong X. Nghiem ◽  
Thang Nguyen
Keyword(s):  
Gaussian Process ◽  
Density Estimation ◽  
Least Square

scholarly journals POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES

2013 ◽  
Vol 30 (3) ◽  
pp. 606-646 ◽  
Author(s):  
Andriy Norets ◽  
Justinas Pelenis
Keyword(s):  
Large Class ◽  
Mixture Models ◽  
Density Estimation ◽  
Nonparametric Estimation ◽  
Simulation Study ◽  
Conditional Density ◽  
Small Samples ◽  
Posterior Consistency ◽  
Bayesian Nonparametric Estimation ◽  
Strongly Consistent

This paper considers Bayesian nonparametric estimation of conditional densities by countable mixtures of location-scale densities with covariate dependent mixing probabilities. The mixing probabilities are modeled in two ways. First, we consider finite covariate dependent mixture models, in which the mixing probabilities are proportional to a product of a constant and a kernel and a prior on the number of mixture components is specified. Second, we consider kernel stick-breaking processes for modeling the mixing probabilities. We show that the posterior in these two models is weakly and strongly consistent for a large class of data-generating processes. A simulation study conducted in the paper demonstrates that the models can perform well in small samples.

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