Independent Finite Approximations for Bayesian Nonparametric Inference

Bayesian Nonparametric and Parametric Inference

This paper reviews Bayesian Nonparametric methods and discusses how parametric predictive densities can be constructed using nonparametric ideas.

Efficient Nonparametric Importance Sampling for Bayesian Inference

Bayesian Nonparametric Inference for Nonhomogeneous Poisson Processes

Several classes of nonparametric priors are employed to model the rate of occurrence of failures of the nonhomogeneous Poisson process used in software reliability or in repairable systems. The classes include the gamma process prior, the beta process prior, and the extended gamma process prior. We derive the posterior distribution for each process. Sampling based methods are developed for Baye...

Bayesian Nonparametric Weighted Sampling Inference

It has historically been a challenge to perform Bayesian inference in a design-based survey context. The present paper develops a Bayesian model for sampling inference in the presence of inverse-probability weights. We use a hierarchical approach in which we model the distribution of the weights of the nonsampled units in the population and simultaneously include them as predictors in a nonpara...

Variational Inference for Nonparametric Bayesian Quantile Regression

Quantile regression deals with the problem of computing robust estimators when the conditional mean and standard deviation of the predicted function are inadequate to capture its variability. The technique has an extensive list of applications, including health sciences, ecology and finance. In this work we present a nonparametric method of inferring quantiles and derive a novel Variational Bay...