Bayesian Nonparametric Weighted Sampling 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 methods for partial stochastic orderings

We discuss two methods of making nonparametric Bayesian inference on probability measures subject to a partial stochastic ordering. The first method involves a nonparametric prior for a measure on partially ordered latent observations, and the second involves rejection sampling. Computational approaches are discussed for each method, and interpretations of prior and posterior information are di...

An Assessment of Bayesian Inference

A Monte Carlo study is performed to assess the properties of a Bayesian procedure for inference in nonparametric regression with a binary response variable. The logodds (logit) of the probability of the response is modeled as an integrated Wiener process. This leads to a generalized smoothing spline as the posterior mode. Such priors have been used by many authors for nonparametric regression w...

Bayesian time series models and scalable inference

With large and growing datasets and complex models, there is an increasing need for scalable Bayesian inference. We describe two lines of work to address this need. In the first part, we develop new algorithms for inference in hierarchical Bayesian time series models based on the hidden Markov model (HMM), hidden semi-Markov model (HSMM), and their Bayesian nonparametric extensions. The HMM is ...