Bayesian nonparametric cross-study validation of prediction methods

Nonparametric Bayesian Methods

. Most of this book emphasizes frequentist methods, especially for nonparametric problems. However, there are Bayesian approaches to many nonparametric problems. In this chapter we present some of the most commonly used nonparametric Bayesian methods. These methods place priors on infinite dimensional spaces. The priors are based on certain stochastic processes called Dirichlet processes and Ga...

Nonparametric Bayesian Regression Methods

A common problem in statistics, and other disciplines , is to approximate adequately a function of several variables. In this paper we review some possible nonparametric Bayesian models with which we can perform this multiple regression problem. We shall also demonstrate how these basic models can be extended to allow the analysis of time series , both conventional and nancial, survival analysi...

Bayesian Nonparametric and Parametric Inference

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

Nonparametric Bayesian Methods 1 What is Nonparametric Bayes?

Bayesian Nonparametric Methods for High-dimensional Data

DAVID C. KESSLER: Bayesian Nonparametric Methods for High-Dimensional Data (Under the direction of Dr. David B. Dunson and Dr. Amy H. Herring) Bayesian nonparametric (BNP or NP Bayes) methods have enjoyed great strides forward in recent years. BNP methods embody the belief that inference is best driven by the data itself with minimal assumptions about the underlying model; this approach has mot...