Nonparametric Bayesian methods: a gentle introduction and overview

Bayes and I: A Gentle Introduction to the Bayesian Approach

Yet, behind the scenes, a very strong assumption—or belief— resides: that there is a fixed single population parameter. In this case, the mean value of weights for teenagers in the city is a single value. Therefore, without a serious flaw in the sampling process, the parameter is constant, though unknown, across all possible samples [2]. However, what if the parameter is not fixed but rather ha...

Introduction to NonParametric Methods

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

. 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...