Transformations and Bayesian density estimation

A Bayesian approach to parameter estimation for kernel density estimation via transformations

In this paper, we present a Markov chain Monte Carlo (MCMC) simulation algorithm for estimating parameters in the kernel density estimation of bivariate insurance claim data via transformations. Our data set consists of two types of auto insurance claim costs and exhibits a high-level of skewness in the marginal empirical distributions. Therefore, the kernel density estimator based on original ...

A Bayesian Density Estimation Algorithm

Density estimation is a central problem in data mining and knowledge discovery , with applications from data visualization and exploratory data analysis to supervised and unsupervised concept learning. This paper presents a simple nonparametric method for univariate density estimation that uses Bayesian inference and the minimum-message length principle to induce appropriate mixture models. Its...

Bayesian Density Estimation and Inference Using Mixtures

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Bayesian Density Estimation and Inference Using

We describe and illustrate Bayesian inference in models for density estimation using mixtures of Dirichlet processes. These models provide natural settings for density estimation, and are exempliied by special cases where data are modelled as a sample from mixtures of normal distributions. EEcient simulation methods are used to approximate various prior, posterior and predictive distributions. ...

Categorization as nonparametric Bayesian density estimation 1 Running head: CATEGORIZATION AS NONPARAMETRIC BAYESIAN DENSITY ESTIMATION Categorization as nonparametric Bayesian density estimation