Work

Normalized random measures driven by increasing additive processes

Year: 2004

Type: article

Abstract: This paper introduces and studies a new class of nonparametric prior distributions. Random probability distribution functions are constructed via normalization of random measures driven by increasing additive processes. In particular, we present results for the distribution of means under both prior and posterior conditions and, via the use of strategic latent variables, undertake a full Bayesian analysis. Our class of priors includes the well-known and widely used mixture of a Dirichlet process.

Source: The Annals of Statistics

Authors: Luis E. Nieto-Barajas, Igor Prünster, Stephen G. Walker

Institutions: Instituto Tecnológico Autónomo de México, University of Kent, University of Pavia

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: University of Bath, Consejo Nacional de Ciencia y Tecnología, Ministero dell’Istruzione, dell’Università e della Ricerca, Engineering and Physical Sciences Research Council, Division of Mathematical Sciences

OpenAlex ID: W2091478817

FWCI: 2.034

Cites: 27

Cited by: 42

Related to: