Normalized random measures driven by increasing additive processes

By Luis E. Nieto-Barajas, Igor Prünster, Stephen G. Walker. 2004

72 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/dfa2c5146c10531c4fe0153c47dde3e67d872f47

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In Artificial Intelligence

In Finance

Lancelot F. James

In Statistics and Probability

Citations in the corpus

Bayesian Poisson process partition calculus with an application to Bayesian Lévy moving averages (2005, background): “A further generalization of these types of models is given in [33].”
Bayesian Nonparametrics: Models beyond the Dirichlet process (2009, unknown): “For an application of Pólya trees to survival analysis see Muliere and Walker (1997). Pólya trees represent an important extension of the Dirichlet process since they stand as priors for absolutely continuous distributions on R: nonetheless, they features a serious drawback, since the inferences ded”