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A constructive definition of dirichlet priors
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The article was published on 1991-01-01 and is currently open access. It has received 1560 citations till now. The article focuses on the topics: Hierarchical Dirichlet process & Constructive.read more
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Book ChapterDOI
Hierarchical Dirichlet Process for Tracking Complex Topical Structure Evolution and Its Application to Autism Research Literature
TL;DR: A framework based on discretization of time into epochs, epoch-wise topic discovery using a hierarchical Dirichlet process-based model, and a temporal similarity graph which allows for the modelling of complex topic changes is proposed.
Journal ArticleDOI
Nonparametric Bayes Testing of Changes in a Response Distribution with an Ordinal Predictor
TL;DR: A Bayesian nonparametric method is proposed for testing for distribution changes across an ordinal predictor using a dynamic mixture of Dirichlet processes that allows the response distribution to change flexibly at each level of the predictor.
Posted Content
Program Synthesis and Semantic Parsing with Learned Code Idioms
TL;DR: PATOIS as discussed by the authors is a system that allows a neural program synthesizer to explicitly interleave high-level and low-level reasoning at every generation step by automatically mining common code idioms from a given corpus and incorporating them into the underlying language for neural synthesis.
Journal ArticleDOI
Additive mixed models with Dirichlet process mixture and P-spline priors
TL;DR: This work proposes a fully Bayesian approach based on Markov chain Monte Carlo simulation techniques that allows for the semiparametric specification of both the trend function and the random effects distribution and investigates the advantages of such DPM prior structures for random effects.
Journal ArticleDOI
Efficient functional clustering of protein sequences using the Dirichlet process
TL;DR: A novel probabilistic framework that models subfamilies within a known protein family, which uses Dirichlet mixture densities to estimate amino acid preferences within subfamily clusters, and places aDirichlet process prior on the overall set of clusters.
References
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Journal ArticleDOI
A Bayesian Analysis of Some Nonparametric Problems
TL;DR: In this article, a class of prior distributions, called Dirichlet process priors, is proposed for nonparametric problems, for which treatment of many non-parametric statistical problems may be carried out, yielding results that are comparable to the classical theory.
Journal ArticleDOI
Ferguson Distributions Via Polya Urn Schemes
TL;DR: In this article, it was shown that a random probability measure P* on X has a Ferguson distribution with parameter p if for every finite partition (B1, *. *, B) of X, the vector p*(B,), * * *, p *(B) has a Dirichlet distribution with parameters (Bj), *--, cp(B,) (when p(B), = 0, this means p*) = 0 with probability 1).
Journal ArticleDOI
Implicit renewal theory and tails of solutions of random equations
TL;DR: For the solutions of certain random equations, or equivalently the stationary solutions of the random recurrences, the distribution tails are evaluated by renewal-theoretic methods as mentioned in this paper.
Book ChapterDOI
Bayesian density estimation by mixtures of normal distributions
TL;DR: In this article, a mixture of a countable number of normal distributions is used to estimate a density f(x) on the real line, which is then used for kernel estimation.
Journal ArticleDOI
On the Asymptotic Behavior of Bayes' Estimates in the Discrete Case
TL;DR: In this article, it was shown that the posterior probability converges to point mass at the true parameter value among almost all sample sequences (for short, the posterior is consistent; see Definition 1) exactly for parameter values in the topological carrier of the prior.