Results 51 to 60 of about 31,307,485 (221)
The dynamic hierarchical Dirichlet process [PDF]
Proceedings of the 25th international conference on Machine learning - ICML '08, 2008 The dynamic hierarchical Dirichlet process (dHDP) is developed to model the time-evolving statistical properties of sequential data sets. The data collected at any time point are represented via a mixture associated with an appropriate underlying model, in the framework of HDP. The statistical properties of data collected at consecutive time points are
Lu Ren, David B. Dunson, Lawrence Carin
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Prior Design for Dependent Dirichlet Processes: An Application to Marathon Modeling. [PDF]
PLoS ONE, 2016 This paper presents a novel application of Bayesian nonparametrics (BNP) for marathon data modeling. We make use of two well-known BNP priors, the single-p dependent Dirichlet process and the hierarchical Dirichlet process, in order to address two ...
Melanie F Pradier +2 more
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Understanding Hierarchical Processes
Entropy, 2022 Hierarchical stochastic processes, such as the hierarchical Dirichlet process, hold an important position as a modelling tool in statistical machine learning, and are even used in deep neural networks.
Wray Buntine
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Untangling sample and population level estimands in Bayesian causal computation
Journal of Causal InferenceModel-based Bayesian inference for sample and population-level causal estimands has been growing in popularity. This literature routinely emphasizes clear specification of the target estimand, however blind implementation of standard computational ...
Oganisian Arman
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Contributions to the Theory of Dirichlet Processes
The Annals of Probability, 1973 Abstract : The authors derive some basic properties of a sample X(1),...,X(n) from a Dirichlet process. Let r(i) = 0 if X(i) = X(k) for some k = 1, ..., i-1, and 1 otherwise. They authors first establish the distribution of the summation from i=1 to n of r(i), the number of distinct observations in the sample, and certain conditional and unconditional ...
Korwar, Ramesh M., Hollander, Myles
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Mathematics, 2022 The extreme value theory is widely used in economic and environmental domains, it aims to study the stochastic extreme behaviors associated with rare events.
Yingjie Wang, Xinsheng Liu
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Note on Convergence of Dirichlet Processes
Bulletin of the London Mathematical Society, 1993 The authors study the limit behaviour of a sequence \(\{P_ n\}\) of distributions on the path space \(C([0,\infty), \mathbb{R}^ d)\) where the definition of each \(P_ n\) involves a measurable function \(\varphi_ n\) on \(\mathbb{R}^ d\) and a conservative diffusion process associated with a peculiar Dirichlet form.
Lyons, T, Zhang, T
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On a rapid simulation of the Dirichlet process
Statistics & Probability Letters, 2012 We describe a simple and efficient procedure for approximating the Lévy measure of a $\text{Gamma}(α,1)$ random variable. We use this approximation to derive a finite sum-representation that converges almost surely to Ferguson's representation of the Dirichlet process based on arrivals of a homogeneous Poisson process.
Zarepour, Mahmoud, Al-Labadi, Luai
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Location Dependent Dirichlet Processes [PDF]
, 2017 Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However, in their basic form they do not directly integrate dependency information among data arising from space and time. In this paper, we propose location dependent Dirichlet processes (LDDP) which incorporate nonparametric Gaussian processes in the DP modeling framework
Shiliang Sun +2 more
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A note on the implementation of hierarchical dirichlet processes [PDF]
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers on - ACL-IJCNLP '09, 2009 The implementation of collapsed Gibbs samplers for non-parametric Bayesian models is non-trivial, requiring considerable book-keeping. Goldwater et al. (2006a) presented an approximation which significantly reduces the storage and computation overhead, but we show here that their formulation was incorrect and, even after correction, is grossly ...
Blunsom, Phil +3 more
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