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Truncated Poisson–Dirichlet approximation for Dirichlet process hierarchical models
The Dirichlet process was introduced by Ferguson in 1973 to use with Bayesian nonparametric inference problems. A lot of work has been done based on...
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Applying Kumaraswamy distribution on stick-breaking process: a Dirichlet neural topic model approach
In recent years, neural topic modeling has increasingly raised extensive attention due to its capacity on generating coherent topics and flexible...
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Multivariate Powered Dirichlet-Hawkes Process
The publication time of a document carries a relevant information about its semantic content. The Dirichlet-Hawkes process has been proposed to...
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Latent Dirichlet Allocation
This chapter first introduces the Dirichlet distribution, then describes the latent Dirichlet distribution model, and finally presents the algorithms...
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One Step Entropy Variation in Sequential Sampling of Species for the Poisson-Dirichlet Process
We consider the sequential sampling of species, where observed samples are classified into the species they belong to. We are particularly interested...
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Density-adaptive registration of pointclouds based on Dirichlet Process Gaussian Mixture Models
We propose an algorithm for rigid registration of pre- and intra-operative patient anatomy, represented as pointclouds, during minimally invasive...
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Powered Dirichlet Process - Controlling the “Rich-Get-Richer” Assumption in Bayesian Clustering
The Dirichlet process is one of the most widely used priors in Bayesian clustering. This process allows for a nonparametric estimation of the number...
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Powered Dirichlet–Hawkes process: challenging textual clustering using a flexible temporal prior
The textual content of a document and its publication date are intertwined. For example, the publication of a news article on a topic is influenced...
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Dirichlet process and its developments: a survey
The core of the nonparametric/semiparametric Bayesian analysis is to relax the particular parametric assumptions on the distributions of interest to...
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Smoothed Dirichlet Distribution
When the cells are ordinal in the multinomial distribution, i.e., when cells have a natural ordering, guaranteeing that the borrowing information...
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Dirichlet-Survival Process: Scalable Inference of Topic-Dependent Diffusion Networks
Information spread on networks can be efficiently modeled by considering three features: documents’ content, time of publication relative to other...
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Dirichlet process mixture models to impute missing predictor data in counterfactual prediction models: an application to predict optimal type 2 diabetes therapy
BackgroundThe handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing...
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Shifted-Scaled Dirichlet-Based Hierarchical Dirichlet Process Hidden Markov Models with Variational Inference Learning
In this chapter, we propose a variational Bayes framework for learning hidden Markov models (HMMs). This approach has some advantages over other...
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Unsupervised nested Dirichlet finite mixture model for clustering
The Dirichlet distribution is widely used in the context of mixture models. Despite its flexibility, it still suffers from some limitations, such as...
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Weak Dirichlet Processes
An important generalization of the notion of Dirichlet process is the one of weak Dirichlet process that is characterized as the sum of a local...
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Stochastic Dirichlet–Poisson Problem on Hilbert Spaces
This paper is devoted to the study of the existence and the uniqueness of the solution of the Stochastic Dirichlet-Poisson problems on Hilbert...
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Self-coloring-Driven Plume Source Localization Strategy for Multiple Robots Using Dirichlet Process Gaussian Mixture Model and Mutation Random Salp Swarm Algorithm
Accidents of leaks and emissions of flammable, explosive, and toxic substances severely threaten people’s health and public safety. Traditional...
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A scaled dirichlet-based predictive model for occupancy estimation in smart buildings
In this study, we introduce a predictive model leveraging the scaled Dirichlet mixture model (SDMM). This data-driven approach offers enhanced...
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Space-filling designs with a Dirichlet distribution for mixture experiments
Uniform designs are widely used for experiments with mixtures. The uniformity of the design points is usually evaluated with a discrepancy criterion....
