Results 1 to 10 of about 15,452 (256)
Dirichlet process mixture models for single-cell RNA-seq clustering [PDF]
Biology Open, 2022 Clustering of cells based on gene expression is one of the major steps in single-cell RNA-sequencing (scRNA-seq) data analysis. One key challenge in cluster analysis is the unknown number of clusters and, for this issue, there is still no comprehensive ...
Nigatu A. Adossa +2 more
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A Dirichlet Process Prior Approach for Covariate Selection [PDF]
Entropy, 2020 The variable selection problem in general, and specifically for the ordinary linear regression model, is considered in the setup in which the number of covariates is large enough to prevent the exploration of all possible models.
Stefano Cabras
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The Supervised Hierarchical Dirichlet Process [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable directly associated with that whole group. We compare the sHDP with another leading method for regression on grouped data, the supervised latent Dirichlet allocation (sLDA) model.
Andrew M. Dai, Amos J. Storkey
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Multimodal Hierarchical Dirichlet Process-Based Active Perception by a Robot [PDF]
Frontiers in Neurorobotics, 2018 In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory,
Tadahiro Taniguchi +2 more
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An Integral Characterization of the Dirichlet Process [PDF]
Journal of Theoretical Probability, 2019 We give a new integral characterization of the Dirichlet process on a general phase space. To do so we first prove a characterization of the nonsymmetric Beta distribution via size-biased sampling. Two applications are a new characterization of the Dirichlet distribution and a marked version of a classical characterization of the Poisson-Dirichlet ...
Gunter Last
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Dirichlet mixtures, the Dirichlet process, and the structure of protein space. [PDF]
J Comput Biol, 2013 Abstract The Dirichlet process is used to model probability distributions that are mixtures of an unknown number of components. Amino acid frequencies at homologous positions within related proteins have been fruitfully modeled by Dirichlet mixtures, and we use the Dirichlet process to derive such mixtures with an unbounded number of ...
Nguyen VA, Boyd-Graber J, Altschul SF.
europepmc +4 more sources
On a rapid simulation of the Dirichlet process
Statistics and 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.
Luai Al Labadi, Mahmoud Zarepour
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Truncated Poisson–Dirichlet approximation for Dirichlet process hierarchical models
Statistics and Computing, 2023 AbstractThe Dirichlet process was introduced by Ferguson in 1973 to use with Bayesian nonparametric inference problems. A lot of work has been done based on the Dirichlet process, making it the most fundamental prior in Bayesian nonparametric statistics.
Junyi Zhang, Angelos Dassios
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Genome-scale MicroRNA target prediction through clustering with Dirichlet process mixture model [PDF]
BMC Genomics, 2018 Background MicroRNA regulation is fundamentally responsible for fine-tuning the whole gene network in human and has been implicated in most physiological and pathological conditions.
Zeynep Hakguder +4 more
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Direct numerical solutions of the SIR and SEIR models via the Dirichlet series approach
PLoS ONE, 2023 Compartment models are implemented to understand the dynamic of a system. To analyze the models, a numerical tool is required. This manuscript presents an alternative numerical tool for the SIR and SEIR models.
Kiattisak Prathom, Asama Jampeepan
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