Results 1 to 10 of about 31,307,485 (221)
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 tutorial on Dirichlet Process mixture modeling. [PDF]
J Math Psychol, 2019 Bayesian nonparametric (BNP) models are becoming increasingly important in psychology, both as theoretical models of cognition and as analytic tools. However, existing tutorials tend to be at a level of abstraction largely impenetrable by non-technicians.
Li Y, Schofield E, Gönen M.
europepmc +2 more sources
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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From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering. [PDF]
Adv Data Anal Classif, 2019 In model-based clustering mixture models are used to group data points into clusters. A useful concept introduced for Gaussian mixtures by Malsiner Walli et al.
Frühwirth-Schnatter S +1 more
europepmc +2 more sources
Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models. [PDF]
Nat Commun, 2017 Using genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans.
Zeng P, Zhou X.
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IEEE Transactions on Neural Networks and Learning Systems, 2019 In this paper, we develop a novel variational Bayesian learning method for the Dirichlet process (DP) mixture of the inverted Dirichlet distributions, which has been shown to be very flexible for modeling vectors with positive elements.
Zhan-Yu Ma +5 more
semanticscholar +3 more sources
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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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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Clustering consistency with Dirichlet process mixtures [PDF]
Biometrika, 2022 Dirichlet process mixtures are flexible nonparametric models, particularly suited to density estimation and probabilistic clustering. In this work we study the posterior distribution induced by Dirichlet process mixtures as the sample size increases ...
F. Ascolani +3 more
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A Dirichlet Process Mixture of Robust Task Models for Scalable Lifelong Reinforcement Learning [PDF]
IEEE Transactions on Cybernetics, 2022 While reinforcement learning (RL) algorithms are achieving state-of-the-art performance in various challenging tasks, they can easily encounter catastrophic forgetting or interference when faced with lifelong streaming information.
Zhi Wang, Chun-Lin Chen, Daoyi Dong
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