Results 21 to 30 of about 15,452 (256)
Multilinear Dirichlet Processes
CoRR, 2021 Dependent Dirichlet processes (DDP) have been widely applied to model data from distributions over collections of measures which are correlated in some way. On the other hand, in recent years, increasing research efforts in machine learning and data mining have been dedicated to dealing with data involving interactions from two or more factors. However,
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Mathematics, 2022 In this research, we investigate an optimal control problem governed by elliptic PDEs with Dirichlet boundary conditions on complex connected domains, which can be utilized to model the cooling process of concrete dam pouring.
Mengya Su, Liuqing Xie, Zhiyue Zhang
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Rank-Based Mixture Models for Temporal Point Processes
Frontiers in Applied Mathematics and Statistics, 2022 Temporal point process, an important area in stochastic process, has been extensively studied in both theory and applications. The classical theory on point process focuses on time-based framework, where a conditional intensity function at each given ...
Yang Chen, Yijia Ma, Wei Wu
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Approximation of Space-Time Fractional Equations
Fractal and Fractional, 2021 The aim of this paper is to provide approximation results for space-time non-local equations with general non-local (and fractional) operators in space and time.
Raffaela Capitanelli, Mirko D’Ovidio
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Background Subtraction with Dirichlet Processes [PDF]
, 2012 Background subtraction is an important first step for video analysis, where it is used to discover the objects of interest for further processing. Such an algorithm often consists of a background model and a regularisation scheme. The background model determines a per-pixel measure of if a pixel belongs to the background or the foreground, whilst the ...
Tom S. F. Haines, Tao Xiang 0002
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Nested Hierarchical Dirichlet Processes [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015 To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence, Special Issue on Bayesian ...
John W. Paisley +3 more
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IEEE Access, 2019 In this paper, we propose nonparametric hierarchical Bayesian models based on two inverted Dirichlet-based distributions and Pitman-Yor process for positive data features clustering.
Wentao Fan, Nizar Bouguila
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Semiparametric Bayesian analysis of high-dimensional censored outcome data
Statistical Theory and Related Fields, 2017 The Surveillance, Epidemiology and End Results (SEER) cancer database contains survival data for US individuals diagnosed with cancer. Semiparametric Bayesian methods are computationally expensive to fit for such large data-sets.
Chetkar Jha, Yi Li, Subharup Guha
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Memorized Variational Continual Learning for Dirichlet Process Mixtures
IEEE Access, 2019 Bayesian nonparametric models are theoretically suitable for streaming data due to their ability to adapt model complexity with the observed data. However, very limited work has addressed posterior inference in a streaming fashion, and most of the ...
Yang Yang, Bo Chen, Hongwei Liu
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Underground Pipeline Mapping Based on Dirichlet Process Mixture Model
IEEE Access, 2020 Underground pipeline mapping is important in urban construction. There are few specific procedures and approaches to map underground pipelines using ground penetration radar (GPR) without knowing the number of buried pipelines.
Qingyuan Wu, Xiren Zhou, Huanhuan Chen
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