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Kernel Analysis Based on Dirichlet Processes Mixture Models [PDF]
Entropy, 2019 Kernels play a crucial role in Gaussian process regression. Analyzing kernels from their spectral domain has attracted extensive attention in recent years. Gaussian mixture models (GMM) are used to model the spectrum of kernels.
Jinkai Tian, Peifeng Yan, Da Huang
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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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Activity Recognition from Daily-Life Sounds Using Unsupervised Learning with Dirichlet Multinomial Mixture Models [PDF]
SensorsTo support ambient assisted living for the elderly living alone, we investigate a method for recognizing daily activities from household sounds. To reduce the cost of building an activity-recognition model, we adopt an unsupervised learning approach ...
Ken Sadohara, Natsuki Miyata
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Dirichlet process mixture models with shrinkage prior
Stat, 2021 We propose Dirichlet process mixture (DPM) models for prediction and cluster‐wise variable selection, based on two choices of shrinkage baseline prior distributions for the linear regression coefficients, namely, the Horseshoe prior and the Normal‐Gamma prior.
Dawei Ding, George Karabatsos
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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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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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Classification of pulsars with Dirichlet process Gaussian mixture model [PDF]
Monthly Notices of the Royal Astronomical Society, 2020 ABSTRACT Young isolated neutron stars (INSs) most commonly manifest themselves as rotationally powered pulsars that involve conventional radio pulsars as well as gamma-ray pulsars and rotating radio transients. Some other young INS families manifest themselves as anomalous X-ray pulsars and soft gamma-ray repeaters that are commonly ...
K Yavuz Ekşi +3 more
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Dirichlet process mixture models with multiple modalities [PDF]
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009 The Dirichlet process can be used as a nonparametric prior for an infinite-dimensional probability mass function on the parameter space of a mixture model. The set of parameters over which it is defined is generally used for a single, parametric distribution.
John W. Paisley, Lawrence Carin
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Econometrics, 2023 In actuarial practice, the modeling of total losses tied to a certain policy is a nontrivial task due to complex distributional features. In the recent literature, the application of the Dirichlet process mixture for insurance loss has been proposed to ...
Minkun Kim +3 more
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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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