Results 11 to 20 of about 11,564,802 (285)
Hierarchical Double Dirichlet Process Mixture of Gaussian Processes
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 We consider an infinite mixture model of Gaussian processes that share mixture components between non-local clusters in data. Meeds and Osindero (2006) use a single Dirichlet process prior to specify a mixture of Gaussian processes using an infinite number of experts.
Aditya Tayal +2 more
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On a Dirichlet Process Mixture Representation of Phase-Type Distributions [PDF]
Bayesian Analysis, 2022 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ayala, Daniel +3 more
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Detecting Spatial Clusters via a Mixture of Dirichlet Processes
Journal of Probability and Statistics, 2018 We proposed an approach that has the ability to detect spatial clusters with skewed or irregular distributions. A mixture of Dirichlet processes (DP) was used to describe spatial distribution patterns.
Meredith A. Ray +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 +5 more sources
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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Memorized Variational Continual Learning for Dirichlet Process Mixtures [PDF]
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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Sampling from Dirichlet process mixture models with unknown concentration parameter: mixing issues in large data implementations. [PDF]
Stat Comput, 2015 We consider the question of Markov chain Monte Carlo sampling from a general stick-breaking Dirichlet process mixture model, with concentration parameter (Formula presented.).
Hastie DI, Liverani S, Richardson S.
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Dirichlet Mixtures, the Dirichlet Process, and the Structure of Protein Space [PDF]
Journal of Computational Biology, 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 ...
Viet-An Nguyen +2 more
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Sequence Alignment with Dirichlet Process Mixtures [PDF]
CoRR, 2018 We present a probabilistic model for unsupervised alignment of high-dimensional time-warped sequences based on the Dirichlet Process Mixture Model (DPMM). We follow the approach introduced in (Kazlauskaite, 2018) of simultaneously representing each data sequence as a composition of a true underlying function and a time-warping, both of which are ...
Kazlauskaite, Ieva +3 more
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Accelerated Variational Dirichlet Process Mixtures [PDF]
, 2007 Dirichlet Process (DP) mixture models are promising candidates for clustering applications where the number of clusters is unknown a priori. Due to computational considerations these models are unfortunately unsuitable for large scale data-mining applications.
Kurihara, K., Welling, M., Vlassis, N.
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