Results 71 to 80 of about 31,307,485 (221)
Nonparametric Click Modeling Using Dirichlet Process Mixture Model for Information Retrieval
IEEE AccessClick models are essential for comprehending user search behavior and enhancing ranking algorithms; nevertheless, current methodologies face challenges due to the variability of user interaction patterns across different query settings.
K. J. Amala, D. Rajeswari
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bspmma: An R Package for Bayesian Semiparametric Models for Meta-Analysis
Journal of Statistical Software, 2012 We introduce an R package, bspmma, which implements a Dirichlet-based random effects model specific to meta-analysis. In meta-analysis, when combining effect estimates from several heterogeneous studies, it is common to use a random-effects model.
Deborah Burr
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Small Area Estimation under Poisson–Dirichlet Process Mixture Models
AxiomsIn this paper, we propose an improved Nested Error Regression model in which the random effects for each area are given a prior distribution using the Poisson–Dirichlet Process. Based on this model, we mainly investigate the construction of the parameter
Xiang Qiu +3 more
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Machinery Early Fault Detection Based on Dirichlet Process Mixture Model
IEEE Access, 2019 The most commonly used single feature-based anomaly detection method for the complex machinery, such as large wind power equipment, steam turbine generator sets, and reciprocating compressors, exhibits a defect of low-alarm accuracy due to the non ...
Bo Ma +4 more
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Regularity of Gaussian Processes on Dirichlet Spaces [PDF]
Constructive Approximation, 2018 We are interested in the regularity of centered Gaussian processes (Z_x), x in M indexed by compact metric spaces M. It is shown that the almost everywhere Besov space regularity of such a process is (almost) equivalent to the Besov regularity of the covariance K(x,y) = E(Z_xZ_y) under the assumption that (i) there is an underlying Dirichlet structure ...
Kerkyacharian, Gerard +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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Distributional properties of means of random probability measures [PDF]
The present paper provides a review of the results concerning distributional properties of means of random probability measures. Our interest in this topic has originated from inferential problems in Bayesian Nonparametrics.
Antonio Lijoi, Igor Prünster
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Clustering and unconstrained ordination with Dirichlet process mixture models
Methods in Ecology and EvolutionAssessment of similarity in species composition or abundance across sampled locations is a common goal in multi‐species monitoring programs. Existing ordination techniques provide a framework for clustering sample locations based on species composition ...
Christian Stratton +5 more
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Clustering gene expression time series data using an infinite Gaussian process mixture model. [PDF]
PLoS Computational Biology, 2018 Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present
Ian C McDowell +5 more
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Markoff process and the Dirichlet problem
Proceedings of the Japan Academy, Series A, Mathematical Sciences, 1945 Not ...
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