Results 31 to 40 of about 11,564,802 (285)
Dirichlet process mixture models for unsupervised clustering of symptoms in Parkinson's disease [PDF]
, 2012 In this paper, the goal of identifying disease subgroups based on differences in observed symptom profile is considered. Commonly referred to as phenotype identification, solutions to this task often involve the application of unsupervised clustering ...
Johnson, Helen +2 more
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Frontiers in Signal Processing, 2022 We consider the problem of a primary source tracking a moving object under time-varying and unknown noise conditions. We propose two methods that integrate sequential Bayesian filtering with transfer learning to improve tracking performance.
Omar Alotaibi +1 more
doaj +1 more source
Frontiers in Energy Research, 2023 Growing server energy consumption is a significant environmental issue, and mitigating it is a key technological challenge. Application-level energy minimization strategies depend on accurate modeling of energy consumption during an application’s ...
Buhua Chen +4 more
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Dirichlet process mixture models for regression discontinuity designs
Statistical Methods in Medical Research, 2022 The regression discontinuity design is a quasi-experimental design that estimates the causal effect of a treatment when its assignment is defined by a threshold for a continuous variable. The regression discontinuity design assumes that subjects with measurements within a bandwidth around the threshold belong to a common population, so that the ...
Federico Ricciardi +2 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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Clustering consistency with Dirichlet process mixtures
Biometrika, 2022 SummaryDirichlet 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, and more specifically focus on consistency for the unknown number of clusters when the observed ...
Ascolani, Filippo +3 more
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Temporal Analysis of Motif Mixtures Using Dirichlet Processes [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014 In this paper, we present a new model for unsupervised discovery of recurrent temporal patterns (or motifs) in time series (or documents). The model is designed to handle the difficult case of multivariate time series obtained from a mixture of activities, that is, our observations are caused by the superposition of multiple phenomena occurring ...
Rémi Emonet +2 more
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Generative Supervised Classification Using Dirichlet Process Priors. [PDF]
, 2010 Choosing the appropriate parameter prior distributions associated to a given Bayesian model is a challenging problem. Conjugate priors can be selected for simplicity motivations.
Davy, Manuel +3 more
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Remote Sensing, 2021 In this paper, we propose a Dirichlet process (DP) mixture model of Gamma distributions, which is an extension of the finite Gamma mixture model to the infinite case.
Ahmed Almulihi +5 more
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Bayesian Inference for Dynamic Models with Dirichlet Process Mixtures [PDF]
2006 9th International Conference on Information Fusion, 2006 Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian state-space models. We address here the case where the noise probability density functions are of unknown functional form. A flexible Bayesian nonparametric noise model based on mixture of Dirichlet processes is introduced.
Caron, F +5 more
openaire +4 more sources