Results 11 to 20 of about 31,307,485 (221)
The Dependent Dirichlet Process and Related Models [PDF]
Statistical Science, 2020 Standard regression approaches assume that some finite number of the response distribution characteristics, such as location and scale, change as a (parametric or nonparametric) function of predictors.
F. Quintana +3 more
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Risk Evaluation of Banking Index with Volatility Estimation through Stochastic Volatility Model: A Semiparametric Bayesian Approach [PDF]
تحقیقات مالی, 2017 Estimation of the return distribution has a crucial role in Risk measurement and since the precision of risk measures depends on the precision of the return distribution, truly estimation of return distribution has attracted a huge attention.
Rasoul Sajjad, Zahra Abtahi
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Observations on K-Image Expansion of Image-Mixing Augmentation
IEEE Access, 2023 Image-mixing augmentations (e.g., Mixup and CutMix), which typically involve mix ing two images, have become the de-facto training techniques for image classification.
Joonhyun Jeong +5 more
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On the Convergence of Dirichlet Processes [PDF]
Bernoulli, 1999 \(X\) is called a Dirichlet process if there exist processes \(M, A\) such that \(X=M+A\) where \(M\) is a local martingale and \(A\) is an adapted process of 0-quadratic variation along some sequence \((D_k)_k\) of partitions of \([0,T]\) with \(\max_{t_j \in D_k} |t_{j+1}-t_j|\longrightarrow 0\) as \(k \rightarrow \infty\), i.e. \(\sum_{t_j \in D_k} |
François Coquet +3 more
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Direct numerical solutions of the SIR and SEIR models via the Dirichlet series approach
PLoS ONE, 2023 Compartment models are implemented to understand the dynamic of a system. To analyze the models, a numerical tool is required. This manuscript presents an alternative numerical tool for the SIR and SEIR models.
Kiattisak Prathom, Asama Jampeepan
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Hierarchical Dirichlet Processes [PDF]
Journal of the American Statistical Association, 2006 We consider problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data.
Teh, Yee Whye +3 more
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CoRR, 2020 Biclustering is a class of techniques that simultaneously clusters the rows and columns of a matrix to sort heterogeneous data into homogeneous blocks. Although many algorithms have been proposed to find biclusters, existing methods suffer from the pre-specification of the number of biclusters or place constraints on the model structure.
Ngo, Michelle N +3 more
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The local Dirichlet process [PDF]
Annals of the Institute of Statistical Mathematics, 2009 As a generalization of the Dirichlet process (DP) to allow predictor dependence, we propose a local Dirichlet process (lDP). The lDP provides a prior distribution for a collection of random probability measures indexed by predictors. This is accomplished by assigning stick-breaking weights and atoms to random locations in a predictor space.
Chung, YS Chung, Yeonseung +1 more
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IEEE Access, 2023 To investigate the advancements of artificial intelligence techniques in the realm of library and information subject, we have chosen the Latent Dirichlet Allocation method as a case study to explore its current study status and implementations ...
Xinzhou Pan, Yu Xu
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Hierarchical Dirichlet scaling process [PDF]
Machine Learning, 2017 We present the \textit{hierarchical Dirichlet scaling process} (HDSP), a Bayesian nonparametric mixed membership model. The HDSP generalizes the hierarchical Dirichlet process (HDP) to model the correlation structure between metadata in the corpus and mixture components.
Dongwoo Kim 0002, Alice Oh
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