Results 31 to 40 of about 157,640 (328)
Hierarchical Bayesian space-time interpolation versus spatio-temporal BME approach [PDF]
Advances in Geosciences, 2010 The restrictions of the analysis of natural processes which are observed at any point in space or time to a purely spatial or purely temporal domain may cause loss of information and larger prediction errors.
I. Hussain, J. Pilz, G. Spoeck
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Hierarchic Bayesian models for kernel learning [PDF]
Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005 The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance when compared to that obtained from any single data source. We present a Bayesian hierarchical model which enables kernel learning and present effective variational Bayes ...
Mark A. Girolami, Simon Rogers
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Bayesian hierarchical modeling of size spectra
Methods in Ecology and Evolution, 2023 Abstract A fundamental pattern in ecology is that smaller organisms are more abundant than larger organisms. This pattern is known as the individual size distribution (ISD), which is the frequency of all individual body sizes in an ecosystem.
Jeff S. Wesner +3 more
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Hierarchical Bayesian modeling of intertemporal choice [PDF]
Judgment and Decision Making, 2017 There is a growing interest in studying individual differences in choices that involve trading off reward amount and delay to delivery because such choices have been linked to involvement in risky behaviors, such as substance abuse.
Melisa E. Chávez +3 more
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Bayesian Hierarchical Models for Counterfactual Estimation
CoRR, 2023 Counterfactual explanations utilize feature perturbations to analyze the outcome of an original decision and recommend an actionable recourse. We argue that it is beneficial to provide several alternative explanations rather than a single point solution and propose a probabilistic paradigm to estimate a diverse set of counterfactuals.
Natraj Raman +2 more
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Bayesian designs for hierarchical linear models [PDF]
Statistica Sinica, 2009 Summary: Two Bayesian optimal design criteria for hierarchical linear models are discussed: the \(\psi_\beta\) criterion for the estimation of individual-level parameters \(\beta\), and the \(\psi_\theta\) criterion for the estimation of hyperparameters \(\mathbf \theta\).
Liu, Qing +2 more
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Nested Hierarchical Dirichlet Processes [PDF]
, 2014 We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP is a generalization of the nested Chinese restaurant process (nCRP) that allows each word to follow its own path to a topic node according to a document ...
Blei, David M. +3 more
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Bayesian hierarchical model for protein identifications [PDF]
Journal of Applied Statistics, 2018 In proteomics, identification of proteins from complex mixtures of proteins extracted from biological samples is an important problem. Among the experimental technologies, mass spectrometry (MS) is the most popular one. Protein identification from MS data typically relies on a ‘two-step’ procedure of identifying the peptide first followed by the ...
Riten, Mitra +3 more
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Towards a mathematical theory of cortical micro-circuits. [PDF]
PLoS Computational Biology, 2009 The theoretical setting of hierarchical Bayesian inference is gaining acceptance as a framework for understanding cortical computation. In this paper, we describe how Bayesian belief propagation in a spatio-temporal hierarchical model, called ...
Dileep George, Jeff Hawkins
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Modeling and visualizing uncertainty in gene expression clusters using Dirichlet process mixtures [PDF]
, 2009 Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (
De la Cruz, Bernard J. +3 more
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