Results 251 to 260 of about 635,362 (294)

Hierarchical Multilabel Classification with Minimum Bayes Risk

2012 IEEE 12th International Conference on Data Mining, 2012
Hierarchical multilabel classification (HMC) allows an instance to have multiple labels residing in a hierarchy. A popular loss function used in HMC is the H-loss, which penalizes only the first classification mistake along each prediction path. However, the H-loss metric can only be used on tree-structured label hierarchies, but not on DAG hierarchies.
Wei Bi, James T. Kwok
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Hierarchical Bayes Models for Response Time Data

Psychometrika, 2010
Human response time (RT) data are widely used in experimental psychology to evaluate theories of mental processing. Typically, the data constitute the times taken by a subject to react to a succession of stimuli under varying experimental conditions.
Craigmile, Peter F., Peruggia, Mario, Van Zandt, Trisha   +2 more
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Empirical Hierarchical Bayes Estimation

1992
It is well known that the James-Stein estimates of mean values of several populations can be derived as empirical Bayes estimates assuming a common prior distribution for all the mean values. But the superiority of such estimates over the usual unbiased estimates diminishes as the variability of the true mean values between populations increases.
C. G. Khatri, C. Radhakrishna Rao
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Hierarchical Bayes Models for Variability

2011
This chapter discusses the Bayesian framework for expanding common likelihood functions introduced in earlier chapters to include additional variability. This variability can be over time, among sources, etc.
Dana Kelly, Curtis Smith
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A Comparison of Hierarchical Bayes and Empirical Bayes Methods with a Forestry Application

Forest Science, 1992
Abstract Gibbs sampling for generating marginal posterior distributions in Bayesian analysis is introduced to the forestry literature. Hierarchical Bayes and (parametric) empirical Bayes methods are compared theoretically and with a practical example.
Edwin J. Green, William E. Strawderman
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A Hierarchical Bayes Model for Assortment Choice

Journal of Marketing Research, 2000
In this research, the authors merge an established methodology—hierarchical Bayesian modeling—and an existing utility model— Farquhar and Rao's (1976) balance model—to describe individual choices among assortments of multiattributed items. This approach facilitates addressing three managerial questions of direct importance: (1) Which assortment of a ...
Eric T. Bradlow, Vithala R. Rao
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Disproportionate Samples in Hierarchical Bayes CBC Analysis

2007
Empirical surveys frequently make use of conjoint data records, where respondents can be split up into segments of different size. A lack of knowledge how to handle such random samples when using Hierarchical Bayes-regression gave cause to a more detailed observation of the preciseness of estimation results.
Sebastian Fuchs, Manfred Schwaiger
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Hierarchical bayes quality measurement plan

Communications in Statistics - Simulation and Computation, 1998
Quality Measurement Plan (QMP) as developed by Hoadley (1981) is a statistical method for analyzing discrete quality audit data which consist of the expected number of defects given the standard quality. The QMP is based on an empirical Bayes (EB) model of the audit sampling process. Despite its wide publicity, Hoadley's method has often been described
Kannan Natarajan, Malay Ghosh, Tapabrata Maiti   +2 more
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Graphs and stochastic relaxation for hierarchical bayes modelling

Statistics in Medicine, 1992
AbstractThis expository paper describes two useful tools for the statistical analysis of processes that generate repeated measures and longitudinal data. The first tool is a graph for a visual description of dependency structures. The second tool is a stochastic relaxation method (‘Gibbs sampling’) for fitting hierarchical Bayes models.
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A hierarchical Bayes model for multilocation auditing

Journal of the Royal Statistical Society: Series D (The Statistician), 2002
Summary. The paper provides a Bayesian analysis of a practical problem in auditing in which substantial prior information needs to be combined with limited sample data. The specific context of the paper is a multilocation audit in which auditors take a two-stage sample of transactions from different sites within an organization.
David J. Laws, Anthony O'Hagan
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