Results 161 to 170 of about 2,165 (200)

Who benefits? Uncovering hidden heterogeneity of treatment effects in adaptive trials using Bayesian methods: a systematic review. [PDF]

Trials
Giblon R, Gao C, Liu K, Ouyang Y, Cunningham J, Pimienta AL, Goligher EC, Heath A.   +7 more
europepmc   +1 more source

Bayesian Structural Equation Envelope Model. [PDF]

Psychometrika
Wang C, Sun R, Feng X, Song X.
europepmc   +1 more source

A Comparison of Methods for Modeling Multistate Cancer Progression Using Screening Data with Censoring after Intervention. [PDF]

Med Decis Making
Akwiwu EU, Coupé VMH, Berkhof J, Klausch T.   +3 more
europepmc   +1 more source

A Bayesian model of distance perception from ocular convergence. [PDF]

PLoS Comput Biol
Scarfe P, Hibbard PB.
europepmc   +1 more source

Bayesian inference for Laplace distribution based on complete and censored samples with illustrations. [PDF]

J Appl Stat
Sun W, Zhu X, Zhang Z, Balakrishnan N.
europepmc   +1 more source

Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses

Oikos, 2019
Throughout the last two decades, Bayesian statistical methods have proliferated throughout ecology and evolution. Numerous previous references established both philosophical and computational guidelines for implementing Bayesian methods.
Nathan P Lemoine
exaly   +3 more sources

On the invariance of noninformative priors

Annals of Statistics, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gauri Sankar Datta
exaly   +3 more sources

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Some Remarks on Noninformative Priors

Journal of the American Statistical Association, 1995
Abstract This article focuses primarily on a comparison between the reference priors of Berger and Bernardo and the reverse reference priors suggested by J. K. Ghosh. Sufficient conditions are given that provide agreement between the two classes of priors. Several examples are given showing the agreement or disagreement between the two.
Gauri Sankar Datta
exaly   +4 more sources

Noninformative Priors and Nuisance Parameters

Journal of the American Statistical Association, 1993
Abstract We study the conflict between priors that are noninformative for a parameter of interest versus priors that are noninformative for the whole parameter. Our investigation leads us to maximize a functional that has two terms: an asymptotic approximation to a standardized expected Kullback-Leibler distance between the marginal prior and marginal ...
, Larry Wasserman
exaly   +2 more sources