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Bayesian approximations in randomized response model
(Elsevier B.V., 1997-06-05)
Practical Bayesian inference depends upon detailed examination of posterior distribution. When the prior and likelihood are conjugate, this is easily carried out; however, in general, one must resort to numerical approximation. ...
Bayesian approximations in randomized response model
(Elsevier B.V., 1997-06-05)
Practical Bayesian inference depends upon detailed examination of posterior distribution. When the prior and likelihood are conjugate, this is easily carried out; however, in general, one must resort to numerical approximation. ...
The FGM bivariate lifetime copula model: a bayesian approach
(2011)
In this paper, we propose a bivariate distribution for the bivariate survival times based on Farlie-Gumbel-Morgenstern copula to model the dependence on a bivariate survival data. The proposed model allows for the presence ...
Bayesian network classifiers: Beyond classification accuracy
(IOS PRESS, 2011)
This work proposes and discusses an approach for inducing Bayesian classifiers aimed at balancing the tradeoff between the precise probability estimates produced by time consuming unrestricted Bayesian networks and the ...
Bayesian analysis improves experimental studies about temporal patterning of aggression in fish
(2017-12-01)
This study aims to describe a Bayesian Hierarchical Linear Model (HLM) approach for longitudinal designs in fish's experimental aggressive behavior studies as an alternative to classical methods In particular, we discuss ...
Gaining acceptability for the Bayesian decision-theoretic approach in dose-escalation studies
(John Wiley & Sons Inc, 2005-07)
There has recently been increasing demand for better designs to conduct first-into-man dose-escalation studies more efficiently, more accurately and more quickly. The authors look into the Bayesian decision-theoretic ...
Bayesian endogeneity bias modeling
(Elsevier, 2013-11)
We propose to model endogeneity bias using prior distributions of moment conditions. The estimator can be obtained both as a method-of-moments estimator and in a Ridge penalized regression framework. We show the estimator's ...