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Bayesian variable selection for genome-wide association study of grain traits in rice. [PDF]

PLoS One
Basu R, Mukhopadhyay S, Adhikari K.
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Bayesian hierarchical models

2000
Publisher Summary This chapter describes the Bayesian hierarchical models. The evaluation of this study, by a Bayesian hierarchical linear model is derived from the data that include the other large clinical trials of thrombolytic therapy and suggests that treatment is also beneficial for patients, arriving much later than six hours after symptom ...
C H, Schmid, E N, Brown
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Bayesian Models

2013
In this chapter, we focus on Bayesian modeling. Information about past hurricanes is available from instruments and written accounts. Written accounts are generally less precise than instrumental observations, which tend to become even more precise as technology advances.
James B. Elsner, Thomas H. Jagger
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Bayesian models of cognition

WIREs Cognitive Science, 2010
AbstractThere has been a recent explosion in research applying Bayesian models to cognitive phenomena. This development has resulted from the realization that across a wide variety of tasks the fundamental problem the cognitive system confronts is coping with uncertainty.
Nick, Chater, Mike, Oaksford, Ulrike, Hahn, Evan, Heit   +3 more
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Bayesian Models

2015
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This book provides a comprehensive and accessible introduction to the latest Bayesian methods.
N. Thompson Hobbs, Mevin B. Hooten
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Bayesian Model Selection and Model Averaging

Journal of Mathematical Psychology, 2000
This paper reviews the Bayesian approach to model selection and model averaging. In this review, I emphasize objective Bayesian methods based on noninformative priors. I will also discuss implementation details, approximations, and relationships to other methods. Copyright 2000 Academic Press.
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Bayesian Semiparametric Proportional Odds Models

Biometrics, 2007
SummaryMethodology for implementing the proportional odds regression model for survival data assuming a mixture of finite Polya trees (MPT) prior on baseline survival is presented. Extensions to frailties and generalized odds rates are discussed. Although all manner of censoring and truncation can be accommodated, we discuss model implementation ...
Hanson, Timothy, Yang, Mingan
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BAYESIAN CHAIN LADDER MODELS

ASTIN Bulletin, 2014
AbstractThe literature on Bayesian chain ladder models is surveyed. Both Mack and cross-classified forms of the chain ladder are considered. Both cases are examined in the context of error terms distributed according to a member of the exponential dispersion family. Tweedie and over-dispersed Poisson errors follow as special cases.
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Bayesian Tensor Regression Models

2018
In this paper we introduce the literature on regression models with tensor variables and present a Bayesian linear model for inference, under the assumption of sparsity of the tensor coefficient. We exploit the CONDECOMP/PARAFAC (CP) representation for the tensor of coefficients in order to reduce the number of parameters and adopt a suitable ...
Monica Billio, Roberto Casarin, Matteo Iacopini   +2 more
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Bayesian Model Selection for Heteroskedastic Models

SSRN Electronic Journal, 2008
It is well known that volatility asymmetry exists in financial markets. This paper reviews and investigates recently developed techniques for Bayesian estimation and model selection applied to a large group of modern asymmetric heteroskedastic models.
Chen, Cathy W.S., Gerlach, Richard H., So, Mike K.P.   +2 more
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