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Bayesian Methods [PDF]

SSRN Electronic Journal, 2011
Chapter written for the Handbook of Research Methods and Applications on Empirical Macroeconomics, edited by Nigar Hashimzade and Michael Thornton, forth- coming in 2012 (Edward Elgar Publishing). This chapter presents an introductory review of Bayesian methods for research in empirical macroeconomics.
Bauwens, L, Korobilis, D
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On Bayesian Methods for Bioequivalence

Biometrics, 1984
Bayesian methods are presented for assessing bioequivalence for studies in which a new formulation and a standard are administered simultaneously, and for Latin square designs which compare two or more new formulations to a standard. Two examples illustrate the application of the methods.
Selwyn, Murray R., Hall, Nancy R.
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Bayesian and Non-Bayesian Methods of Inference

Annals of Internal Medicine, 1983
Excerpt Statistics is an indispensable tool in clinical research. Disagreements over the use of various approaches such as those reflected in the letters-to-the-editor section of this issue (1,2) s...
R D, Small, S S, Schor
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Bayesian methods for proteomics

PROTEOMICS, 2007
Abstract Biological and medical data have been growing exponentially over the past several years [1, 2]. In particular, proteomics has seen automation dramatically change the rate at which data are generated [3]. Analysis that systemically incorporates prior information is becoming essential to making inferences about the myriad ...
Gil, Alterovitz, Jonathan, Liu, Ehsan, Afkhami, Marco F, Ramoni   +3 more
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Basic Bayesian Methods

2007
In this chapter, we introduce the basics of Bayesian data analysis. The key ingredients to a Bayesian analysis are the likelihood function, which reflects information about the parameters contained in the data, and the prior distribution, which quantifies what is known about the parameters before observing data.
Mark E, Glickman, David A, van Dyk
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Variational Bayesian Method for Retinex

IEEE Transactions on Image Processing, 2014
In this paper, we propose a variational Bayesian method for Retinex to simulate and interpret how the human visual system perceives color. To construct a hierarchical Bayesian model, we use the Gibbs distributions as prior distributions for the reflectance and the illumination, and the gamma distributions for the model parameters.
Liqian Wang, Liang Xiao 0001, Hongyi Liu 0001, Zhihui Wei   +3 more
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Computational Interaction with Bayesian Methods

Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, 2019
This course introduces computational methods in human--computer interaction. Computational interaction methods use computational thinking---abstraction, automation, and analysis---to explain and enhance interaction. This course introduces the theory of practice of computational interaction by teaching Bayesian methods for interaction across four wide ...
Per Ola Kristensson, Nikola Banovic 0001, Antti Oulasvirta, John Williamson 0001   +3 more
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A Bayesian Method for Historgrams

Biometrika, 1973
SUMMARY This paper describes a Bayesian procedure for the simultaneous estimation of the proba- bilities in a histogram. A two-stage prior distribution is constructed which assumes that probabilities corresponding to adjacent intervals are likely to be closely related.
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Bayesian Econometric Methods

2007
This volume in the Econometric Exercises series contains questions and answers to provide students with useful practice, as they attempt to master Bayesian econometrics. In addition to many theoretical exercises, this book contains exercises designed to develop the computational tools used in modern Bayesian econometrics.
Koop, G.M., Poirier, D., Tobias, J.
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