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The class of joint densities uniquelly determined by conditionals used in Gibbs sampling

100%

Kopciuszewski P.

Demonstratio Mathematica

1999

tom Vol. 32, nr 4

847-860

This work is about uniqueness of joint densities, where the conditionals are given. We formulate a necessary and sufficient condition for uniqueness of joint densities, at the same time answering the question risen by Arnold and Press [1] as well as Chan [4]. The theorem is illustrated in a few examples and discussed in context of Gibbs sampling method.

Analysis of single gene multitrait effects in livestock by the use of Gibbs sampling

88%

Dobek A. , Molinski K. , Szydlowski M. , Szwaczkowski T.

tom 41

nr 4

Bayesian estimation of dominance merits in noninbred populations by using Gibbs sampling with two reduced sets of mixed model equations

88%

Chalh A. , Gazzah M. E.

Journal of Applied Genetics

2004

tom 45

nr 3

Bayesian Analysis for Hybrid MSF–SBEKK Models of Multivariate Volatility

75%

Osiewalski J. , Pajor A.

Central European Journal of Economic Modelling and Econometrics

2009

nr 2

Bayesian Inference and Gibbs Sampling in Generalized True Random-Effects Models

75%

Makieła K.

Central European Journal of Economic Modelling and Econometrics

2017

nr 1

On Sensitivity of Inference in Bayesian MSF-MGARCH Models

75%

Osiewalski J. , Pajor A.

Central European Journal of Economic Modelling and Econometrics

2019

nr 3

Simulation study on the application of Gibbs sampling for major gene detection in a population of laying hens

63%

Szydlowski M. , Szwaczkowski T.

nr 4

A method for the detection of segregating major genes based on the analysis of estimated marginal posterior major gene variance density was examined. The properties of the method were investigated using data sets simulated for a real population of laying hens consisting of eleven generations. Marginal posterior densities of model parameters were estimated by the Gibbs sampling approach proposed by Janss et al. (1995). With the data of about 4000 observations it was possible to detect a major gene responsible for one third of the genetic variance and one tenth of the phenotypic variance, irrespectively of the degree of dominance at the major locus. The inference based on the posterior marginal major gene variance can be sensitive to skewness of the data. It was shown that skewness of 0.2 can lead to a false detection of a major gene. The method is robust against a non-genetic mixture of normal distributions.