scholarly journals Bayesian estimation of a bivariate copula using the Jeffreys prior

Bernoulli ◽  
2012 ◽  
Vol 18 (2) ◽  
pp. 496-519 ◽  
Author(s):  
Simon Guillotte ◽  
François Perron
Keyword(s):  
Bayesian Estimation ◽  
Jeffreys Prior ◽  
Bivariate Copula

scholarly journals A Bayesian model for binary Markov chains

2004 ◽  
Vol 2004 (8) ◽  
pp. 421-429 ◽  
Author(s):  
Souad Assoudou ◽  
Belkheir Essebbar
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chains ◽  
Bayesian Estimation ◽  
Bayesian Model ◽  
Transition Probabilities ◽  
Simulated Data ◽  
Bayesian Estimator ◽  
Jeffreys Prior ◽  
Data Set

This note is concerned with Bayesian estimation of the transition probabilities of a binary Markov chain observed from heterogeneous individuals. The model is founded on the Jeffreys' prior which allows for transition probabilities to be correlated. The Bayesian estimator is approximated by means of Monte Carlo Markov chain (MCMC) techniques. The performance of the Bayesian estimates is illustrated by analyzing a small simulated data set.

scholarly journals Bayesian Estimation of Two-Parameter Weibull Distribution Using Extension of Jeffreys' Prior Information with Three Loss Functions

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Chris Bambey Guure ◽  
Noor Akma Ibrahim ◽  
Al Omari Mohammed Ahmed
Keyword(s):  
Weibull Distribution ◽  
Bayesian Estimation ◽  
Mean Square Error ◽  
Loss Function ◽  
Prior Information ◽  
Loss Functions ◽  
Bayesian Estimator ◽  
Mean Square ◽  
Jeffreys Prior ◽  
Two Parameter

The Weibull distribution has been observed as one of the most useful distribution, for modelling and analysing lifetime data in engineering, biology, and others. Studies have been done vigorously in the literature to determine the best method in estimating its parameters. Recently, much attention has been given to the Bayesian estimation approach for parameters estimation which is in contention with other estimation methods. In this paper, we examine the performance of maximum likelihood estimator and Bayesian estimator using extension of Jeffreys prior information with three loss functions, namely, the linear exponential loss, general entropy loss, and the square error loss function for estimating the two-parameter Weibull failure time distribution. These methods are compared using mean square error through simulation study with varying sample sizes. The results show that Bayesian estimator using extension of Jeffreys' prior under linear exponential loss function in most cases gives the smallest mean square error and absolute bias for both the scale parameterαand the shape parameterβfor the given values of extension of Jeffreys' prior.

BAYESIAN ESTIMATION OF A TIME DEPENDENT INTERNAL HEAT SOURCE IN A HETEROGENEOUS MEDIA

2016 ◽  
Author(s):  
Carolina Palma Naveira Cotta ◽  
Kelvin Chen ◽  
Christopher Tostado ◽  
Philippe Rollemberg d'Egmont ◽  
Fernando Duda ◽  
...  
Keyword(s):  
Heat Source ◽  
Bayesian Estimation ◽  
Heterogeneous Media ◽  
Internal Heat ◽  
Internal Heat Source ◽  
Time Dependent
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