scholarly journals Cure fraction models using mixture and non-mixture models

2012 ◽  
Vol 51 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Jorge A. Achcar ◽  
Emílio A. Coelho-Barros ◽  
Josmar Mazucheli
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Mixture Models ◽  
Censored Data ◽  
Life Time ◽  
Time Data ◽  
Data Set ◽  
Cure Fraction ◽  
The Bayesian Approach

ABSTRACT We introduce the Weibull distributions in presence of cure fraction, censored data and covariates. Two models are explored in this paper: mixture and non-mixture models. Inferences for the proposed models are obtained under the Bayesian approach, using standard MCMC (Markov Chain Monte Carlo) methods. An illustration of the proposed methodology is given considering a life- time data set.

scholarly journals Fitting Genetic Models Using Markov Chain Monte Carlo Algorithms With BUGS

2006 ◽  
Vol 9 (3) ◽  
pp. 334-342 ◽  
Author(s):  
Stéphanie M. van den Berg ◽  
Leo Beem ◽  
Dorret I. Boomsma
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Repeated Measures ◽  
Environment Interaction ◽  
Stepping Stones ◽  
Data Set ◽  
Extended Pedigrees ◽  
Measurement Models ◽  
Complex Models

AbstractMaximum likelihood estimation techniques are widely used in twin and family studies, but soon reach computational boundaries when applied to highly complex models (e.g., models including gene-by-environment interaction and gene–environment correlation, item response theory measurement models, repeated measures, longitudinal structures, extended pedigrees). Markov Chain Monte Carlo (MCMC) algorithms are very well suited to fit complex models with hierarchically structured data. This article introduces the key concepts of Bayesian inference and MCMC parameter estimation and provides a number of scripts describing relatively simple models to be estimated by the freely obtainable BUGS software. In addition, inference using BUGS is illustrated using a data set on follicle-stimulating hormone and luteinizing hormone levels with repeated measures. The examples provided can serve as stepping stones for more complicated models, tailored to the specific needs of the individual researcher.

scholarly journals A Study of Perks-II Distribution via Bayesian Paradigm

Pravaha ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 1-17
Author(s):  
A. K. Chaudhary
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Analysis ◽  
Real Data ◽  
Simulation Method ◽  
Mcmc Methods ◽  
Data Set ◽  
Bayesian Paradigm ◽  
Mcmc Simulation

In this paper, the Markov chain Monte Carlo (MCMC) method is used to estimate the parameters of Perks-II distribution based on a complete sample. The procedures are developed to perform full Bayesian analysis of the Perks-II distributions using Markov Chain Monte Carlo (MCMC) simulation method in OpenBUGS, established software for Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. We have obtained the Bayes estimates of the parameters, hazard and reliability functions, and their probability intervals are also presented. We have also discussed the issue of model compatibility for the given data set. A real data set is considered for illustration under gamma sets of priors.PravahaVol. 24, No. 1, 2018,page: 1-17 

scholarly journals Markov Chain Monte Carlo-Based Bayesian Inference for Learning Finite and Infinite Inverted Beta-Liouville Mixture Models

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Sami Bourouis ◽  
Roobaea Alroobaea ◽  
Saeed Rubaiee ◽  
Murad Andejany ◽  
Fahd M. Almansour ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Inference ◽  
Mixture Models

scholarly journals The Bayesian Approach to Radiocarbon Calibration Curve Estimation: The IntCal13, Marine13, and SHCal13 Methodologies

Radiocarbon ◽  
2013 ◽  
Vol 55 (4) ◽  
pp. 1905-1922 ◽  
Author(s):  
M Niu ◽  
T J Heaton ◽  
P G Blackwell ◽  
C E Buck
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Data Structures ◽  
Monte Carlo Sampling ◽  
Curve Estimation ◽  
Tree Ring Data ◽  
Data Source ◽  
The Bayesian Approach ◽  
Radiocarbon Calibration

This article outlines the Bayesian models and methods used to facilitate construction of the 2013 internationally agreed radiocarbon calibration curves known as IntCal13, Marine13, and SHCal13. The models build on those used for the 2004 and 2009 estimates of the curves and, as in 2009, arc implemented using Markov chain Monte Carlo sampling, specifically a Metropolis-within-Gibbs sampler. In addition to the data structures accounted for within the 2004 and 2009 models, the approach outlined here also allows for: the presence of additional uncertainty that the data providers have been unable to quantify; tree-ring data that derive their calendar age from wiggle-matching (in addition to ring counting); varve-counted data that exhibit zero increase in calendar age error between 2 or more consecutive layers; and any data source for which we have dependent calendar age uncertainties.

scholarly journals A Bayesian Analysis of Perks Distribution via Markov Chain Monte Carlo Simulation

2013 ◽  
Vol 14 (1) ◽  
pp. 153-166 ◽  
Author(s):  
Arun Kumar Chaudhary ◽  
Vijay Kumar
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Analysis ◽  
Real Data ◽  
Simulation Method ◽  
Complete Sample ◽  
Data Set ◽  
Mcmc Simulation ◽  
The Given

In this paper the Markov chain Monte Carlo (MCMC) method is used to estimate the parameters of Perks distribution based on a complete sample. The procedures are developed to perform full Bayesian analysis of the Perks distributions using MCMC simulation method in OpenBUGS. We obtained the Bayes estimates of the parameters, hazard and reliability functions, and their probability intervals are also presented. We also discussed the issue of model compatibility for the given data set. A real data set is considered for illustration under gamma sets of priors. Nepal Journal of Science and Technology Vol. 14, No. 1 (2013) 153-166 DOI: http://dx.doi.org/10.3126/njst.v14i1.8936

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