scholarly journals The Rise of Markov Chain Monte Carlo Estimation for Psychometric Modeling

2009 ◽  
Vol 2009 ◽  
pp. 1-18 ◽  
Author(s):  
Roy Levy
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Approaches ◽  
Mcmc Estimation ◽  
Powerful Approach ◽  
Psychometric Modeling ◽  
Psychometric Models ◽  
Monte Carlo Estimation

Markov chain Monte Carlo (MCMC) estimation strategies represent a powerful approach to estimation in psychometric models. Popular MCMC samplers and their alignment with Bayesian approaches to modeling are discussed. Key historical and current developments of MCMC are surveyed, emphasizing how MCMC allows the researcher to overcome the limitations of other estimation paradigms, facilitates the estimation of models that might otherwise be intractable, and frees the researcher from certain possible misconceptions about the models.

scholarly journals Markov chain Monte Carlo estimation of quantiles

2014 ◽  
Vol 8 (2) ◽  
pp. 2448-2478 ◽  
Author(s):  
Charles R. Doss ◽  
James M. Flegal ◽  
Galin L. Jones ◽  
Ronald C. Neath
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Estimation

scholarly journals Bayesian adaptive Markov chain Monte Carlo estimation of genetic parameters

Heredity ◽  
2012 ◽  
Vol 109 (4) ◽  
pp. 235-245 ◽  
Author(s):  
B Mathew ◽  
A M Bauer ◽  
P Koistinen ◽  
T C Reetz ◽  
J Léon ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Genetic Parameters ◽  
Monte Carlo Estimation ◽  
Estimation Of Genetic Parameters

scholarly journals Estimasi Parameter Distribusi Mixture Eksponensial dan Weibull dengan Metode Bayesian Markov Chain Monte Carlo

2019 ◽  
Vol 1 (1) ◽  
pp. 34
Author(s):  
Ulfa Destiarina ◽  
Mustika Hadijati ◽  
Desy Komalasari ◽  
Nurul Fitriyani
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Estimation Method ◽  
Mixture Distribution ◽  
Integration Process ◽  
Simulation Data ◽  
Mcmc Estimation

In parameter estimation, sometimes there are several problems that require the completion of a mixture distribution. This study aimed to apply the parameter estimation of exponential and Weibull mixture distribution in simulation data using the Bayesian Markov Chain Monte Carlo (MCMC) estimation method. The results obtained indicate that the analytic calculations of parameter estimation were more accurate than the calculations with the help of software, based on the terms of the suitability of the theory and its integration process.

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