scholarly journals Markov Chain Monte Carlo Simulation Methods for Structural Reliability Analysis

2017 ◽  
Vol 199 ◽  
pp. 1122-1127 ◽  
Author(s):  
Carsten Proppe
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Reliability Analysis ◽  
Structural Reliability ◽  
Simulation Methods ◽  
Structural Reliability Analysis

On some properties of Markov chain Monte Carlo simulation methods based on the particle filter

2012 ◽  
Vol 171 (2) ◽  
pp. 134-151 ◽  
Author(s):  
Michael K. Pitt ◽  
Ralph dos Santos Silva ◽  
Paolo Giordani ◽  
Robert Kohn
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Particle Filter ◽  
Simulation Methods

Markov Chain Monte Carlo Simulation Methods in Econometrics

1996 ◽  
Vol 12 (3) ◽  
pp. 409-431 ◽  
Author(s):  
Siddhartha Chib ◽  
Edward Greenberg
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Em Algorithm ◽  
Gibbs Sampler ◽  
Model Specification ◽  
Simulation Methods ◽  
Theoretical Literature ◽  
The Em Algorithm

We present several Markov chain Monte Carlo simulation methods that have been widely used in recent years in econometrics and statistics. Among these is the Gibbs sampler, which has been of particular interest to econometricians. Although the paper summarizes some of the relevant theoretical literature, its emphasis is on the presentation and explanation of applications to important models that are studied in econometrics. We include a discussion of some implementation issues, the use of the methods in connection with the EM algorithm, and how the methods can be helpful in model specification questions. Many of the applications of these methods are of particular interest to Bayesians, but we also point out ways in which frequentist statisticians may find the techniques useful.

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