More stable estimation of the STARTS model: A Bayesian approach using Markov chain Monte Carlo techniques.

2018 ◽  
Vol 23 (3) ◽  
pp. 570-593 ◽  
Author(s):  
Oliver Lüdtke ◽  
Alexander Robitzsch ◽  
Jenny Wagner
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Approach ◽  
Monte Carlo Techniques ◽  
Stable Estimation

scholarly journals Typical representatives of free homotopy classes in multi-punctured plane

2019 ◽  
Vol 11 (03) ◽  
pp. 623-659
Author(s):  
Maxim Arnold ◽  
Yuliy Baryshnikov ◽  
Yuriy Mileyko
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Probability Measure ◽  
Homotopy Class ◽  
Piecewise Linear ◽  
Homotopy Classes ◽  
Simple Method ◽  
Monte Carlo Techniques ◽  
Free Homotopy Class

We show that a uniform probability measure supported on a specific set of piecewise linear loops in a nontrivial free homotopy class in a multi-punctured plane is overwhelmingly concentrated around loops of minimal lengths. Our approach is based on extending Mogulskii’s theorem to closed paths, which is a useful result of independent interest. In addition, we show that the above measure can be sampled using standard Markov Chain Monte Carlo techniques, thus providing a simple method for approximating shortest loops.

Bayesian Approach to Study of Unfolded Bifurcation Structure via Markov Chain Monte Carlo Method

2017 ◽  
Vol 14 (3) ◽  
pp. 1661-1666
Author(s):  
EssamO. Abdel-Rahman ◽  
Mahmoud Elmezain ◽  
Zohair.S.A. Malki ◽  
GamalA. Abd-Elmougod
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Method ◽  
Bayesian Approach ◽  
Bifurcation Structure
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