Automated Sensitivity Computations for Bayesian Markov Chain Monte Carlo Inference: A New Approach for Prior Robustness and Convergence Analysis

2019 ◽  
Author(s):  
Liana Jacobi ◽  
Mark Joshi ◽  
Dan Zhu
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Convergence Analysis ◽  
New Approach

On solving stochastic differential equations

2019 ◽  
Vol 25 (2) ◽  
pp. 155-161
Author(s):  
Sergej M. Ermakov ◽  
Anna A. Pogosian
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Integral Equations ◽  
Wide Class ◽  
Monte Carlo Methods ◽  
New Approach ◽  
Difference Approximations

Abstract This paper proposes a new approach to solving Ito stochastic differential equations. It is based on the well-known Monte Carlo methods for solving integral equations (Neumann–Ulam scheme, Markov chain Monte Carlo). The estimates of the solution for a wide class of equations do not have a bias, which distinguishes them from estimates based on difference approximations (Euler, Milstein methods, etc.).

On Markov Chain Monte Carlo Acceleration

1994 ◽  
Author(s):  
Alan E. Gelfand ◽  
Sujit K. Sahu
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo
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