scholarly journals Investigation of diffusion process inside the cell using Monte Carlo Cell (MCell)

2016 ◽  
Vol 694 ◽  
pp. 012077
Author(s):  
A Sutresno ◽  
F Haryanto ◽  
S Viridi ◽  
I Arif
Keyword(s):  
Monte Carlo ◽  
Diffusion Process

scholarly journals Convergence of Conditional Metropolis-Hastings Samplers

2014 ◽  
Vol 46 (2) ◽  
pp. 422-445 ◽  
Author(s):  
Galin L. Jones ◽  
Gareth O. Roberts ◽  
Jeffrey S. Rosenthal
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Diffusion Process ◽  
Sample Path ◽  
Discrete Observations ◽  
Entire Sample ◽  
Bayesian Inferences ◽  
Monte Carlo Algorithms

We consider Markov chain Monte Carlo algorithms which combine Gibbs updates with Metropolis-Hastings updates, resulting in a conditional Metropolis-Hastings sampler (CMH sampler). We develop conditions under which the CMH sampler will be geometrically or uniformly ergodic. We illustrate our results by analysing a CMH sampler used for drawing Bayesian inferences about the entire sample path of a diffusion process, based only upon discrete observations.

KINETIC ROUGHENING AND INTERACTING REACTING SPECIES: NONLINEAR ASPECTS

Fractals ◽  
1993 ◽  
Vol 01 (03) ◽  
pp. 470-474 ◽  
Author(s):  
I.M. SOKOLOV ◽  
P. ARGYRAKIS ◽  
A. BLUMEN
Keyword(s):  
Monte Carlo ◽  
Theoretical Analysis ◽  
Diffusion Process ◽  
Monte Carlo Simulations ◽  
Short Range ◽  
Reaction Diffusion ◽  
Kinetic Roughening ◽  
Constant Rate ◽  
Good Agreement

We consider the A+B→0 reaction, in which particles interact through short-range forces. The analysis leads to expressions akin in form to those which describe kinetic roughening. In a situation in which particles are generated with a constant rate j0, their concentration n(t) grows as [Formula: see text] in d=1. Here the theoretical analysis predicts γ=1/5 and β=2/5, in very good agreement with direct Monte-Carlo simulations of the reaction-diffusion process.

Monte Carlo Simulation of Diffusion Process on Quasi-Lattice

1992 ◽  
Vol 31 (Part 1, No. 5A) ◽  
pp. 1417-1423 ◽  
Author(s):  
Yasushi Sasajima ◽  
Kazuhiko Sakayori ◽  
Minoru Ichimura ◽  
Mamoru Imabayashi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Diffusion Process

scholarly journals Convergence of Conditional Metropolis-Hastings Samplers

2014 ◽  
Vol 46 (02) ◽  
pp. 422-445 ◽  
Author(s):  
Galin L. Jones ◽  
Gareth O. Roberts ◽  
Jeffrey S. Rosenthal
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Diffusion Process ◽  
Sample Path ◽  
Discrete Observations ◽  
Entire Sample ◽  
Bayesian Inferences ◽  
Monte Carlo Algorithms

We consider Markov chain Monte Carlo algorithms which combine Gibbs updates with Metropolis-Hastings updates, resulting in aconditional Metropolis-Hastings sampler(CMH sampler). We develop conditions under which the CMH sampler will be geometrically or uniformly ergodic. We illustrate our results by analysing a CMH sampler used for drawing Bayesian inferences about the entire sample path of a diffusion process, based only upon discrete observations.

scholarly journals Monte Carlo Methods and New Jump Diffusion Processes and Their Application in Gold Price

2017 ◽  
Author(s):  
Kutluk Kağan Sümer
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Standard Deviation ◽  
Diffusion Process ◽  
Wiener Process ◽  
Mean Reversion ◽  
Jump Diffusion ◽  
Gold Market ◽  
Jump Diffusion Process ◽  
Standard Deviations

This study aimed to execute Monte Carlo simulation method with Wiener Process, Generalized Wiener Process, Mean Reversion Process and Mean Reversion Jump Diffusion Process and to compare them and then expended with the idea of how to include negative and positive news shocks in the gold market to the Monte Carlo simulation. By enhancing the determination of the 3 standard deviation shocks within the process of Classic Mean Jump Diffusion Process, an enchanted model for the 1,96 and 3 standard deviation shocks were being used and additionally positive and negative shocks were added to the system in a different way. This new Mean Reversion Jump Diffusion Process that have been developed by Sümer, executes Monte Carlo simulation regarding the gold market return with five random variables that are chosen from Poisson distribution and one random variable chosen from the normal distribution. Additionally, by accepting volatilities as outlies over the 1,96 and 3 standard deviations with the effect of the new and good news and the standard deviations on the traditional approximate return and the standard deviations (volatility) and the obtained new approximate return and the new standard deviation (volatility) and compares them with the Monte Carlo simulations.

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