SOLVING STOCHASTIC CHEMICAL KINETICS BY METROPOLIS-HASTINGS SAMPLING

DOI10.11948/2016025zbMath1463.92090arXiv1410.8155OpenAlexW2963666324MaRDI QIDQ5121338

Paul Tranquilli, Adrian Sandu, Azam Mooasvi

Publication date: 14 September 2020

Published in: Journal of Applied Analysis & Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1410.8155

zbMATH Keywords

SSAmetropolis-HastingsCMEtau-leap

Mathematics Subject Classification ID

Classical flows, reactions, etc. in chemistry (92E20) Applications of stochastic analysis (to PDEs, etc.) (60H30)

Related Items (1)

Implementation of variable parameters in the Krylov-based finite state projection for solving the chemical master equation

Uses Software

• MATLAB expm

Cites Work

• Unnamed Item
• Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms
• Bayesian analysis of the logit model and comparison of two Metropolis-Hastings strategies.
• A new look at the chemical master equation
• The Scaling and Squaring Method for the Matrix Exponential Revisited
• Nineteen Dubious Ways to Compute the Exponential of a Matrix, Twenty-Five Years Later
• APPROXIMATE EXPONENTIAL ALGORITHMS TO SOLVE THE CHEMICAL MASTER EQUATION
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications

This page was built for publication: SOLVING STOCHASTIC CHEMICAL KINETICS BY METROPOLIS-HASTINGS SAMPLING