5 citations · 5 across the 3 of their papers we have counts for
3 papers
stat.CO2021
Generalized Transitional Markov Chain Monte Carlo Sampling Technique for Bayesian Inversion
Han Lu, Mohammad Khalil, Thomas Catanach +5
In the context of Bayesian inversion for scientific and engineering modeling, Markov chain Monte Carlo sampling strategies are the benchmark due to their flexibility and robustness…
stat.CO2020
Bayesian inference of Stochastic reaction networks using Multifidelity Sequential Tempered Markov Chain Monte Carlo
Thomas A. Catanach, Huy D. Vo, Brian Munsky
Stochastic reaction network models are often used to explain and predict the dynamics of gene regulation in single cells. These models usually involve several parameters, such as t…
stat.CO2018★ 5 cited
Bayesian Updating and Uncertainty Quantification using Sequential Tempered MCMC with the Rank-One Modified Metropolis Algorithm
Thomas A. Catanach, James L. Beck
Bayesian methods are critical for quantifying the behaviors of systems. They capture our uncertainty about a system's behavior using probability distributions and update this under…