Markov Chain Monte Carlo Data Association for Merge and Split Detection in Tracking Protein Clusters

2006 ◽  
Author(s):  
Quan Wen ◽  
Jean Gao ◽  
K. Luby-Phelps
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Data Association ◽  
Monte Carlo Data ◽  
Protein Clusters

scholarly journals A Disease Outbreak Prediction Model Using Bayesian Inference: A Case of Influenza

2019 ◽  
Vol 7 (3) ◽  
pp. 91-98
Author(s):  
Atefeh Sadat Mirarabshahi ◽  
Mehrdad Kargari
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Disease Transmission ◽  
Transmission Rate ◽  
Monte Carlo Algorithm ◽  
Transmission Model ◽  
Monte Carlo Data ◽  
Influenza Outbreak ◽  
High Dependency

Introduction: One major problem in analyzing epidemic data is the lack of data and high dependency among the available data, which is due to the fact that the epidemic process is not directly observable. Methods: One method for epidemic data analysis to estimate the desired epidemic parameters, such as disease transmission rate and recovery rate, is data intensification. In this method, unknown quantities are considered as additional parameters of the model and are extracted using other parameters. The Markov Chain Monte Carlo algorithm is extensively used in this field. Results: The current study presents a Bayesian statistical analysis of influenza outbreak data using Markov Chain Monte Carlo data intensification that is independent of probability approximation and provides a wider range of results than previous studies. A method for estimating the epidemic parameters has been presented in a way that the problem of uncertainty regarding the modeling of dynamic biological systems can be solved. The proposed method is then applied to fit an SIR-like flu transmission model to data from 19 years leading up to the seventh week of the 2017 incidence of influenza. Conclusion: The proposed method showed an improvement in estimating the values of all the parameters considered in the study. The results of this study showed that the distributions are significant and the error ranges are real.

scholarly journals Biomass Hydrothermal Carbonization: Markov-Chain Monte Carlo Data Analysis and Modeling

2021 ◽  
Vol 3 ◽  
Author(s):  
Alberto Gallifuoco ◽  
Alessandro Antonio Papa ◽  
Luca Taglieri
Keyword(s):  
Experimental Data ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Biomass Conversion ◽  
Hydrothermal Carbonization ◽  
Statistical Simulation ◽  
Reaction Scheme ◽  
Least Square ◽  
Monte Carlo Data

This paper introduces Bayesian statistical methods for studying the kinetics of biomass hydrothermal carbonization. Two simple, specially developed computer programs implement Markov-chain Monte Carlo methods to illustrate these techniques' potential, long since established in other areas of chemical reaction engineering. A range of experimental data, both from this study and the literature, test the soundness of a Bayesian approach to modeling biomass hydrothermal carbonization kinetics. The first program carries out parameter estimations and performs better or equal than the traditional deterministic methods (R2 as high as 0.9998). For three out of the 22 datasets, the program detected the global minima of the parameter space, while the deterministic least-square found local values. The second program uses Gillespie's algorithm for the statistical simulation of the reactions occurring in hydrothermal carbonization. Comparing six basic kinetic models with literature data tested the stochastic simulation as a tool for assessing biomass conversion reaction networks rapidly. Among the simple models discussed, reaction scheme 3 fitted better to the experimental data (R2 > 0.999). The proposed approach is worth extending to more complex, time-consuming computer models and could support other techniques for studying hydrothermal conversions.

Sign in / Sign up

Export Citation Format

Share Document