scholarly journals Forward Simulation Markov Chain Monte Carlo with Applications to Stochastic Epidemic Models

2014 ◽  
Vol 42 (2) ◽  
pp. 378-396 ◽  
Author(s):  
Peter Neal ◽  
Chien Lin Terry Huang
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Epidemic Models ◽  
Forward Simulation ◽  
Stochastic Epidemic Models ◽  
Stochastic Epidemic

scholarly journals Fitting mechanistic epidemic models to data: A comparison of simple Markov chain Monte Carlo approaches

2018 ◽  
Vol 27 (7) ◽  
pp. 1956-1967 ◽  
Author(s):  
Michael Li ◽  
Jonathan Dushoff ◽  
Benjamin M Bolker
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Infectious Diseases ◽  
Epidemic Models ◽  
Estimation Accuracy ◽  
Observation Error ◽  
Widespread Application ◽  
Discrete State

Simple mechanistic epidemic models are widely used for forecasting and parameter estimation of infectious diseases based on noisy case reporting data. Despite the widespread application of models to emerging infectious diseases, we know little about the comparative performance of standard computational-statistical frameworks in these contexts. Here we build a simple stochastic, discrete-time, discrete-state epidemic model with both process and observation error and use it to characterize the effectiveness of different flavours of Bayesian Markov chain Monte Carlo (MCMC) techniques. We use fits to simulated data, where parameters (and future behaviour) are known, to explore the limitations of different platforms and quantify parameter estimation accuracy, forecasting accuracy, and computational efficiency across combinations of modeling decisions (e.g. discrete vs. continuous latent states, levels of stochasticity) and computational platforms (JAGS, NIMBLE, Stan).

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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