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@selcukorkmaz

1/ Bayesian inference is a powerful statistical framework that allows us to estimate the probability distribution of parameters based on data and prior knowledge. And R has a variety of packages for implementing Bayesian analysis! #rstats #datascience #Bayesian

2/ First up, there's 'rstan', which provides R interfaces to the popular Stan modeling language. It includes a suite of powerful algorithms for Bayesian inference, including Markov Chain Monte Carlo (MCMC) sampling and Variational Bayes (VB). #rstats #stan

3/ Another popular R package for Bayesian analysis is 'JAGS', which stands for Just Another Gibbs Sampler. It's an alternative to Stan that uses a different MCMC algorithm, and can be easier to use for some types of models. #rstats #JAGS

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Kath M O'Reilly

@kathmoreilly

New paper: Tooling-up for infectious disease transmission modelling. The Intro to the #Epidemics special issue on Computational Methods for Modelling an Infectious Disease (COMMAND) doi-org.ez.lshtm.ac.uk/10.1016/j.epid… @GrahamMedley @EleanorMRees @N_R_Waterlow @cmmid_lshtm In the series:

@sbfnk

1. Choices and trade-offs in inference with infectious disease models by @sbfnk and A King doi.org/10.1016/j.epid… #IDmodelling #modelchoice

@kathmoreilly

2. Desirable BUGS in models of infectious diseases by @kathmoreilly @drrachellowe and others doi.org/10.1016/j.epid… #JAGS #graphs

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