Implementing MCMC sampling from scratch in R for various Bayesian models
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Updated
Dec 7, 2023 - HTML
Implementing MCMC sampling from scratch in R for various Bayesian models
IRT models using various Bayesian methods
Homeworks from the Bayesian Statistics course of accademic year 2018/2019 at University of Trieste
The programming part for the second assignment of the course DSC 531 - Statistical Simulations and Data Analysis of the University of Cyprus MSc in Data Science programme
R program for a metropolis-hastings based MCMC sampler using a multivariate-normal proposal distribution.
In this repository, software applications in simulation and visualization for various applications are presented with interesting examples.
Bayesian logistic regression using Metropolis-Hastings sampling techniques in R
Decrypt a message with the Metropolis-Hasting Algorithm
An implementation of the BUGS example LSAT: item response (http://www.openbugs.net/Examples/Lsat.html) on R. Parameters for the Rasch model are estimated using Maximum Marginal Likelihood as well as Bayesian Inference using jags and an implementation of Metropolis on R.
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