Fit Statistical Models Using Hamiltonian Monte Carlo
Provide users with a framework to learn the intricacies of the Hamiltonian Monte Carlo algorithm with hands-on experience by tuning and fitting their own models. All of the code is written in R. Theoretical references are listed below:.
Neal, Radford (2011) "Handbook of Markov Chain Monte Carlo" ISBN: 978-1420079418,
Betancourt, Michael (2017) "A Conceptual Introduction to Hamiltonian Monte Carlo" ,
Thomas, S., Tu, W. (2020) "Learning Hamiltonian Monte Carlo in R" ,
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013) "Bayesian Data Analysis" ISBN: 978-1439840955,
Agresti, Alan (2015) "Foundations of Linear and Generalized Linear Models ISBN: 978-1118730034,
Pinheiro, J., Bates, D. (2006) "Mixed-effects Models in S and S-Plus" ISBN: 978-1441903174.