Workshop on Bayesian learning & computation

Programa Avançado de Data Science (PADS)

Hedibert Freitas Lopes

Professor of Statistics and Econometrics

Head of the Center of Statistics, Data and Decision Sciences

November 2020

1. Bayesian ingredients and computation

http://hedibert.org/wp-content/uploads/2018/09/modernbayesianstatistics-13-aMostra-part-II.pdf

1.1 Divergent priors, convergent posteriors

https://tiagomendonca.shinyapps.io/bayes/

1.2 Bernoulli iid, logit regression or probit regression?

http://hedibert.org/wp-content/uploads/2020/02/bernoulli-regression.html

http://hedibert.org/wp-content/uploads/2020/02/bernoulli-regression-Rmd.txt

2. Multiple linear regression

http://hedibert.org/wp-content/uploads/2018/05/multiplelinearregression.pdf

2.1 Boston housing data

http://hedibert.org/wp-content/uploads/2020/02/myfirst-gibbssampler-multiplelinearregression.html

3. Time-varying variance modeling

3.1 GARCH modeling

http://hedibert.org/wp-content/uploads/2018/09/modernbayesianstatistics-13-aMostra-part-III-a.pdf

3.2 Stochastic volatility modeling

http://hedibert.org/wp-content/uploads/2015/04/EconometriaAvancada-aula9.pdf

3.3 Factor stochastic volatility modeling

http://hedibert.org/wp-content/uploads/2018/09/modernbayesianstatistics-13-aMostra-part-III-d.pdf