DAY 1: Basic Bayes + NDLM/SVM + particle filtering

Brief overview of key Bayesian ingredients and computation
http://hedibert.org/wp-content/uploads/2020/12/toyexample-normal-normal.html

Model selection/comparison: Bernoulli, logit/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 

Bayes factor and Bayesian model averaging
http://hedibert.org/wp-content/uploads/2021/05/bayesfactor-bayesiammodelaveraging.html

Normal dynamic linear modeling (NDLM)
http://hedibert.org/wp-content/uploads/2013/12/dm.pdf

Stochastic volatility model
http://hedibert.org/wp-content/uploads/2019/05/stochasticvolatilitymodels.pdf

Particle filtering
http://hedibert.org/wp-content/uploads/2019/05/SMC-purefilter.pdf



DAY 2: Research topics

Variable selection and regularization
http://hedibert.org/wp-content/uploads/2018/05/multiplelinearregression.pdf

The illusion of the illusion of sparsity
http://hedibert.org/wp-content/uploads/2020/09/TheIllusionoftheIllusionofSparsity-slides.pdf

Dynamic sparsity in dynamic regressions
http://hedibert.org/wp-content/uploads/2020/02/talk-ASU-Feb2020.pdf

Dynamic ordering learning in multivariate forecasting 
http://hedibert.org/wp-content/uploads/2021/06/levy-lopes-DynamicOrderingLearning-slides-june2021.pdf