Relevant Readings

I will put readings here that are relevant to the lectures.

Hierarchical (linear) models

• BDA3 Chapters 2,5,12,14,15
• Data Analysis Using Regression and Multilevel/Hierarchical Models
• Ridge regression in practice
• Bayesian LASSO
• Zellner’s g-prior
• Mixtures of g priors for Bayesian variable selection
• MCMC using Hamiltonian dynamics
• Handling sparsity via the horseshoe
• The horseshoe estimator for sparse signals
• O’Hara and Sillanpaa (2009) A review of Bayesian variable selection methods: what, how and which
• Bayesian model averaging: A tutorial

State-space models

• Dynamic Linear Models with R
• Bayesian Forecasting and Dynamic Models
• State Space and Unobserved Component Models
• Sequential Monte Carlo Methods in Practice

Spatial

• Hierarchical Modeling and Analysis for Spatial Data (Ch. 1-3)
• BDA3 Chapter 21

Bayesian nonparametrics

• BDA3 Chapters 22,23
• Bayesian Nonparametric Inference – Why and How
• Markov Chain Sampling Methods for Dirichlet Process Mixture Models
• A Bayesian Analysis of Some Nonparametric Problems