Bayesian computation via Monte Carlo methods: A brief introduction

VI Congreso Bayesiano de America Latina - VI COBAL

Lima, Peru, Junho 2019

Hedibert Freitas Lopes

Professor of Statistics and Econometrics

Insper Institute of Education and Research

URL: www.hedibert.org

E-mail: hedibertfl@insper.edu.br

Outline of the tutorial

Part 1: Bayesian ingredients

Part 2: Monte Carlo methods

Part 3: Markov Chain MC methods

Part 4: Stochastic volatility models

Part 5: Sequential Monte Carlo (pure filter)

Part 6: Sequential Monte Carlo (parameter learning)

Additional material

Comparing MC integration, SIR and Raoblackwellization

Finite Mixture of Poisson Distributions (Rmarkdown)

Bayesian inference via Gibbs sampler for the multiple linear regression with Gaussian errors and conditionally conjugate, ridge, Laplace and horseshoe priors

Bayesian GARCH Modeling

AR(1) plus noise model: block-move versus single-move

Hidden Markov Model: variance-switching (Rmarkdown)

SV-AR(1): MCMC & SMC