TDDE07

TDDE07 - Bayesian Learning | Linköpings university

Lab 1 contains

• Bernoulli distributions: Draw numbers and compute posterior distribution from simulation
• Log-normal distribution
• Gini coefficient
• Credible interval
• Highest Posterior Density

Lab 2 contains

• Bayesian analysis of polynomial regression
• Conjugate priors
• Simulations from joint posterior distribution
• Estimation of polynomial model of higher degree
• Posterior approximation for classification with logistic regression

Lab 3 contains

• Mixture of normal model with semi-conjugate prior
• Gibbs sampling
• Autoregressive models
• Markov chain Monte Carlo

Lab 4 contains

• Markov chain Monte Carlo
• Metropolis algorithm