A series of 8 lectures focusing primarily on the use of Dirichlet processes
*****NOTE*****
Classes beging on Wed Oct 26th.
Topics to be covered include:
What is Bayesian nonparametrics, modeling mean functions vs. modeling distributions, mixture models
Basics of Dirichlet processes; early work with Dirichlet processes; Dirichlet processes and Gibbs sampling, basics of inference using Dirichlet processes
MCMC computation with Dirichlet processes
Dirichlet process approximation – partial sum methods; empirical distribution function approaches
Predictive Dirichlet process inference; full Dirichlet process inference
Dependent Dirichlet processes; Clustered/hierarchical Dirichlet processes; Polya trees and dyadic partitions
Nonparametric Bayesian approaches for survival analysis
Nonparametric Bayesian approaches for spatial and spatio-temporal data.
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