Bayesian Nonparametrics and Semi-parametrics
One of the fastest growing research areas in Bayesian inference is the study of prior probability models for random distributions, also known as nonparametric Bayesian models. While the literature goes back to the 1970s, nonparametric Bayes remained a highly specialized field until the 1990s when new computational methods facilitated the use of such models for actual data analysis. This eventually led to a barrage of new nonparametric Bayesian literature over the last 10 years. In this chapter we highlight some of the current research challenges in nonparametric Bayes.
KeywordsRandom Measure Dirichlet Process Beta Process Hierarchical Dirichlet Process Nonhomogeneous Poisson Process
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