This chapter reviews the distinguishing features of parametric and nonparametric models, presents a brief account of measure‐theoretic probability concepts, and explains the notion of exchangeability. Then, it provides guidelines for constructing nonparametric Bayesian models from parametric Bayesian equations, investigates the posterior computability, and presents the notion of algorithmic sufficiency. The reviewed applications of nonparametric Bayesian models include multiple object tracking, probabilistic optimal power flow, and single‐molecule fluorescence microscopy.
Nonparametric Bayesian Models
Nonlinear Filters ; 213-234
2022-04-12
22 pages
Article/Chapter (Book)
Electronic Resource
English
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