After successful completion of the course, students are able to to create stochastic models, master the standard methods of Bayesian statistics, have knowledge of special fields (such as nonparametric methods in the Bayesian context), the use of the general conditional expectation under a sigma algebra is also in the non-dominated Case dominated, the unrestricted uses of Bayesian models can in theory and Data analysis are introduced.independent creation and implementation of software
Bayesian principle, prior and posterior distributions, conjugate models, parametric and non-prametric Bayes procedures, decision theory, asymptotics, MCMC methods, information, multivariate Bayes procedures
Exercises for creating such learning models, Software creation and implementation, demonstration of optimality statements
Eine Anmeldung bzw. Einschreibung ist in TISS und TUWEL möglich.
Beginn/Vorbesprechung 1.3.22 14h SEM grün 03
