107.395 Bayesian Statistics
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2020S, VO, 2.0h, 3.0EC
TUWEL

Properties

  • Semester hours: 2.0
  • Credits: 3.0
  • Type: VO Lecture

Learning outcomes

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

 

 

 

Subject of course

Bayesian principle, prior and posterior distributions, conjugate models, parametric and non-prametric Bayes procedures, decision theory, asymptotics, MCMC methods, information, multivariate Bayes procedures

Teaching methods

Exercises for creating such learning models, Software creation and implementation, demonstration of optimality statements

Mode of examination

Oral

Additional information

 VORBESPRECHUNG/BEGINN:  3.03.2020, SEM 107, 13:00

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Tue13:00 - 15:4503.03.2020 - 10.03.2020Sem.R. DA grün 06A Bayes
Bayesian Statistics - Single appointments
DayDateTimeLocationDescription
Tue03.03.202013:00 - 15:45Sem.R. DA grün 06A Bayes
Tue10.03.202013:00 - 15:45Sem.R. DA grün 06A Bayes

Examination modalities

Presentation of solutions of the exercises, oral exam in the form of a lecture colloquium

Course registration

Not necessary

Curricula

Literature

Lecture notes for this course are available. Price: 10.0

Miscellaneous

Language

German