447-6273-00L  Bayes Methods

SemesterAutumn Semester 2020
LecturersY.‑L. Grize
Periodicitytwo-yearly recurring course
Language of instructionGerman
CommentSpecial Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to Link. The Registrar's Office will then register you for the course.



Courses

NumberTitleHoursLecturers
447-6273-00 GBayes-Methoden Special students and auditors need a special permission from the lecturers.
Blockkurs. Weitere Informationen unter Link
09.11.20
16.11.20
23.11.20
30.11.20
07.12.20
14.12.20
Leistungskontrolle am 11.01.2021
21s hrs
Mon/214:15-16:00HG E 1.2 »
16:15-18:00HG E 5 »
Y.‑L. Grize

Catalogue data

Abstractconditional probability; bayes inference (conjugate distributions, HPD-areas; linear and empirical bayes); determination of the a-posteriori distribution through simulation (MCMC with R2Winbugs); introduction to multilevel/hierarchical models.
Objective
ContentBayes statistics is attractive, because it allows to make decisions under uncertainty where a classical frequentist statistical approach fails. The course provides an introduction into bayesian methods. It is moderately mathematically technical, but demands a flexibility of mind, which should not underestimated.
LiteratureGelman A., Carlin J.B., Stern H.S. and D.B. Rubin, Bayesian Data Analysis, Chapman and Hall, 2nd Edition, 2004.

Kruschke, J.K., Doing Bayesian Data Analysis, Elsevier2011.
Prerequisites / NoticePrerequisite:Basic knowledge of statistics; Knowledge of R.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits2 credits
ExaminersY.-L. Grize
Typeungraded semester performance
Language of examinationGerman
RepetitionRepetition possible without re-enrolling for the course unit.

Learning materials

No public learning materials available.
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

General : Special students and auditors need a special permission from the lecturers

Offered in

ProgrammeSectionType
CAS in Applied StatisticsFurther CoursesWInformation
DAS in Applied StatisticsElectivesWInformation
Statistics MasterStatistical and Mathematical CoursesWInformation
Statistics MasterSubject Specific ElectivesWInformation