Bayesian methods (NMST431)

Arnošt Komárek

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Bayesian methods (NMST431)

Summer semester 2022–23

SIS pages of the course:    ENG    CZE

Language of both lectures and all exercise classes is English if and only if there is at least one officially subscribed student who is not enrolled in the Czech study programme.

TIMETABLE

Lectures: Monday 11:00 in K4 and 12:20 in K1   
Exercise class: Monday 14:00 in K1   

Division into "lecture" and "exercise class" is just formal. The style of teaching ("lecture"/"exercise class") will change dynamically over the semester. The teaching part will be finalized on April 24.

MATERIALS

Lecture slides:   PDF   (last change 02/04/2023)
 
R script:    Vaha_lehkych1.R
 
Exercise assignment 1:    PDF  (updated on 03/03/2023)
Exercise 2, R script:    R script  (published on 20/03/2023)
    Data (mastitis)
Exercise assignment 3:    PDF  (published on 27/03/2023)
    Data (toenail)
Exercise assignment 4:    PDF  (published on 16/04/2023)
    Data (Cars2004)

JAGS SOFTWARE

Next to R, we will also use JAGS (Just Another Gibbs Sampler) which can be downloaded (for various platforms) here (latest version seems optimal, read README first...):

Source code (to compile)
DEB for Debian Linux
Package for Ubuntu Linux
RPM based Linux distributions
Mac OS X
Windows bin

User manual to JAGS can be found here. Installation manual (if needed) is here. Examples of JAGS analyzes are here. As a study material, practical exercises from the short course given at UseR conference can be useful. Solutions to the exercises are here.

To be able to call JAGS from R, package runjags available in a standard way from CRAN is needed.

LITERATURE

Hušková, M. (1985).
Bayesovské metody (course notes in Czech).
Praha: Univerzita Karlova v Praze.     PDF
 
Robert, C. P. (2001, 2007).
The Bayesian Choice: From Decision-Theoretic Foundations
to Computational Implementation, Second Edition.
New York: Springer.     Information on the publisher's web
 
Marin, J.-M., Robert, C. P. (2007).
Bayesian Core: A Practical Approach to Computational Bayesian Statistics.
New York: Springer.     Information on the publisher's web
 
Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B., Dunson, D. B. (2014).
Bayesian Data Analysis, Third Edition.
Boca Raton: Chapman & Hall/CRC.     Information on the publisher's web
 
Carlin, B. P., Louis, T. A. (2008).
Bayesian Methods for Data Analysis, Third Edition.
Boca Raton: Chapman & Hall/CRC.     Information on the publisher's web
 
Robert, C. P., Casella, G. (2004).
Monte Carlo Statistical Methods, Second Edition.
New York: Springer.     Information on the publisher's web
 

 

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