Subjects

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Bayesian biostatistics - MB162C03

Title:	Bayesiánská biostatistika
Czech title:	Bayesiánská biostatistika
Guaranteed by:	Department of Ecology (31-162)
Faculty:	Faculty of Science
Actual:	from 2015
Semester:	winter
E-Credits:	2
Examination process:	winter s.:
Hours per week, examination:	winter s.:0/4, C [DS]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	not taught
Language:	Czech
Note:	enabled for web enrollment

Guarantor:	Mgr. Petr Keil, Ph.D.

Opinion survey results Examination dates Schedule

Annotation -

Last update: RNDr. Veronika Sacherová, Ph.D. (15.04.2013)

The course is an introduction to modern applied Bayesian statistics. Its aim is to show that one can do statistics
outside of the classical frequentist categories, and that statistics can work as a modular kit in which several simple
blocks can be used to analyze problems of any complexity. The course will emphasize the practical adavantages
of Bayesian statistics rather than the theoretical or philosophical ones. We will use mainly ecological examples but
the methods are universally applicable throughout the whole biology. Particularly, participants will learn to specify,
fit and evaluate models in BUGS language (OpenBUGS, JAGS). The course assumes basic knowledge of R (i.e. "I
can launch R, load data into R, I can do a simple regression model" and so on). Basic programming skills will be
advantageous (but not critical). The course will have a form of a 3-4 days of intensive seminar-workshop. The
course can be taught either in Czech or English, depending on the language skills of the participants.

Literature -

Last update: RNDr. Veronika Sacherová, Ph.D. (15.04.2013)

Kéry M. (2010) Introduction to WinBUGS for Ecologists: Bayesian approach to regression, ANOVA, mixed models and related. Academic Press.

McCarthy M.A. (2007) Bayesian Methods for Ecology. Cambridge Univ. Press.

Clark J.S. (2007) Models for Ecological Data. Princeton Univ. Press.

Gelman A., Carlin J.B., Stern H.S. & Rubin D.B. (2004) Bayesian Data Analysis. Chapman & Hall.

Bolker B.M. (2008) Ecological Models and Data in R. Princeton Univ. Press.

OpenBUGS user manual: www.openbugs.info

Requirements to the exam -

Last update: RNDr. Veronika Sacherová, Ph.D. (15.04.2013)

Students will be credited for attendance and for performing a simple Bayesian analysis.

Syllabus -

Last update: RNDr. Veronika Sacherová, Ph.D. (15.04.2013)

1. Basics of Bayesian statistics, likelihood, duality of model and data.

2. Principle of Markov Chain Monte Carlo (MCMC) and basics of OpenBUGS and JAGS.

3. Simple Bayesian models, bestiary of probability distributions and their implementation in BUGS language.

4. Generalized linear models - linear regression, logistic regression, Poisson regression, ANOVA etc., all that in BUGS.

5. Hierarchical (mixed-effect, multilevel) models, random effects vs. fixed effects, latent variables, complex models, informative vs. non-informative priors.

6. Time series analysis, autocorrelation function, density dependence, random walks.

7. Modelling spatial and geographical data, spatial autocorrelation, GeoBUGS module.

8. Model selection, model evaluation, information theory criteria, handling uncertainty, Bayesian credible intervals, prediction intervals.