#
PřF:MAIBDA An Introduction to Bayesian Da - Course Information

## MAIBDA An Introduction to Bayesian Data Analysis

**Faculty of Science**

spring 2018

**Extent and Intensity**- 10/0. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: z (credit).
**Teacher(s)**- prof. Pablo Emilio Verde (lecturer), prof. RNDr. Ivanka Horová, CSc. (deputy)
**Supervisor**- prof. RNDr. Ivanka Horová, CSc.

Department of Mathematics and Statistics - Departments - Faculty of Science

Supplier department: Department of Mathematics and Statistics - Departments - Faculty of Science **Prerequisites**- To attend the course, participants need to have a good background in classical statistics and a working knowledge of the statistical software R.
**Course Enrolment Limitations**- The course is offered to students of any study field.

The capacity limit for the course is 24 student(s).

Current registration and enrolment status: enrolled:**6**/24, only registered:**0**/24, only registered with preference (fields directly associated with the programme):**0**/24 **Course objectives**- This course is for data analysts and students who are familiar with classical statistics and they want to get a working knowledge in Bayesian data analysis.
**Learning outcomes**- This course is for data analysts and students who are familiar with classical statistics and they want to get a working knowledge in Bayesian data analysis.
**Syllabus**- Introduction to Bayesian inference
- Bayesian statistical of simple statistical models
- Using R and OpenBUGS/JAGS for simple models
- Introduction to Bayesian computations (MCMC, Gibb sampling, Metropolis sampling, etc.)
- The role of prior distributions in Bayesian inference
- Bayesian analysis of regression models (linear regression and generalized liner models)
- Introduction to Bayesian computations (MCMC, Gibb sampling, Metropolis sampling, etc.)
- Bayesian analysis of multivariate models
- Introduction to Hierarchical Modeling
- Longitudinal data analysis
- Bayesian analysis of special models: mixtures of distribution, non-parametric models and survival-data.

**Literature**- he BUGS Book: A Practical Introduction to Bayesian Analysis. (2013) CRC Press.
- Bayesian Data Analysis (Third Edition). (2014) Gelman et al. CRC Press.

**Teaching methods**- The course presentation is practical with many worked examples. Emphasis to the complementary aspects of Bayesian Statistics to Classical Statistics rather than one vs. the other.
**Language of instruction**- English
**Further Comments**- Study Materials

The course is taught only once.

The course is taught: in blocks.

- Enrolment Statistics (recent)

- Permalink: https://is.muni.cz/course/sci/spring2018/MAIBDA