CS

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.
The course is also listed under the following terms Spring 2016.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/spring2018/MAIBDA