1. komponenta

Lecture type	Total
Lectures	45

* Load is given in academic hour (1 academic hour = 45 minutes)

COURSE AIMS AND OBJECTIVES:
The goal of the course is to introduce the basics of Bayesian inference in statistics.

COURSE DESCRIPTION AND SYLLABUS:
1. Bayesian inference for discrete random variables
2. Bayesian inference for the proportion of binomial distribution
3. Comparing Bayesian and frequentist inferences for the proportion of binomial distribution
4. Bayesian inference for Poisson distribution
5. Bayesian inference for mean
6. Comparing Bayesian and frequentist inference for mean
7. Bayesian inference for difference between means
8. Bayesian inference for simple linear regression
9. Bayesian inference for standard deviation

1. Introduction to Bayesian Statistics, William M. Bolstad, John Wiley and Sons, 2007.
2. Bayesian Computation with R, Jim Albert, Springer, 2007.
3. A comparison of the Bayesian and frequentist approaches to estimation, Francisco J. Samaniego, Springer, 2010.
4. Bayesian Statistics An introduction, Peter M. Lee, John Wiley and Sons, 2012.

Enrollment :
Attended : Fundamentals of probability theory

Examination :
Passed : Fundamentals of probability theory

Ostali izborni predmeti - Regular study - Computer Science and Mathematics
Vezani kolegiji C - Regular study - Computer Science and Mathematics

Ostali izborni predmeti - Regular study - Computer Science and Mathematics
Vezani kolegiji C - Regular study - Computer Science and Mathematics