Bayesian Statistics (SL_111)
About Study Course
Objective
The objective of this course is to give the students an overview of key areas of Bayesian Inference. The software package R will be used for computation and case study applications.
Prerequisites
• Familiarity with most common discrete and continuous distributions as well as basic notions of probability.
• Familiarity with basics of statistical inference and Maximum likelihood estimation (MLE).
• Linear models with different types of dependent variables.
• In lab sessions we will learn how to use R, so basic knowledge in R is also required.
Learning outcomes
• Understand the difference between various interpretations of probability.
• Classify and articulate the key components of Bayesian Inference.
• Distinguish the key aspects, and applications, of prior distribution selection and associated considerations.
• Describe the role of the posterior distribution, the likelihood function and the posterior distribution in Bayesian inference about a parameter.
• Interpret statistical simulation-based computational methods.
• Formulate Bayesian solutions to real-data problems, including forming hypotheses, collecting and analysing data, and reaching appropriate conclusions.
• Calculate posterior probabilities using Bayes’ theorem.
• Derive posterior distributions for a given data model and use computational techniques to obtain relevant estimates.
• Operate Bayesian models and provide the technical specifications for such models.
• Apply Bayesian computation using Markov chain Monte Carlo methods using R.
• Assess the Bayesian framework for data analysis and when it can be beneficial, including its flexibility in contrast to the frequentist approach.
• Use independently statistical analyses in practice by using simulation-based computational methods, to present the results and findings orally and in writing.
• Determine the role of the prior distribution in Bayesian inference, and the usage of non-informative priors and conjugate priors.
• Interpret the results of a Bayesian analysis and perform Bayesian model evaluation and assessment.
Study course planning
| Study programme | Study semester | Program level | Study course category | Lecturers | Schedule |
|---|---|---|---|---|---|
| Biostatistics, MFBS | 2 | Master’s | Limited choice | ||
| Biostatistics, MFBSeng | 2 | Master’s | Limited choice |
