The aim of the course is for students to gain experience and understanding of Bayesian statistics, simulation based statistical inference and how to implement simulation based Bayesian inference in practice on a computer.
has knowledge of the Bayesian principle including (conjugate) priors
has knowledge of algorithms used for Bayesian inference such as e.g. the Gibbs sampler and the Metropolis-Hastings algorithm
has knowledge of the theory of Markov chain Monte Carlo methods such as e.g. irreducibility, aperiodicity and invariant densities
has knowledge of practical challenges when using simulation based inference such as e.g. tuning, acceptance rates and burn-in
can apply the relevant methodologies from the course to conduct a Bayesian analysis of a given data set
can state the underlying assumptions and argue about limitations and extensibility of the chosen methodologies
can implement a relevant algorithm from the course to conduct simulation based Bayesian inference
The teaching is organised in accordance with the general form of teaching in the curriculum, re. § 17.
This is a 5 ECTS project module and the work load is expected to be 150 hours for the student.
|Name of exam||Bayesian Statistics, Simulation and Software|
|Type of exam|
Active participation/continuous evaluationRe-examination: Oral exam based on submitted assignment.
|Type of grading||Internal examination|
|Criteria of assessment||The criteria of assessment are stated in the Examination Policies and Procedures|
|Danish title||Bayesiansk statistik, simulering og software|
|Language of instruction||Danish|
|Location of the lecture||Campus Aalborg|
|Responsible for the module|
|Study Board||Study Board of Mathematical Sciences|
|Department||Department of Mathematical Sciences|
|Faculty||The Faculty of Engineering and Science|