Content and teaching | Assessment | Availability

Module content and teaching

Principal aims

This module looks at: Bayesian Inference & Modelling in Biostatistics (Fabio Rigat); Statistical Methods for Large Biological Datasets (Julia Brettschneider); Modelling & Inference for Brain Image Data (Tom Nichols).

Principal learning outcomes

By the end of this component, students will have become familiar with the mechanics of Bayesian inference, with the scientific questions inspiring the models illustrated along this component and with the technical issues involved by the construction of such statistical models. Students should alsobe able to support biologists with the design and the analysis of the data generated by genomics experiments and support neuroscientists with the design and the analysis of the data generated by neuroimaging experiments.

Timetabled teaching activities

9 lectures plus one revision class for each module component

Departmental link

Other essential notes

Prerequisite(s): ST111/112 Probability A and B, ST217 Mathematical Statistics A and B, basic computing literacy (R, Matlab,...)

Module assessment

Assessment group Assessment name Percentage
15 CATS (Module code: ST416-15)
B (Examination only) Examination - Main Summer Exam Period (weeks 4-9) 100%

Module availability

This module is available on the following courses:



Optional Core