Show/hide contentOpenClose All
Curricular information is subject to change
The student will familiarise with the different types of relational data and the related statistical models. The student will be able to manipulate data stored as a network, and to choose and implement appropriate statistical methodologies to analyse these data. The student will be able to summarise relational data using algorithms and models, by implementing them in the programming language R. The student will be able to interpret the results and draw conclusions from them.Indicative Module Content:
Topological properties of networks. Erdos-Renyi random graph. Community detection. Stochastic Block Models. Latent Position Models. Exponential Random Graph Models. Network Autocorrelation Models.
The module has a strong focus on programming with R, and on the computational aspects of the statistical methodologies that are introduced.
|Student Effort Type||Hours|
|Specified Learning Activities||
|Autonomous Student Learning||
Background on statistical inference including probability spaces, likelihood-based inference, regression is essential. Students should be familiar with linear algebra and calculus.Learning Recommendations:
Familiarity with R, or with a computer programming language that is related to data science.
|Resit In||Terminal Exam|
• Group/class feedback, post-assessment
Not yet recorded.