Knowledge Organisation and Representation|Public Policy and Administration

Knowledge &amp; Governance builds upon the Term 1 MPA courses, exploring some of the concepts in more depth as well as opening up in breadth to include...

Knowledge & Governance builds upon the Term 1 MPA courses, exploring some of the concepts in more depth as well as opening up in breadth to include governance in its wider sense, beyond national government, and in international settings. This module is intended to be as practical as possible, highlighting theoretical concepts and frameworks that will enable you to think about issues that arise at the interface between knowledge and governance.

The first part of the module will focus on knowledge and its value-ladenness nature, look at the various influences that end up affecting it and some of the hurdles it may face in conditions of uncertainty or limited capacity to generate, process and act upon knowledge. The second part of the module will explore in more detail the larger governance structures in which knowledge finds its way, both at the national and international level, where it is incorporated and negotiated. An international, multi-stakeholder negotiation simulation will be held in the last part of the course to further cement all previous concepts through experiential learning.

This module is designed to encourage experiential and reflective learning. Classes will generally follow the same format of a relatively interactive lecture presenting concepts and frameworks that will then be applied to particular topics or case studies, in the form of discussions, debates, role-play and other forms of experiential approaches.

Aims - This module aims to introduce the Bayesian approach to statistical inference, to develop relevant theory, methodology and computational techniques for...

Aims - This module aims to introduce the Bayesian approach to statistical inference, to develop relevant theory, methodology and computational techniques for its implementation and to develop basic skills in use of the WinBUGS software for Bayesian modelling. It is primarily intended for students registered on the Masters degree programmes offered by the Department of Statistical Science, or jointly with other departments.

Objectives - On successful completion of the module, a student should be able to give an account of the underlying principles of Bayesian inference, and contrast these with those of other schools of inference; manipulate probability formulae to derive posterior and predictive distributions; perform conjugate prior-to-posterior analysis for simple binomial, Poisson and normal models; analyse these and more complex normal models, using priors representing great prior uncertainty; use hierarchical and graphical modelling to represent and analyse complex systems; describe and implement Gibbs sampling methods for estimating posterior quantities; and use WinBUGS software to estimate complex Bayesian models.

Applications - Bayesian methods are currently gaining increasing popularity, largely because advances in computing facilities and in modern simulation-based Markov Chain Monte Carlo (MCMC) methods provide a means of analysing the complex data structures that arise in application areas as diverse as artificial intelligence, biology, genetics and environmental science. This module focuses on fundamental concepts and techniques, and introduces the computational tools needed to apply Bayesian methods in challenging research-level problems. 

Content - Introduction to Bayesian statistics. Bayesian inference. Prior distributions. Graphical models. Hierarchical models. Markov chain Monte Carlo (MCMC: Gibbs sampling). WinBUGS software.

Key texts - Available from ReadingLists@UCL.