| Unit name | Theory of Inference |
|---|---|
| Unit code | MATH35610 |
| Credit points | 10 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 2D (weeks 19 - 24) |
| Unit director | Dr. Jonty Rougier |
| Open unit status | Not open |
| Pre-requisites | |
| Co-requisites |
None |
| School/department | School of Mathematics |
| Faculty | Faculty of Science |
Statistical inference is the science concerned with drawing inferences on the basis of uncertain data. In contrast to numerical or graphical descriptive techniques, which have the relatively simple aim of summarising the data actually observed, in inference we intend to draw conclusions about the populations from which the data are drawn. In doing so, almost universally, a probabilistic model is built for the mechanism generating the data, and the specific objects of inference are the unknowns (parameters) appearing in such models. There are several approaches to doing this, and we shall cover the main features of the two most important of these: (a) classical (or frequentist) inference and (b) Bayesian inference.
Additional unit information can be found at http://www.maths.bristol.ac.uk/study/undergrad/current_units/index.html
To gain an understanding of some key principles of statistical inference, and how these impact upon current practice across a range of fields.
Lectures, problems classes, homeworks to be done by students, Office Hours.
2.5 hour written examination
Reading and references are available at http://www.maths.bristol.ac.uk/study/undergrad/current_units/index.html
