[Announcements] [Timetable] [Lecture schedule with links to corresponding online material] [Practicals] [Textbooks] [gPy and the algorithm library] [Assessment] [Acknowledgements] [Other useful links]
| Type | Day | Time | Place | Duration |
|---|---|---|---|---|
| Lecture | Tuesday | 0915-1015 | LMB/031 | Weeks: 02-10 |
| Lecture | Tuesday | 1515-1615 | RCH/Lakehouse | Weeks: 02-10 |
| Practical | Friday | 0915-1115 | CSE/069 | Weeks: 03-10 |
| Surgery | Tuesday | 1015-1115 | Room 326, The Hub | Weeks: 02-10 |
| No. | Title | Materials | Day | Cussens | Cowell et al |
|---|---|---|---|---|---|
| 01 | Introduction | HERE | 02/Tue/0915 | ||
| 02 | Python: basics | HERE | 02/Tue/1515 | ||
| 03 | Python: object-orientation | HERE | 03/Tue/0915 | ||
| 04 | Data and probabilities | HERE | 03/Tue/1515 | 3.1 - 3.2 | 2.5 - 2.8 |
| 05 | Factors | HERE | 04/Tue/0915 | 4.1 - 4.5 | 6.2 |
| 06 | Conditional independence in factored distributions | HERE | 04/Tue/1515 | 5.1 - 5.3 | 4.1, 5.1, 5.2 |
| 07 | Variable elimination | HERE | 05/Tue/0915 | 4.6 - 4.10, 5.4 | 4.8 - 4.10 |
| 08 | Bayesian nets | HERE | 05/Tue/1515 | 7.1 | 5.3 |
| 09 | Decomposable models and join forests | HERE | 06/Tue/0915 | 6.2 | 4.2 - 4.4 |
| 10 | Probability propagation in join forests | HERE | 06/Tue/1515 | 6.1, 6.3 | 6.1 - 6.3 |
| 11 | 07/Tue/0915 | ||||
| 12 | Rejection and importance sampling | HERE | 07/Tue/1515 | 8.1 - 8.5 | |
| 13 | Gibbs sampling | HERE | 08/Tue/0915 | 8.6 | Appendix B |
| 14 | Bayesian parameter estimation | HERE | 08/Tue/1515 | 9.1 - 9.4 | |
| 16 | Dynamic graphical models | HERE | 09/Tue/0915 | ||
| 17 | Structure learning | HERE | 10/Tue/0915 | Chapter 11 | |
| 18 | Spare/revision | 10/Tue/1515 |
| No. | Title | Day |
|---|---|---|
| 01 | Python programming | 3/Fri |
| 02 | Factored distributions, graphs and hypergraphs | 4/Fri |
| 03 | Knowledge engineering with Bayesian nets | 5/Fri |
| 04 | Graphs and hypergraphs | 6/Fri |
| 05 | Join trees and sampling | 7/Fri |
| 06 | Gibbs sampling with BUGS | 8/Fri |
| 07 | Parameter estimation | 9/Fri |
| 08 | Machine learning | 9/Fri |
An accompanying textbook Algorithms for Graphical Models is available. Feel free to send me comments on it.
A useful (complete!) textbook is Probabilistic Networks and Expert Systems by Cowell, Dawid, Lauritzen and Spiegelhalter. There are 5 copies in the library available under a variety of loan schemes.
gPy is a collection of Python modules (a
Python package) which
support the teaching of this module. To be able to use them in
your Python programs it suffices to set the
environmental variable
PYTHONPATH to /usr/course/agm/gPy. If
you use the (default) bash shell you can do this by adding the
following line to your .profile in your home directory.
export PYTHONPATH=/usr/course/agm/gPy
The environmental variable PYTHONPATH will then be set
to the right value each time you log in. Typing source
.profile will set it immediately.
If you use tcsh add
setenv PYTHONPATH /usr/course/agm/gPy
to your .tcshrc file.
There is API documentation for gPy.
In the table below, the links for each algorithm take you to its top-level description. Clicking on "source code" in the top right-hand corner of this description takes you to the source which may be annotated. Clicking on highlighted names in the source will take you to the top-level description of the object in question (usually a method).
| Algorithm | Author(s) | Annotated | Lecture | Comments |
|---|---|---|---|---|
| Factor marginalisation | TODO | 05 | Low-level, gPy-specific | |
| Applying a binary operation (e.g. multiplication) between two factors | TODO | 05 | Low-level, gPy-specific | |
| Finding the cliques of a graph | Bron & Kerbosch | Partially | ||
| Gibbs sampling | 13 | |||
| Importance sampling | 12 | |||
| Moralisation | 08 | |||
| Triangulation with a given elimination ordering | 09 | |||
| Iterative proportional fitting | 15 | |||
| Join forest model calibration | Lauritzen & Spiegelhalter | 10 | ||
| Join forest construction from an arbitrary hypergraph and an elimination ordering | TODO | 10 | ||
| Join forest construction from a decomposable hypergraph using vertex elimination | Graham; Beeri et al | YES | 09 | |
| Finding a perfect sequence of cliques in a join forest | TODO | 10 | ||
| Rejection sampling | 12 | |||
| Maximum cardinality search for graphs | Tarjan & Yannakakis | YES | 09 | |
| Restricted maximum cardinality search on hypergraphs | Tarjan & Yannakakis | NO | 09 | |
| Variable elimination | 07 | |||
| Constructing the essential graph of a digraph | Chickering | TODO |
Last modified: Tue Dec 6 16:58:55 GMT 2011