Introduction to graphical models and Bayesian networks for social scientists
Faculty of Medicine
Imperial College London
St. Mary's Campus, London
8-9 June 2009
Lecturers
Dr Sara Geneletti of the Imperial College department of Epidemiology and Public Health and the BIAS II project funded by the National Centre for Research Methods and Dr Gianluca Baio of University College London department of Epidemiology and Public Health.
Course outline
This 1.5 day course has two components. Day 1 covers the basics of graphical models, also known as causal diagrams, focusing on directed acyclic graphs. Day 2 is a half-day introduction to Bayesian networks and expert systems with Hugin software which used graphical models to inform decision making processes.
Day 1: Diagrams are often used to express belief about the relationships between variables in complex problems. By formulating such diagrams as graphical models and in particular directed acyclic graphs (DAGs) it is possible to formalise these diagrams and use them for statistical and causal inference. Also, by representing complex problems using DAGs it is possible to build a bridge between researchers in different fields.
Day 2: We cover the basics of Bayesian inference, with specific reference to social sciences and its relationships and possible applications with the technology of Probabilistic Expert Systems (Bayesian Networks). The main general issues are discussed in a tutorial form with worked examples in Hugin, one of the main computer softwares used in the field.
This course is aimed at introducing graphical modelling concepts to social scientists with a basic understanding of statistical methods (e.g. hypothesis testing, regression).
Tentative schedule:
Day 1:
10:30-11:00 Registration + Coffee
11:00-12:30 Lecture: Basic definitions
12:30-13:15 Lunch
13:15-14:45 Lecture: Drawing DAGs
14:45-15:00 Coffee break
15:00-16:15 Lecture: Confounding and other bias
16:15-17:00 Lecture: Overview of software and discussion
Day 2:
9.30-10.30 Lecture: Introduction to Bayesian inference and the use of Probabilistic Expert Systems
10.30-11.00 Coffee Break
11.00-12.00 Exercise/Tutorial with examples in Hugin
12.00-12.30 Conclusions/Discussion
Location
The course will take place in the Division of Epidemiology, Public Health and Primary Care of the Faculty of Medicine and the participants will be able to use these facilities. Note that this is in the Faculty of Medicine, Imperial College London at St. Mary's Campus, and NOT in the main campus in South Kensington.
The complete address is
Faculty of Medicine
Imperial College London
St. Mary's Campus, Norfolk Place
W2 1PG London - UK
More information on how to arrive can be found at http://www1.imperial.ac.uk/medicine/contacts/campuses/stmarys/
By Car
St Mary's is situated outside of the congestion charge zone but please note that there are no College parking spaces available.
By Underground/Rail
Paddington tube stations (Bakerloo, District, Circle and Hammersmith/City line) are two minutes walk from St Marys Campus Paddington Railway Station is also a two minute walk away.
By Bus
Buses 7, 15, 23, 27 and 36 stop directly outside St Mary's campus.
Booking and course fees
£25 per day for postgraduate students registered at UK academic institutions
£50 per day for staff at UK academic institutions, ESRC funded researchers and registered charity organisations
£195 per day for all other participants
This includes the course materials, refreshment and lunch.
https://www5.imperial.ac.uk/medicine/coursebookings/graphmodelsbayesiannetworks_0906.html
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