# Graphical Probability Models for Inference and Decison Making

Students are required to do a project applying what they have learned in this course to a problem of their own choosing.  The most straightforward type of project is a modeling exercise.  Consider a problem related to your work or dissertation topic in which uncertainty plays an important role.  Construct a model for the problem using Hugin, Netica, UnBBayes, or another graphical modeling package (see links on the course web site).
• You may apply the knowledge engineering process described in class to do the knowledge engineering for your model.  Even if you are your own expert, make sure you document the elements of your model, the rationale for your design decisions,  the process by which you evaluated and refined your model, and your overall evaluation of the results of modeling.
• You may use data to learn the parameters and/or structure of your model.  Netica's "learn from cases" capability is a very basic parameter learning method.  You can use more sophisticated approaches in which you estimate a parameterized model for the conditional distribution of a node given its parents.  The Weka package has several methods for learning structure and parameters of graphical models from data.
• You can try relational modeling with a relational package such as UNBBayes, Tuffy or Alchemy.

Deliverables for the research project:

• Project proposal: Due March 21.  This should be no more than 3 single spaced pages (it can be less).  The purpose is to get feedback on your idea to help you produce a better project.  Here is a sample proposal outline :
• Introduction: Describe the problem.  Why is the problem important?  What role does uncertainty play?  Why is it important to incorporate uncertainty?  Give a brief synopsis (few sentences) of your project and how it addresses these issues.
• Background:  You should review literature relevant to your problem and to the role uncertainty plays.  What has been done by others?  What are the open issues?  How does your work relate to what has been done previously?
• Research plan:  Describe what you plan to do.
• Final report:  Due 11:59 PM May 16. Here is a sample outline for the final report:
• Introduction: Describe the problem.  Why is the problem important?  What role does uncertainty play?  Why is it important to incorporate uncertainty?  Give a brief synopsis (few sentences) of your project and how it addresses these issues.
• Background:  You should do a survey of the literature relevant to your problem and to the role uncertainty plays.  What has been done by others?  What are the open issues?  How does your work relate to what has been done previously?
• Description of your project.  If you built a belief network model, you should include a graphic showing your network structure, justify the conditional dependence and independence assumptions you are making, and describe qualitatively the probability assessments you made and the reasons for them.  Describe the test cases you ran, and discuss the results.  Are they what you expected?  Why or why not?
• Evaluation:  What did you learn from your project?  Was it a success?  Were your results satisfactory or unsatisfactory?  Why?  What would you change if you had it to do over?
• Summary and Conclusions.
• Appendices:  You should include full conditional probability tables for your networks, as well as full documentation of test cases you ran (values of input variables and probability distributions for output variables).  Include printout of source and documentation for any code you developed for your project.
• Softcopy:  Please provide softcopy of code, models, and reports you produced for this project.