Is the most likely model likely to be the correct model?

In this work, I test the hypothesis that the 2-dimensional dependencies of a deterministic model can be correctly recovered via hypothesis-enumeration and Bayesian selection for a linear sequence, and what the degree of 'ignorance' or 'uncertainty' is that Bayesian selection can tolerate concerning the properties of the model and data. The experiment tests the data created by a number of rules of size 3 and compares the implied dependency map to the (correct) dependencies of the various generating rules, then extends it to a composition of 2 rules of total size 5. I found that 'causal' belief networks do not map directly to the dependencies of actual causal structures. For deterministic rules satisfying the condition of multiple involvement (two tails), the correct model is not likely to be retrieved without augmenting the model selection with a prior high enough to suggest that the desired dependency model is already known - simply restricting the class of models to trees, and placing other restrictions (such as ordering) is not sufficient. Second, the identified-model to correct-model map is not 1 to 1 - in the rule cases where the correct model is identified, the identified model could just as easily have been produced by a different rule. Third, I discovered that uncertainty concerning identification of observations directly resulted in the loss of existing information and made model selection the product of pure chance (such as the last observation). How to read and identify observations had to be agreed upon a-priori by both the rule and the learner to have any consistency in model identification.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2009

Author dc:creator

• Yankama, Beracah

Advisor dc:contributor.advisor

• Robert C. Berwick and Whitman A. Richards.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/54654

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/54654