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Download the Paper (366 K, PDF file) - January 1, 2006 Tell a colleague about it. Printing Tips: Select 'print as image' in the Acrobat print dialog if you have trouble printing.

ABSTRACT:
Research in computer science, engineering, mathematics, and statistics has produced a variety of tools that are useful in developing probabilistic models of human cognition. We provide an introduction to the principles of probabilistic inference that are used in the papers appearing in this special issue. We lay out the basic principles that underlie probabilistic models in detail, and then briefly survey some of the tools that can be used in applying these models to human cognition.

SUGGESTED CITATION:
Thomas L. Griffiths and Alan L. Yuille, "Technical Introduction: A Primer on Probabilistic Inference" (January 1, 2006). Department of Statistics, UCLA. Department of Statistics Papers. Paper 2006010103.
http://repositories.cdlib.org/uclastat/papers/2006010103