Authors
Thomas Icard
Stanford University
Abstract
A probabilistic Chomsky–Schützenberger hierarchy of grammars is introduced and studied, with the aim of understanding the expressive power of generative models. We offer characterizations of the distributions definable at each level of the hierarchy, including probabilistic regular, context-free, (linear) indexed, context-sensitive, and unrestricted grammars, each corresponding to familiar probabilistic machine classes. Special attention is given to distributions on (unary notations for) positive integers. Unlike in the classical case where the "semi-linear" languages all collapse into the regular languages, using analytic tools adapted from the classical setting we show there is no collapse in the probabilistic hierarchy: more distributions become definable at each level. We also address related issues such as closure under probabilistic conditioning.
Keywords Probabilistic computation  Chomsky hierarchy  Generative models
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References found in this work BETA

Aspects of the Theory of Syntax.Noam Chomsky - 1965 - Cambridge, MA, USA: MIT Press.
Computing Machinery and Intelligence.Alan M. Turing - 1950 - Mind 59 (October):433-60.
Psychological Predicates.Hilary Putnam - 1967 - In W. H. Capitan & D. D. Merrill (eds.), Art, Mind, and Religion. University of Pittsburgh Press. pp. 37--48.

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