Vector space formulation of probabilistic finite state automata

This paper develops a vector space model of a class of probabilistic finite state automata (PFSA) that are constructed from finite-length symbol sequences. The vector space is constructed over the real field, where the algebraic operations of vector addition and the associated scalar multiplication operations are defined on a probability measure space, and implications of these algebraic operations are interpreted. The zero element of this vector space is semantically equivalent to a PFSA, referred to as symbolic white noise. A norm is introduced on the vector space of PFSA, which provides a measure of the information content. An application example is presented in the framework of pattern recognition for identification of robot motion in a laboratory environment.

► Development of a vector space model of a class of probabilistic finite state automata (PFSA).
► Vector space construction over the real field.
► Generation of probabilities from finite-length data.
► Introduction of symbolic white noise serving as the zero vector.
► Experimental validation of the theoretical results on a laboratory apparatus.