Learning Probability Distributions Generated by Finite-State Machines [chapter]

Jorge Castro, Ricard Gavaldà
2016 Topics in Grammatical Inference  
We review methods for inference of probability distributions generated by probabilistic automata and related models for sequence generation. We focus on methods that can be proved to learn in the inference in the limit and PAC formal models. The methods we review are state merging and state splitting methods for probabilistic deterministic automata and the recently developed spectral method for nondeterministic probabilistic automata. In both cases, we derive them from a high-level algorithm
more » ... cribed in terms of the Hankel matrix of the distribution to be learned, given as an oracle, and then describing how to adapt that algorithm to account for the error introduced by a finite sample.
doi:10.1007/978-3-662-48395-4_5 fatcat:u4cepbpghjcv7ct6zoqrgir2cy