Stochastic automata and learning systems

Learning Stochastic Finite Automata

Stochastic deterministic finite automata have been introduced and are used in a variety of settings. We report here a number of results concerning the learnability of these finite state machines. In the setting of identification in the limit with probability one, we prove that stochastic deterministic finite automata cannot be identified from only a polynomial quantity of data. If concerned wit...

Learning Stochastic Finite Automata from Experts

We present in this paper a new learning problem called learning distributions from experts. In the case we study the experts are stochastic deterministic finite automata (sdfa). We deal with the situation arising when wanting to learn sdfa from unrepeated examples. This is intended to model the situation where the data is not generated automatically, but in an order dependent of its probability...

Stochastic cellular automata modeling of excitable systems

Tamás Szakály, István Lagzi, Ferenc Izsák ∗and András Volford a Department of Physical Chemistry, Eötvös University, P.O. Box 32, Budapest H-1518, Hungary b Department of Applied Mathematics, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands c Department of Applied Analysis and Computational Mathematics, Eötvös University, P.O. Box 120, Budapest H-1518, Hungary d Department ...

A theory of stochastic systems part I: Stochastic automata

This paper presents the theoretical underpinning of a model for symbolically representing probabilistic transition systems, an extension of labelled transition systems for the modelling of general (discrete as well as continuous or singular) probability spaces. These transition systems are particularly suited for modelling softly timed systems, real-time systems in which the time constraints ar...

Automata Learning and Stochastic Modeling for Biosequence Analysis