Abductive learning of quantized stochastic processes with probabilistic finite automata
نویسندگان
چکیده
منابع مشابه
Abductive learning of quantized stochastic processes with probabilistic finite automata.
We present an unsupervised learning algorithm (GenESeSS) to infer the causal structure of quantized stochastic processes, defined as stochastic dynamical systems evolving over discrete time, and producing quantized observations. Assuming ergodicity and stationarity, GenESeSS infers probabilistic finite state automata models from a sufficiently long observed trace. Our approach is abductive; att...
متن کاملLearning Probabilistic 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...
متن کاملLearning 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...
متن کاملLearning Probabilistic Residual Finite State Automata
We introduce a new class of probabilistic automata: Probabilistic Residual Finite State Automata. We show that this class can be characterized by a simple intrinsic property of the stochastic languages they generate (the set of residual languages is finitely generated) and that it admits canonical minimal forms. We prove that there are more languages generated by PRFA than by Probabilistic Dete...
متن کاملLearning 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...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
سال: 2013
ISSN: 1364-503X,1471-2962
DOI: 10.1098/rsta.2011.0543