An Introduction to Probabilistic Automata

By Marielle Stoelinga. Bull. EATCS, 2002

547 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/9956f29b45ec60463511cb24bafdb2380e6b1aad

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Citations in the corpus

A bibliographical study of grammatical inference (2005, background): “Stochastic automata Stochastic finite state machines have been introduced more than 30 years ago [107,108].”
Maximum Entropy Markov Models for Information Extraction and Segmentation (2000, background): “That is, the model is in the form of probabilistic finite-state acceptor (Paz, 1971), in which ¦ § £ ©£ " ¤ 3 is the probability of the transition from state £ " ¤ to state £ on input .”
Rule Extraction from Recurrent Neural Networks: ATaxonomy and Review (2005, background): “These extracted machines were however not equivalent to the stochastic machines as deﬁned by Paz (1971) and Rabin (1963) in that the conditional probability of the output given the state transition was not included in the model, i.e. the extracted machines did not model the output of the RNN.”
Recurrent Neural Networks as Weighted Language Recognizers (2017, background): “Highest Weighted String for General RNN (MPS) Given: RNN R, Probability p ∈ (0, 1) Return: Is the strict cut-point (Paz, 1971) set {s|P (R, s) > p} empty?”
Hidden Markov processes (2002, background): “Finite-state channels are also known as stochastic sequential machines (SSMs) or probabilistic automata [250].”
On graphs with equal algebraic and vertex connectivity (2002, unknown): “[1] (but see also [3,13], and [14, p.”
Probabilistic finite-state machines - part I (2005, background): “These are directly inspired by a number of works in machine learning and pattern recognition , including [1], [4], [13], [45], [46], [47].”
Coefficients of ergodicity and the scrambling index (2009, methodology): “An explicit expression for a coefficient of ergodicity in terms of the entries of the given matrix is the well known Dobrushin or delta coefficient, denoted by τ 1 (· ), which according to Seneta [10], was first introduced by Dobrushin [3] and Paz [6].”
Identifiability of parameters in latent structure models with many observed variables (2008, background): “Thus our approach yields generic versions of the claims of [17] and [38] described above.”
Efficient Training of Conditional Random Fields (2002, methodology): “One of the most common methods for performing such labelling and segmentation tasks is that of employing hidden Markov models [45] (HMMs) or probabilistic finite state automata [40] to identify the most likely sequence of labels for the words in any given sentence.”
On the undecidability of probabilistic planning and related stochastic optimization problems (2003, methodology): “General PFAs are powerful enough to accept even non-context-free languages [41].”