Distribution functions of probabilistic automata
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Real PCF extended with integration
LICS '96 Proceedings of the 11th Annual IEEE Symposium on Logic in Computer Science
The Scott Topology Induces the Weak Topology
LICS '96 Proceedings of the 11th Annual IEEE Symposium on Logic in Computer Science
Approximating Labeled Markov Processes
LICS '00 Proceedings of the 15th Annual IEEE Symposium on Logic in Computer Science
Approximating labelled Markov processes
Information and Computation
Riemann and Edalat integration on domains
Theoretical Computer Science - Topology in computer science
Mathematical Structures in Computer Science
Domains, integration and ‘positive analysis’
Mathematical Structures in Computer Science
A testing scenario for probabilistic processes
Journal of the ACM (JACM)
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We establish domain-theoretic models of finite-state discrete stochastic processes, Markov processes and vector recurrent iterated function systems. In each case, we show that the distribution of the stochastic process is canonically obtained as the least upper bound of an increasing chain of simple valuations in a probabilistic power domain associated to the process. This leads to various formulas and algorithms to compute the expected values of functions which are continuous almost everywhere with respect to the distribution of the stochastic process. We also prove the existence and uniqueness of the invariant distribution of a vector recurrent iterated function system which is used in fractal image compression, and present a finite algorithm to decode the image.