AbstractIn [12] it is shown that the probabilistic powerdomain of a continuous domain is again continuous. The category of continuous domains, however, is not cartesian closed, and one has to look at subcategories such as RB, the retracts of bifinite domains. [8] offers a proof that the probabilistic powerdomain construction can be restricted to RB.In this paper, we give a counterexample to Graham's proof and describe our own attempts at proving a closure result for the probabilistic powerdomain construction. We have positive results for finite trees and finite reversed trees. These illustrate the difficulties we face, rather than being a satisfying answer to the question of whether the probabilistic powerdomain and function spaces can be r...
AbstractIn this paper, we consider strongly bounded linear operators on a finite dimensional probabi...
abstract of talkProbabilistic logic programs combine the power of a programming language with a poss...
AbstractWe present applications of domain theory in stochastic learning automata and in neural nets....
AbstractIn [12] it is shown that the probabilistic powerdomain of a continuous domain is again conti...
AbstractIn this paper we consider Milner's calculus CCS enriched by a probabilistic choice operator....
AbstractWe provide a domain-theoretic framework for possibility theory by studying possibility measu...
Recently, there has been a lot of attention for statistical relational learning and probabilistic pr...
AbstractIn this paper we investigate the Plotkin powerdomain under order-theoretical aspects. We ans...
We consider stochastic programming problems with probabilistic constraints involving integer-valued ...
AbstractWe consider a generalisation of Larsen and Skou's [19] reactive probabilistic transition sys...
Invited talk.Recently, there has been a lot of attention for statistical relational learning and pro...
Invited talk.A multitude of different probabilistic programming languages exists today, all extendin...
We consider stochastic programming problems with probabilistic constraints involving random variable...
AbstractWe present domain-theoretic models that support both probabilistic and nondeterministic choi...
abstract of talkProbabilistic programs combine the power of programming languages with that of proba...
AbstractIn this paper, we consider strongly bounded linear operators on a finite dimensional probabi...
abstract of talkProbabilistic logic programs combine the power of a programming language with a poss...
AbstractWe present applications of domain theory in stochastic learning automata and in neural nets....
AbstractIn [12] it is shown that the probabilistic powerdomain of a continuous domain is again conti...
AbstractIn this paper we consider Milner's calculus CCS enriched by a probabilistic choice operator....
AbstractWe provide a domain-theoretic framework for possibility theory by studying possibility measu...
Recently, there has been a lot of attention for statistical relational learning and probabilistic pr...
AbstractIn this paper we investigate the Plotkin powerdomain under order-theoretical aspects. We ans...
We consider stochastic programming problems with probabilistic constraints involving integer-valued ...
AbstractWe consider a generalisation of Larsen and Skou's [19] reactive probabilistic transition sys...
Invited talk.Recently, there has been a lot of attention for statistical relational learning and pro...
Invited talk.A multitude of different probabilistic programming languages exists today, all extendin...
We consider stochastic programming problems with probabilistic constraints involving random variable...
AbstractWe present domain-theoretic models that support both probabilistic and nondeterministic choi...
abstract of talkProbabilistic programs combine the power of programming languages with that of proba...
AbstractIn this paper, we consider strongly bounded linear operators on a finite dimensional probabi...
abstract of talkProbabilistic logic programs combine the power of a programming language with a poss...
AbstractWe present applications of domain theory in stochastic learning automata and in neural nets....