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193,540
Hybrid Probabilistic Programs
 Journal of Logic Programming
, 1997
"... The precise probability of a compound event (e.g. e1 e2 ; e1 e2) depends upon the known relationships (e.g. independence, mutual exclusion, ignorance of any relationship, etc.) between the primitive events that constitute the compound event. To date, most research on probabilistic logic programmin ..."
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Cited by 75 (2 self)
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programming [20, 19, 22, 23, 24] has assumed that we are ignorant of the relationship between primitive events. Likewise, most research in AI (e.g. Bayesian approaches) have assumed that primitive events are independent. In this paper, we propose a hybrid probabilistic logic programming language in which
Embedded probabilistic programming
 In Working conf. on domain specific lang
, 2009
"... Abstract. Two general techniques for implementing a domainspecific language (DSL) with less overhead are the finallytagless embedding of object programs and the directstyle representation of side effects. We use these techniques to build a DSL for probabilistic programming, for expressing countab ..."
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Cited by 33 (3 self)
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Abstract. Two general techniques for implementing a domainspecific language (DSL) with less overhead are the finallytagless embedding of object programs and the directstyle representation of side effects. We use these techniques to build a DSL for probabilistic programming, for expressing
Learning Probabilistic Programs
"... We develop a technique for generalising from data in which models are samplers represented as program text. We establish encouraging empirical results that suggest that Markov chain Monte Carlo probabilistic programming inference techniques coupled with higherorder probabilistic programming langu ..."
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We develop a technique for generalising from data in which models are samplers represented as program text. We establish encouraging empirical results that suggest that Markov chain Monte Carlo probabilistic programming inference techniques coupled with higherorder probabilistic programming
Probabilistic Program Analysis with Martingales
"... We present techniques for the analysis of infinite state probabilistic programs to synthesize probabilistic invariants and prove almostsure termination. Our analysis is based on the notion of (super) martingales from probability theory. First, we define the concept of (super) martingales for loop ..."
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Cited by 1 (1 self)
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We present techniques for the analysis of infinite state probabilistic programs to synthesize probabilistic invariants and prove almostsure termination. Our analysis is based on the notion of (super) martingales from probability theory. First, we define the concept of (super) martingales
On probabilistic program equivalence and refinement
 In Proceedings of CONCUR, volume 3653 of LNCS
, 2005
"... Abstract. We study notions of equivalence and refinement for probabilistic programs formalized in the secondorder fragment of Probabilistic Idealized Algol. Probabilistic programs implement randomized algorithms: a given input yields a probability distribution on the set of possible outputs. Intuit ..."
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Cited by 11 (7 self)
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Abstract. We study notions of equivalence and refinement for probabilistic programs formalized in the secondorder fragment of Probabilistic Idealized Algol. Probabilistic programs implement randomized algorithms: a given input yields a probability distribution on the set of possible outputs
VERIFICATION OF PROBABILISTIC PROGRAMS*
"... Abstract. A general method for proving properties of probabilistic programs is presented, This method generalizes the intermediate assertion method in that it extends a given assertion on the output distribution into an invariant assertion on all intermediate distributions, too. The proof method is ..."
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Abstract. A general method for proving properties of probabilistic programs is presented, This method generalizes the intermediate assertion method in that it extends a given assertion on the output distribution into an invariant assertion on all intermediate distributions, too. The proof method
The Principles and Practice of Probabilistic Programming
"... models, probabilistic programs Probabilities describe degrees of belief, and probabilistic inference describes rational reasoning under uncertainty. It is no wonder, then, that probabilistic models have exploded onto the scene of modern artificial intelligence, cognitive science, and applied statist ..."
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Cited by 4 (0 self)
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models, probabilistic programs Probabilities describe degrees of belief, and probabilistic inference describes rational reasoning under uncertainty. It is no wonder, then, that probabilistic models have exploded onto the scene of modern artificial intelligence, cognitive science, and applied
Probabilistic Programming Concepts
, 2013
"... A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particu ..."
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A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a
Stationarity in Probabilistic Programs
 In Proc. 17th Conf. Foundations Prog. Semantics, Arhus
, 1998
"... We present an axiom system for a weak form of expectation operators leading to an interpretation in which a probabilistic process is modelled as a set of Markov matrices rather than a single matrix. Such `generalised Markov processes' have applications in computing science for modelling probabi ..."
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Cited by 1 (1 self)
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probabilistic programming and specification languages [7, 4]. We use the resulting calculus of operators over a domain of realvalued functions to formulate and prove generalised limit theorems. In the more general context we find that the limit theorems are considerably simpler than the corresponding results
Towards Probabilistic Program Slicing
, 2006
"... This paper outlines the concept of probabilistic program slicing. Whereas conventional slicing removes statements that cannot affect the slicing criterion, probabilistic slicing also removes statements that are unlikely to affect the criterion. The paper presents a simple example before describing s ..."
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This paper outlines the concept of probabilistic program slicing. Whereas conventional slicing removes statements that cannot affect the slicing criterion, probabilistic slicing also removes statements that are unlikely to affect the criterion. The paper presents a simple example before describing
Results 1  10
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193,540