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29
Just do it: Simple monadic equational reasoning
 In Proceedings of the 16th International Conference on Functional Programming (ICFP’11
, 2011
"... One of the appeals of pure functional programming is that it is so amenable to equational reasoning. One of the problems of pure functional programming is that it rules out computational effects. Moggi and Wadler showed how to get round this problem by using monads to encapsulate the effects, leadin ..."
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One of the appeals of pure functional programming is that it is so amenable to equational reasoning. One of the problems of pure functional programming is that it rules out computational effects. Moggi and Wadler showed how to get round this problem by using monads to encapsulate the effects, leading in essence to a phase distinction—a pure functional evaluation yielding an impure imperative computation. Still, it has not been clear how to reconcile that phase distinction with the continuing appeal of functional programming; does the impure imperative part become inaccessible to equational reasoning? We think not; and to back that up, we present a simple axiomatic approach to reasoning about programs with computational effects.
Semipullbacks and Bisimulation in Categories of Markov Processes
, 1999
"... this paper, we show that the answer to the above question is positive. More specifically, we give a canonical construction for semipullbacks in the category whose objects are families of Markov processes, with given transition kernels, on Polish spaces and whose morphisms are transition probability ..."
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this paper, we show that the answer to the above question is positive. More specifically, we give a canonical construction for semipullbacks in the category whose objects are families of Markov processes, with given transition kernels, on Polish spaces and whose morphisms are transition probability preserving surjective continuous maps. One immediate consequence is that the category of probability measures on Polish spaces with measurepreserving continuous maps has semipullbacks. Our construction gives semipullbacks for various full subcategories, including that of Markov processes on locally compact second countable spaces and also in the larger category where the objects are Markov processes on analytic spaces (i.e. continuous images of Polish spaces) and morphisms are transition probability preserving surjective Borel maps. It also applies to the corresponding categories of ultrametric spaces. Finally, our result also holds in the larger categories with Markov processes which are given by subprobability distributions, i.e. the total probability of transition from a state can be strictly less than one. We now explain the relevance of our result in computer science. The consequences of Semipullbacks and Bisimulation 3 our mathematical result in the theory of probabilistic bisimulation has been investigated in (Blute et al., 1997; Desharnais et al., 1998). We will briefly review this here. Following the work of Joyal, Nielsen and Winskel (Joyal et al., 1996) on the notion of bisimulation using open maps, define two objects A and B in a category to be bisimular if there exists an object C and morphisms f : C ! A and g : C ! B, i.e.,
Possibilistic and Probabilistic AbstractionBased Model Checking
 Process Algebra and Probabilistic Methods, Performance Modeling and Veri Second Joint International Workshop PAPMPROBMIV 2002, volume 2399 of Lecture Notes in Computer Science
, 2002
"... models whose verification results transfer to the abstracted models for a logic with unrestricted use of negation and quantification. This framework is novel in that its models have quantitative or probabilistic observables and state transitions. Properties of a quantitative temporal logic have meas ..."
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Cited by 4 (2 self)
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models whose verification results transfer to the abstracted models for a logic with unrestricted use of negation and quantification. This framework is novel in that its models have quantitative or probabilistic observables and state transitions. Properties of a quantitative temporal logic have measurable denotations in these models. For probabilistic models such denotations approximate the probabilistic semantics of full LTL. We show how predicatebased abstractions specify abstract quantitative and probabilistic models with finite state space. 1
The SheafTheoretic Structure Of NonLocality and Contextuality
, 2011
"... Locality and noncontextuality are intuitively appealing features of classical physics, which are contradicted by quantum mechanics. The goal of the classic nogo theorems by Bell, KochenSpecker, et al. is to show that nonlocality and contextuality are necessary features of any theory whose predic ..."
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Locality and noncontextuality are intuitively appealing features of classical physics, which are contradicted by quantum mechanics. The goal of the classic nogo theorems by Bell, KochenSpecker, et al. is to show that nonlocality and contextuality are necessary features of any theory whose predictions agree with those of quantum mechanics. We use the mathematics of sheaf theory to analyze the structure of nonlocality and contextuality in a very general setting. Starting from a simple experimental scenario, and the kind of probabilistic models familiar from discussions of Bell’s theorem, we show that there is a very direct, compelling formalization of these notions in sheaftheoretic terms. Moreover, on the basis of this formulation, we show that the phenomena of nonlocality and contextuality can be characterized precisely in terms of obstructions to the existence of global sections. We give linear algebraic methods for computing these obstructions, and use these methods to obtain a number of new insights into nonlocality and contextuality. For example, we distinguish a proper hierarchy of strengths of nogo theorems, and show that three leading examples — due to Bell, Hardy, and Greenberger, Horne and Zeilinger, respectively — occupy successively higher levels of this hierarchy. We show how our abstract setting can be represented in quantum mechanics. In doing so, we uncover a strengthening of the usual nosignalling theorem, which shows that quantum mechanics obeys nosignalling for arbitrary families of commuting observables, not just those represented on different factors of a tensor product.
The Demonic Product of Probabilistic Relations
, 2001
"... The demonic product of two probabilistic relations is defined and investigated. It is shown that the product is stable under bisimulations when the mediating object is probabilistic, and that under some mild conditions the nondeterministic fringe of the probabilistic relations behaves properly: the ..."
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The demonic product of two probabilistic relations is defined and investigated. It is shown that the product is stable under bisimulations when the mediating object is probabilistic, and that under some mild conditions the nondeterministic fringe of the probabilistic relations behaves properly: the fringe of the product equals the demonic product of the fringes.
A DSL for Explaining Probabilistic Reasoning
 In IFIP Working Conference on DomainSpecific Languages
, 2009
"... Abstract. We propose a new focus in language design where languages provide constructs that not only describe the computation of results, but also produce explanations of how and why those results were obtained. We posit that if users are to understand computations produced by a language, that langu ..."
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Abstract. We propose a new focus in language design where languages provide constructs that not only describe the computation of results, but also produce explanations of how and why those results were obtained. We posit that if users are to understand computations produced by a language, that language should provide explanations to the user. As an example of such an explanationoriented language we present a domainspecific language for explaining probabilistic reasoning, a domain that is not well understood by nonexperts. We show the design of the DSL in several steps. Based on a storytelling metaphor of explanations, we identify generic constructs for building stories out of events, and obtaining explanations by applying stories to specific examples. These generic constructs are then adapted to the particular explanation domain of probabilistic reasoning. Finally, we develop a visual notation for explaining probabilistic reasoning. 1
Exemplaric Expressivity of Modal Logics
, 2008
"... This paper investigates expressivity of modal logics for transition systems, multitransition systems, Markov chains, and Markov processes, as coalgebras of the powerset, finitely supported multiset, finitely supported distribution, and measure functor, respectively. Expressivity means that logically ..."
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This paper investigates expressivity of modal logics for transition systems, multitransition systems, Markov chains, and Markov processes, as coalgebras of the powerset, finitely supported multiset, finitely supported distribution, and measure functor, respectively. Expressivity means that logically indistinguishable states, satisfying the same formulas, are behaviourally indistinguishable. The investigation is based on the framework of dual adjunctions between spaces and logics and focuses on a crucial injectivity property. The approach is generic both in the choice of systems and modalities, and in the choice of a “base logic”. Most of these expressivity results are already known, but the applicability of the uniform setting of dual adjunctions to these particular examples is what constitutes the contribution of the paper.
The Weakest Completion Approach to the Probabilistic Semantics
, 2000
"... A standard program starts its execution in an initial state, and terminates (if it ever does) in one of a set of final states. Its behaviour can be modelled by a binary relation between the initial and final states. The difference between the standard and the probabilistic semantics is that the form ..."
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A standard program starts its execution in an initial state, and terminates (if it ever does) in one of a set of final states. Its behaviour can be modelled by a binary relation between the initial and final states. The difference between the standard and the probabilistic semantics is that the former tells us which final states are or are not possible, whereas the latter tells us the probability with which they may occur. This paper presents a link between the probabilistic and the imperative programming using the weakest completion. We demonstrate how the probabilistic semantics can be derived directly from the standard relational one using the type embedding and healthiness condition of real programs. Carroll Morgan is an adjunct professor in the department of computer science at New South Wales University in Australia. He conducts research in the area of refinement theories and formal methods applied to software engineering and applications to parallel and distributed computing, ...
A Programming Language for Probabilistic Computation
, 2005
"... As probabilistic computations play an increasing role in solving various problems, researchers have designed probabilistic languages to facilitate their modeling. Most of the existing probabilistic languages, however, focus only on discrete distributions, and there has been little effort to develop ..."
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As probabilistic computations play an increasing role in solving various problems, researchers have designed probabilistic languages to facilitate their modeling. Most of the existing probabilistic languages, however, focus only on discrete distributions, and there has been little effort to develop probabilistic languages whose expressive power is beyond discrete distributions. This dissertation presents a probabilistic language, called PTP (ProbabilisTic Programming), which supports all kinds of probability distributions.
Modeling Genome Evolution with a DSEL for Probabilistic Programming
 In 8th Int. Symp. on Practical Aspects of Declarative Languages
, 2006
"... Abstract. Many scientific applications benefit from simulation. However, programming languages used in simulation, such as C++ or Matlab, approach problems from a deterministic procedural view, which seems to differ, in general, from many scientists ’ mental representation. We apply a domainspecifi ..."
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Abstract. Many scientific applications benefit from simulation. However, programming languages used in simulation, such as C++ or Matlab, approach problems from a deterministic procedural view, which seems to differ, in general, from many scientists ’ mental representation. We apply a domainspecific language for probabilistic programming to the biological field of gene modeling, showing how the mentalmodel gap may be bridged. Our system assisted biologists in developing a model for genome evolution by separating the concerns of model and simulation and providing implicit probabilistic nondeterminism.