TY - COMP
TI - dmurfet/2simplicialtransformer
AU - Murfet, Daniel
AB - Code for the 2-simplicial Transformer paper. Contribute to dmurfet/2simplicialtransformer development by creating an account on GitHub.
DA - 2019/10/14/T08:10:47Z
PY - 2019
DP - GitHub
LA - Python
UR - https://github.com/dmurfet/2simplicialtransformer
Y2 - 2019/11/22/16:50:05
KW - Abstract machines
KW - Algebra
KW - Implementation
KW - Machine learning
KW - Semantics
ER -
TY - JOUR
TI - Logic and the $2$-Simplicial Transformer
AU - Murfet, Daniel
AU - Clift, James
AU - Doryn, Dmitry
AU - Wallbridge, James
T2 - arXiv:1909.00668 [cs, stat]
AB - We introduce the $2$-simplicial Transformer, an extension of the Transformer which includes a form of higher-dimensional attention generalising the dot-product attention, and uses this attention to update entity representations with tensor products of value vectors. We show that this architecture is a useful inductive bias for logical reasoning in the context of deep reinforcement learning.
DA - 2019/09/02/
PY - 2019
DP - arXiv.org
UR - http://arxiv.org/abs/1909.00668
Y2 - 2019/11/21/20:31:14
KW - Abstract machines
KW - Algebra
KW - Machine learning
KW - Semantics
ER -
TY - JOUR
TI - Reactive Probabilistic Programming
AU - Baudart, Guillaume
AU - Mandel, Louis
AU - Atkinson, Eric
AU - Sherman, Benjamin
AU - Pouzet, Marc
AU - Carbin, Michael
T2 - arXiv:1908.07563 [cs]
AB - Synchronous reactive languages were introduced for designing and implementing real-time control software. These domain-specific languages allow for writing a modular and mathematically precise specification of the system, enabling a user to simulate, test, verify, and, finally, compile the system into executable code. However, to date these languages have had limited modern support for modeling uncertainty -- probabilistic aspects of the software's environment or behavior -- even though modeling uncertainty is a primary activity when designing a control system. In this paper we extend Z\'elus, a synchronous programming language, to deliver ProbZ\'elus, the first synchronous probabilistic programming language. ProbZ\'elus is a probabilistic programming language in that it provides facilities for probabilistic models and inference: inferring latent model parameters from data. We present ProbZ\'elus's measure-theoretic semantics in the setting of probabilistic, stateful stream functions. We then demonstrate a semantics-preserving compilation strategy to a first-order functional core calculus that lends itself to a simple semantic presentation of ProbZ\'elus's inference algorithms. We also redesign the delayed sampling inference algorithm to provide bounded and streaming delayed sampling inference for ProbZ\'elus models. Together with our evaluation on several reactive programs, our results demonstrate that ProbZ\'elus provides efficient, bounded memory probabilistic inference.
DA - 2019/08/20/
PY - 2019
DP - arXiv.org
UR - http://arxiv.org/abs/1908.07563
Y2 - 2019/11/28/10:25:56
KW - Bayesian inference
KW - Denotational semantics
KW - Implementation
KW - Probabilistic programming
KW - Programming language theory
ER -
TY - JOUR
TI - Differentials and distances in probabilistic coherence spaces
AU - Ehrhard, Thomas
T2 - arXiv:1902.04836 [cs]
AB - In probabilistic coherence spaces, a denotational model of probabilistic functional languages, mor-phisms are analytic and therefore smooth. We explore two related applications of the corresponding derivatives. First we show how derivatives allow to compute the expectation of execution time in the weak head reduction of probabilistic PCF (pPCF). Next we apply a general notion of "local" differential of morphisms to the proof of a Lipschitz property of these morphisms allowing in turn to relate the observational distance on pPCF terms to a distance the model is naturally equipped with. This suggests that extending probabilistic programming languages with derivatives, in the spirit of the differential lambda-calculus, could be quite meaningful.
DA - 2019/02/13/
PY - 2019
DP - arXiv.org
UR - http://arxiv.org/abs/1902.04836
Y2 - 2019/11/28/11:57:10
KW - Coherence spaces
KW - Denotational semantics
KW - Differential Linear Logic
KW - Differentiation
KW - Linear logic
KW - Probabilistic programming
KW - Programming language theory
ER -
TY - JOUR
TI - A Domain Theory for Statistical Probabilistic Programming
AU - Vákár, Matthijs
AU - Kammar, Ohad
AU - Staton, Sam
T2 - arXiv:1811.04196 [cs]
AB - We give an adequate denotational semantics for languages with recursive higher-order types, continuous probability distributions, and soft constraints. These are expressive languages for building Bayesian models of the kinds used in computational statistics and machine learning. Among them are untyped languages, similar to Church and WebPPL, because our semantics allows recursive mixed-variance datatypes. Our semantics justifies important program equivalences including commutativity. Our new semantic model is based on `quasi-Borel predomains'. These are a mixture of chain-complete partial orders (cpos) and quasi-Borel spaces. Quasi-Borel spaces are a recent model of probability theory that focuses on sets of admissible random elements. Probability is traditionally treated in cpo models using probabilistic powerdomains, but these are not known to be commutative on any class of cpos with higher order functions. By contrast, quasi-Borel predomains do support both a commutative probabilistic powerdomain and higher-order functions. As we show, quasi-Borel predomains form both a model of Fiore's axiomatic domain theory and a model of Kock's synthetic measure theory.
DA - 2018/11/10/
PY - 2018
DP - arXiv.org
UR - http://arxiv.org/abs/1811.04196
Y2 - 2019/10/10/11:49:16
ER -
TY - COMP
TI - dmurfet/deeplinearlogic
AU - Murfet, Daniel
AB - Deep learning and linear logic. Contribute to dmurfet/deeplinearlogic development by creating an account on GitHub.
DA - 2018/07/14/T01:08:44Z
PY - 2018
DP - GitHub
LA - Jupyter Notebook
UR - https://github.com/dmurfet/deeplinearlogic
Y2 - 2019/11/22/16:44:43
KW - Categorical ML
KW - Implementation
KW - Linear logic
KW - Machine learning
KW - Semantics
ER -
TY - JOUR
TI - Influence Networks Compared with Reaction Networks: Semantics, Expressivity and Attractors
AU - Fages, Francois
AU - Martinez, Thierry
AU - Rosenblueth, David A.
AU - Soliman, Sylvain
T2 - IEEE/ACM Trans. Comput. Biol. Bioinformatics
AB - Biochemical reaction networks are one of the most widely used formalisms in systems biology to describe the molecular mechanisms of high-level cell processes. However, modellers also reason with influence diagrams to represent the positive and negative influences between molecular species and may find an influence network useful in the process of building a reaction network. In this paper, we introduce a formalism of influence networks with forces, and equip it with a hierarchy of Boolean, Petri net, stochastic and differential semantics, similarly to reaction networks with rates. We show that the expressive power of influence networks is the same as that of reaction networks under the differential semantics, but weaker under the discrete semantics. Furthermore, the hierarchy of semantics leads us to consider a positive Boolean semantics that cannot test the absence of a species, that we compare with the negative Boolean semantics with test for absence of a species in gene regulatory networks à la Thomas. We study the monotonicity properties of the positive semantics and derive from them an algorithm to compute attractors in both the positive and negative Boolean semantics. We illustrate our results on models of the literature about the p53/Mdm2 DNA damage repair system, the circadian clock, and the influence of MAPK signaling on cell-fate decision in urinary bladder cancer.
DA - 2018/07//
PY - 2018
DO - 10/ggdf94
DP - ACM Digital Library
VL - 15
IS - 4
SP - 1138
EP - 1151
SN - 1545-5963
ST - Influence Networks Compared with Reaction Networks
UR - https://doi.org/10.1109/TCBB.2018.2805686
Y2 - 2019/11/23/07:40:24
KW - Biology
KW - Rewriting theory
KW - Symbolic logic
KW - Systems biology
ER -
TY - COMP
TI - dmurfet/polysemantics
AU - Murfet, Daniel
AB - Polynomial semantics of linear logic. Contribute to dmurfet/polysemantics development by creating an account on GitHub.
DA - 2018/04/29/T20:41:43Z
PY - 2018
DP - GitHub
LA - Python
UR - https://github.com/dmurfet/polysemantics
Y2 - 2019/11/22/16:45:35
KW - Categorical ML
KW - Implementation
KW - Linear logic
KW - Machine learning
KW - Semantics
ER -
TY - JOUR
TI - Probabilistic call by push value
AU - Ehrhard, Thomas
AU - Tasson, Christine
T2 - arXiv:1607.04690 [cs]
AB - We introduce a probabilistic extension of Levy's Call-By-Push-Value. This extension consists simply in adding a " flipping coin " boolean closed atomic expression. This language can be understood as a major generalization of Scott's PCF encompassing both call-by-name and call-by-value and featuring recursive (possibly lazy) data types. We interpret the language in the previously introduced denotational model of probabilistic coherence spaces, a categorical model of full classical Linear Logic, interpreting data types as coalgebras for the resource comonad. We prove adequacy and full abstraction, generalizing earlier results to a much more realistic and powerful programming language.
DA - 2018///
PY - 2018
DO - 10/ggdk8z
DP - arXiv.org
UR - http://arxiv.org/abs/1607.04690
Y2 - 2019/11/27/20:51:36
KW - Denotational semantics
KW - Linear logic
KW - Probabilistic programming
KW - Programming language theory
ER -
TY - CONF
TI - The concurrent game semantics of Probabilistic PCF
AU - Castellan, Simon
AU - Clairambault, Pierre
AU - Paquet, Hugo
AU - Winskel, Glynn
T2 - the 33rd Annual ACM/IEEE Symposium
AB - We define a new games model of Probabilistic PCF (PPCF) by enriching thin concurrent games with symmetry, recently introduced by Castellan et al, with probability. This model supports two interpretations of PPCF, one sequential and one parallel. We make the case for this model by exploiting the causal structure of probabilistic concurrent strategies. First, we show that the strategies obtained from PPCF programs have a deadlock-free interaction, and therefore deduce that there is an interpretation-preserving functor from our games to the probabilistic relational model recently proved fully abstract by Ehrhard et al. It follows that our model is intensionally fully abstract. Finally, we propose a definition of probabilistic innocence and prove a finite definability result, leading to a second (independent) proof of full abstraction.
C1 - Oxford, United Kingdom
C3 - Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer Science - LICS '18
DA - 2018///
PY - 2018
DO - 10/ggdjfz
DP - Crossref
SP - 215
EP - 224
LA - en
PB - ACM Press
SN - 978-1-4503-5583-4
UR - http://dl.acm.org/citation.cfm?doid=3209108.3209187
Y2 - 2019/11/26/16:57:36
KW - Denotational semantics
KW - Game semantics
KW - Interactive semantics
KW - Probabilistic programming
KW - Programming language theory
ER -
TY - JOUR
TI - Denotational validation of higher-order Bayesian inference
AU - Ścibior, Adam
AU - Kammar, Ohad
AU - Vákár, Matthijs
AU - Staton, Sam
AU - Yang, Hongseok
AU - Cai, Yufei
AU - Ostermann, Klaus
AU - Moss, Sean K.
AU - Heunen, Chris
AU - Ghahramani, Zoubin
T2 - Proceedings of the ACM on Programming Languages
AB - We present a modular semantic account of Bayesian inference algorithms for probabilistic programming languages, as used in data science and machine learning. Sophisticated inference algorithms are often explained in terms of composition of smaller parts. However, neither their theoretical justification nor their implementation reflects this modularity. We show how to conceptualise and analyse such inference algorithms as manipulating intermediate representations of probabilistic programs using higher-order functions and inductive types, and their denotational semantics. Semantic accounts of continuous distributions use measurable spaces. However, our use of higher-order functions presents a substantial technical difficulty: it is impossible to define a measurable space structure over the collection of measurable functions between arbitrary measurable spaces that is compatible with standard operations on those functions, such as function application. We overcome this difficulty using quasi-Borel spaces, a recently proposed mathematical structure that supports both function spaces and continuous distributions. We define a class of semantic structures for representing probabilistic programs, and semantic validity criteria for transformations of these representations in terms of distribution preservation. We develop a collection of building blocks for composing representations. We use these building blocks to validate common inference algorithms such as Sequential Monte Carlo and Markov Chain Monte Carlo. To emphasize the connection between the semantic manipulation and its traditional measure theoretic origins, we use Kock's synthetic measure theory. We demonstrate its usefulness by proving a quasi-Borel counterpart to the Metropolis-Hastings-Green theorem.
DA - 2017/12/27/
PY - 2017
DO - 10.1145/3158148
DP - arXiv.org
VL - 2
IS - POPL
SP - 1
EP - 29
J2 - Proc. ACM Program. Lang.
SN - 24751421
UR - http://arxiv.org/abs/1711.03219
Y2 - 2019/10/10/11:49:49
ER -
TY - JOUR
TI - Measurable Cones and Stable, Measurable Functions
AU - Ehrhard, Thomas
AU - Pagani, Michele
AU - Tasson, Christine
T2 - Proceedings of the ACM on Programming Languages
AB - We define a notion of stable and measurable map between cones endowed with measurability tests and show that it forms a cpo-enriched cartesian closed category. This category gives a denotational model of an extension of PCF supporting the main primitives of probabilistic functional programming, like continuous and discrete probabilistic distributions, sampling, conditioning and full recursion. We prove the soundness and adequacy of this model with respect to a call-by-name operational semantics and give some examples of its denotations.
DA - 2017/12/27/
PY - 2017
DO - 10/ggdjf8
DP - arXiv.org
VL - 2
IS - POPL
SP - 1
EP - 28
J2 - Proc. ACM Program. Lang.
SN - 24751421
UR - http://arxiv.org/abs/1711.09640
Y2 - 2019/11/26/17:06:12
KW - Denotational semantics
KW - Probabilistic programming
KW - Programming language theory
ER -
TY - JOUR
TI - A Convenient Category for Higher-Order Probability Theory
AU - Heunen, Chris
AU - Kammar, Ohad
AU - Staton, Sam
AU - Yang, Hongseok
T2 - arXiv:1701.02547 [cs, math]
AB - Higher-order probabilistic programming languages allow programmers to write sophisticated models in machine learning and statistics in a succinct and structured way, but step outside the standard measure-theoretic formalization of probability theory. Programs may use both higher-order functions and continuous distributions, or even define a probability distribution on functions. But standard probability theory does not handle higher-order functions well: the category of measurable spaces is not cartesian closed. Here we introduce quasi-Borel spaces. We show that these spaces: form a new formalization of probability theory replacing measurable spaces; form a cartesian closed category and so support higher-order functions; form a well-pointed category and so support good proof principles for equational reasoning; and support continuous probability distributions. We demonstrate the use of quasi-Borel spaces for higher-order functions and probability by: showing that a well-known construction of probability theory involving random functions gains a cleaner expression; and generalizing de Finetti's theorem, that is a crucial theorem in probability theory, to quasi-Borel spaces.
DA - 2017/01/10/
PY - 2017
DP - arXiv.org
UR - http://arxiv.org/abs/1701.02547
Y2 - 2019/10/10/11:48:09
ER -
TY - CHAP
TI - Commutative Semantics for Probabilistic Programming
AU - Staton, Sam
T2 - Programming Languages and Systems
A2 - Yang, Hongseok
AB - We show that a measure-based denotational semantics for probabilistic programming is commutative. The idea underlying probabilistic programming languages (Anglican, Church, Hakaru, ...) is that programs express statistical models as a combination of prior distributions and likelihood of observations. The product of prior and likelihood is an unnormalized posterior distribution, and the inference problem is to ﬁnd the normalizing constant. One common semantic perspective is thus that a probabilistic program is understood as an unnormalized posterior measure, in the sense of measure theory, and the normalizing constant is the measure of the entire semantic domain.
CY - Berlin, Heidelberg
DA - 2017///
PY - 2017
DP - Crossref
VL - 10201
SP - 855
EP - 879
LA - en
PB - Springer Berlin Heidelberg
SN - 978-3-662-54433-4 978-3-662-54434-1
UR - http://link.springer.com/10.1007/978-3-662-54434-1_32
Y2 - 2019/11/23/16:35:50
KW - Bayesianism
KW - Probabilistic programming
KW - Programming language theory
KW - Semantics
ER -
TY - JOUR
TI - Mixed powerdomains for probability and nondeterminism
AU - Keimel, Klaus
AU - Plotkin, Gordon D.
T2 - arXiv:1612.01005 [cs]
AB - We consider mixed powerdomains combining ordinary nondeterminism and probabilistic nondeterminism. We characterise them as free algebras for suitable (in)equation-al theories; we establish functional representation theorems; and we show equivalencies between state transformers and appropriately healthy predicate transformers. The extended nonnegative reals serve as `truth-values'. As usual with powerdomains, everything comes in three flavours: lower, upper, and order-convex. The powerdomains are suitable convex sets of subprobability valuations, corresponding to resolving nondeterministic choice before probabilistic choice. Algebraically this corresponds to the probabilistic choice operator distributing over the nondeterministic choice operator. (An alternative approach to combining the two forms of nondeterminism would be to resolve probabilistic choice first, arriving at a domain-theoretic version of random sets. However, as we also show, the algebraic approach then runs into difficulties.) Rather than working directly with valuations, we take a domain-theoretic functional-analytic approach, employing domain-theoretic abstract convex sets called Kegelspitzen; these are equivalent to the abstract probabilistic algebras of Graham and Jones, but are more convenient to work with. So we define power Kegelspitzen, and consider free algebras, functional representations, and predicate transformers. To do so we make use of previous work on domain-theoretic cones (d-cones), with the bridge between the two of them being provided by a free d-cone construction on Kegelspitzen.
DA - 2017///
PY - 2017
DO - 10/ggdmrp
DP - arXiv.org
UR - http://arxiv.org/abs/1612.01005
Y2 - 2019/11/28/12:04:57
KW - Denotational semantics
KW - Powerdomains
KW - Probabilistic programming
KW - Programming language theory
ER -
TY - JOUR
TI - A Formal Semantics of Influence in Bayesian Reasoning
AU - Jacobs, Bart
AU - Zanasi, Fabio
T2 - Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik GmbH, Wadern/Saarbruecken, Germany
AB - This paper proposes a formal deﬁnition of inﬂuence in Bayesian reasoning, based on the notions of state (as probability distribution), predicate, validity and conditioning. Our approach highlights how conditioning a joint entwined/entangled state with a predicate on one of its components has ‘crossover’ inﬂuence on the other components. We use the total variation metric on probability distributions to quantitatively measure such inﬂuence. These insights are applied to give a rigorous explanation of the fundamental concept of d-separation in Bayesian networks.
DA - 2017///
PY - 2017
DO - 10/ggdgbc
DP - DataCite
LA - en
UR - http://drops.dagstuhl.de/opus/volltexte/2017/8089/
Y2 - 2019/11/24/12:11:15
KW - Bayesianism
KW - Categorical probability theory
KW - Programming language theory
KW - Semantics
ER -
TY - JOUR
TI - A Predicate/State Transformer Semantics for Bayesian Learning
AU - Jacobs, Bart
AU - Zanasi, Fabio
T2 - Electronic Notes in Theoretical Computer Science
T3 - The Thirty-second Conference on the Mathematical Foundations of Programming Semantics (MFPS XXXII)
AB - This paper establishes a link between Bayesian inference (learning) and predicate and state transformer operations from programming semantics and logic. Specifically, a very general definition of backward inference is given via first applying a predicate transformer and then conditioning. Analogously, forward inference involves first conditioning and then applying a state transformer. These definitions are illustrated in many examples in discrete and continuous probability theory and also in quantum theory.
DA - 2016/10/05/
PY - 2016
DO - 10/ggdgbb
DP - ScienceDirect
VL - 325
SP - 185
EP - 200
J2 - Electronic Notes in Theoretical Computer Science
LA - en
SN - 1571-0661
UR - http://www.sciencedirect.com/science/article/pii/S1571066116300883
Y2 - 2019/11/24/12:04:12
KW - Bayesianism
KW - Categorical ML
KW - Categorical probability theory
KW - Effectus theory
KW - Programming language theory
KW - Semantics
ER -
TY - JOUR
TI - An introduction to Differential Linear Logic: proof-nets, models and antiderivatives
AU - Ehrhard, Thomas
T2 - arXiv:1606.01642 [cs]
AB - Differential Linear Logic enriches Linear Logic with additional logical rules for the exponential connectives, dual to the usual rules of dereliction, weakening and contraction. We present a proof-net syntax for Differential Linear Logic and a categorical axiomatization of its denotational models. We also introduce a simple categorical condition on these models under which a general antiderivative operation becomes available. Last we briefly describe the model of sets and relations and give a more detailed account of the model of finiteness spaces and linear and continuous functions.
DA - 2016/06/06/
PY - 2016
DP - arXiv.org
ST - An introduction to Differential Linear Logic
UR - http://arxiv.org/abs/1606.01642
Y2 - 2019/11/28/11:52:31
KW - Denotational semantics
KW - Differential Linear Logic
KW - Differentiation
KW - Linear logic
ER -
TY - JOUR
TI - Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints
AU - Staton, Sam
AU - Yang, Hongseok
AU - Heunen, Chris
AU - Kammar, Ohad
AU - Wood, Frank
T2 - Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science - LICS '16
AB - We study the semantic foundation of expressive probabilistic programming languages, that support higher-order functions, continuous distributions, and soft constraints (such as Anglican, Church, and Venture). We define a metalanguage (an idealised version of Anglican) for probabilistic computation with the above features, develop both operational and denotational semantics, and prove soundness, adequacy, and termination. They involve measure theory, stochastic labelled transition systems, and functor categories, but admit intuitive computational readings, one of which views sampled random variables as dynamically allocated read-only variables. We apply our semantics to validate nontrivial equations underlying the correctness of certain compiler optimisations and inference algorithms such as sequential Monte Carlo simulation. The language enables defining probability distributions on higher-order functions, and we study their properties.
DA - 2016///
PY - 2016
DO - 10/ggdf97
DP - arXiv.org
SP - 525
EP - 534
ST - Semantics for probabilistic programming
UR - http://arxiv.org/abs/1601.04943
Y2 - 2019/11/23/16:36:30
KW - Bayesianism
KW - Probabilistic programming
KW - Programming language theory
KW - Semantics
ER -
TY - CONF
TI - Probabilistic coherence spaces are fully abstract for probabilistic PCF
AU - Ehrhard, Thomas
AU - Tasson, Christine
AU - Pagani, Michele
T2 - the 41st ACM SIGPLAN-SIGACT Symposium
AB - Probabilistic coherence spaces (PCoh) yield a semantics of higherorder probabilistic computation, interpreting types as convex sets and programs as power series. We prove that the equality of interpretations in PCoh characterizes the operational indistinguishability of programs in PCF with a random primitive.
C1 - San Diego, California, USA
C3 - Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages - POPL '14
DA - 2014///
PY - 2014
DO - 10/ggdf9x
DP - Crossref
SP - 309
EP - 320
LA - en
PB - ACM Press
SN - 978-1-4503-2544-8
UR - http://dl.acm.org/citation.cfm?doid=2535838.2535865
Y2 - 2019/11/22/17:00:49
KW - Coherence spaces
KW - Probabilistic programming
KW - Programming language theory
KW - Semantics
ER -
TY - JOUR
TI - Probabilistic coherence spaces as a model of higher-order probabilistic computation
AU - Ehrhard, Thomas
AU - Danos, Vincent
T2 - Information and Computation
AB - We study a probabilistic version of coherence spaces and show that these objects provide a model of linear logic. We build a model of the pure lambda-calculus in this setting and show how to interpret a probabilistic version of the functional language PCF. We give a probabilistic interpretation of the semantics of probabilistic PCF closed terms of ground type. Last we suggest a generalization of this approach, using Banach spaces.
DA - 2011/06/01/
PY - 2011
DO - 10/ctfch6
DP - ScienceDirect
VL - 209
IS - 6
SP - 966
EP - 991
J2 - Information and Computation
LA - en
SN - 0890-5401
UR - http://www.sciencedirect.com/science/article/pii/S0890540111000411
Y2 - 2019/11/22/17:01:53
KW - Coherence spaces
KW - Probabilistic programming
KW - Programming language theory
KW - Semantics
ER -
TY - CONF
TI - The Computational Meaning of Probabilistic Coherence Spaces
AU - Ehrhard, Thomas
AU - Pagani, Michele
AU - Tasson, Christine
T2 - 2011 26th Annual IEEE Symposium on Logic in Computer Science (LICS 2011)
AB - We study the probabilistic coherent spaces — a denotational semantics interpreting programs by power series with non negative real coefﬁcients. We prove that this semantics is adequate for a probabilistic extension of the untyped λ-calculus: the probability that a term reduces to a head normal form is equal to its denotation computed on a suitable set of values. The result gives, in a probabilistic setting, a quantitative reﬁnement to the adequacy of Scott’s model for untyped λ-calculus.
C1 - Toronto, ON, Canada
C3 - 2011 IEEE 26th Annual Symposium on Logic in Computer Science
DA - 2011/06//
PY - 2011
DO - 10/cpv52n
DP - Crossref
SP - 87
EP - 96
LA - en
PB - IEEE
SN - 978-1-4577-0451-2
UR - http://ieeexplore.ieee.org/document/5970206/
Y2 - 2019/11/26/20:50:00
KW - Coherence spaces
KW - Denotational semantics
KW - Probabilistic programming
KW - Programming language theory
ER -
TY - JOUR
TI - Predicate Transformers for Extended Probability and Non-determinism
AU - Keimel, Klaus
AU - Plotkin, Gordon d.
T2 - Mathematical. Structures in Comp. Sci.
AB - We investigate laws for predicate transformers for the combination of non-deterministic choice and (extended) probabilistic choice, where predicates are taken to be functions to the extended non-negative reals, or to closed intervals of such reals. These predicate transformers correspond to state transformers, which are functions to conical powerdomains, which are the appropriate powerdomains for the combined forms of non-determinism. As with standard powerdomains for non-deterministic choice, these come in three flavours – lower, upper and (order-)convex – so there are also three kinds of predicate transformers. In order to make the connection, the powerdomains are first characterised in terms of relevant classes of functionals. Much of the development is carried out at an abstract level, a kind of domain-theoretic functional analysis: one considers d-cones, which are dcpos equipped with a module structure over the non-negative extended reals, in place of topological vector spaces. Such a development still needs to be carried out for probabilistic choice per se; it would presumably be necessary to work with a notion of convex space rather than a cone.
DA - 2009/06//
PY - 2009
DO - 10/bkvgqc
DP - ACM Digital Library
VL - 19
IS - 3
SP - 501
EP - 539
SN - 0960-1295
UR - http://dx.doi.org/10.1017/S0960129509007555
Y2 - 2019/11/26/20:33:27
KW - Denotational semantics
KW - Powerdomains
KW - Programming language theory
ER -
TY - JOUR
TI - Semantic Domains for Combining Probability and Non-Determinism
AU - Tix, Regina
AU - Keimel, Klaus
AU - Plotkin, Gordon
T2 - Electronic Notes in Theoretical Computer Science
AB - We present domain-theoretic models that support both probabilistic and nondeterministic choice. In [36], Morgan and McIver developed an ad hoc semantics for a simple imperative language with both probabilistic and nondeterministic choice operators over a discrete state space, using domain-theoretic tools. We present a model also using domain theory in the sense of D.S. Scott (see e.g. [15]), but built over quite general continuous domains instead of discrete state spaces.
DA - 2009/02//
PY - 2009
DO - 10/d9hwq7
DP - Crossref
VL - 222
SP - 3
EP - 99
LA - en
SN - 15710661
UR - https://linkinghub.elsevier.com/retrieve/pii/S1571066109000036
Y2 - 2019/11/26/19:01:09
KW - Denotational semantics
KW - Powerdomains
KW - Probabilistic programming
KW - Programming language theory
ER -
TY - JOUR
TI - The cartesian closed bicategory of generalised species of structures
AU - Fiore, M.
AU - Gambino, N.
AU - Hyland, M.
AU - Winskel, G.
T2 - Journal of the London Mathematical Society
AB - The concept of generalised species of structures between small categories and, correspondingly, that of generalised analytic functor between presheaf categories are introduced. An operation of substitution for generalised species, which is the counterpart to the composition of generalised analytic functors, is also put forward. These deﬁnitions encompass most notions of combinatorial species considered in the literature—including of course Joyal’s original notion—together with their associated substitution operation. Our ﬁrst main result exhibits the substitution calculus of generalised species as arising from a Kleisli bicategory for a pseudo-comonad on profunctors. Our second main result establishes that the bicategory of generalised species of structures is cartesian closed.
DA - 2008/02//
PY - 2008
DO - 10/bd2mr9
DP - Crossref
VL - 77
IS - 1
SP - 203
EP - 220
LA - en
SN - 00246107
UR - http://doi.wiley.com/10.1112/jlms/jdm096
Y2 - 2019/11/28/16:31:36
KW - Denotational semantics
KW - Differential Linear Logic
KW - Differentiation
KW - Linear logic
ER -
TY - JOUR
TI - BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge
AU - Fages, F.
AU - Calzone, L.
AU - Soliman, S.
T2 - Bioinformatics
DA - 2006/07/15/
PY - 2006
DO - 10/dfv
DP - Crossref
VL - 22
IS - 14
SP - 1805
EP - 1807
LA - en
SN - 1367-4803, 1460-2059
ST - BIOCHAM
UR - https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btl172
Y2 - 2019/11/23/07:28:51
KW - Abstract machines
KW - Biology
KW - Implementation
KW - Rewriting theory
KW - Symbolic logic
KW - Systems biology
ER -
TY - JOUR
TI - Distributing probability over non-determinism
AU - Varacca, Daniele
AU - Winskel, Glynn
T2 - Mathematical Structures in Computer Science
DA - 2006/02/21/
PY - 2006
DO - 10/czs9sx
DP - Crossref
VL - 16
IS - 01
SP - 87
LA - en
SN - 0960-1295, 1469-8072
UR - http://www.journals.cambridge.org/abstract_S0960129505005074
Y2 - 2019/11/26/20:30:24
KW - Categorical probability theory
KW - Denotational semantics
KW - Programming language theory
ER -
TY - JOUR
TI - Domain theory, testing and simulation for labelled Markov processes
AU - van Breugel, Franck
AU - Mislove, Michael
AU - Ouaknine, Joël
AU - Worrell, James
T2 - Theoretical Computer Science
T3 - Foundations of Software Science and Computation Structures
AB - This paper presents a fundamental study of similarity and bisimilarity for labelled Markov processes (LMPs). The main results characterize similarity as a testing preorder and bisimilarity as a testing equivalence. In general, LMPs are not required to satisfy a finite-branching condition—indeed the state space may be a continuum, with the transitions given by arbitrary probability measures. Nevertheless we show that to characterize bisimilarity it suffices to use finitely-branching labelled trees as tests. Our results involve an interaction between domain theory and measure theory. One of the main technical contributions is to show that a final object in a suitable category of LMPs can be constructed by solving a domain equation D≅V(D)Act, where V is the probabilistic powerdomain. Given an LMP whose state space is an analytic space, bisimilarity arises as the kernel of the unique map to the final LMP. We also show that the metric for approximate bisimilarity introduced by Desharnais, Gupta, Jagadeesan and Panangaden generates the Lawson topology on the domain D.
DA - 2005/03/01/
PY - 2005
DO - 10/ft9vc5
DP - ScienceDirect
VL - 333
IS - 1
SP - 171
EP - 197
J2 - Theoretical Computer Science
LA - en
SN - 0304-3975
UR - http://www.sciencedirect.com/science/article/pii/S030439750400711X
Y2 - 2019/11/26/19:59:13
KW - Coalgebras
KW - Denotational semantics
KW - Probabilistic transition systems
KW - Transition systems
ER -
TY - JOUR
TI - The differential lambda-calculus
AU - Ehrhard, Thomas
AU - Regnier, Laurent
T2 - Theoretical Computer Science
AB - We present an extension of the lambda-calculus with differential constructions. We state and prove some basic results (confluence, strong normalization in the typed case), and also a theorem relating the usual Taylor series of analysis to the linear head reduction of lambda-calculus.
DA - 2003/12/02/
PY - 2003
DO - 10/bf3b8v
DP - ScienceDirect
VL - 309
IS - 1
SP - 1
EP - 41
J2 - Theoretical Computer Science
LA - en
SN - 0304-3975
UR - http://www.sciencedirect.com/science/article/pii/S030439750300392X
Y2 - 2019/11/24/17:23:34
KW - Differentiation
KW - Linear logic
KW - Programming language theory
ER -
TY - JOUR
TI - Bisimulation for Labelled Markov Processes
AU - Desharnais, Josée
AU - Edalat, Abbas
AU - Panangaden, Prakash
T2 - Information and Computation
AB - In this paper we introduce a new class of labelled transition systems—labelled Markov processes— and define bisimulation for them. Labelled Markov processes are probabilistic labelled transition systems where the state space is not necessarily discrete. We assume that the state space is a certain type of common metric space called an analytic space. We show that our definition of probabilistic bisimulation generalizes the Larsen–Skou definition given for discrete systems. The formalism and mathematics is substantially different from the usual treatment of probabilistic process algebra. The main technical contribution of the paper is a logical characterization of probabilistic bisimulation. This study revealed some unexpected results, even for discrete probabilistic systems. •Bisimulation can be characterized by a very weak modal logic. The most striking feature is that one has no negation or any kind of negative proposition.•We do not need any finite branching assumption, yet there is no need of infinitary conjunction. We also show how to construct the maximal autobisimulation on a system. In the finite state case, this is just a state minimization construction. The proofs that we give are of an entirely different character than the typical proofs of these results. They use quite subtle facts about analytic spaces and appear, at first sight, to be entirely nonconstructive. Yet one can give an algorithm for deciding bisimilarity of finite state systems which constructs a formula that witnesses the failure of bisimulation.
DA - 2002/12/15/
PY - 2002
DO - 10/fmp9vd
DP - ScienceDirect
VL - 179
IS - 2
SP - 163
EP - 193
J2 - Information and Computation
LA - en
SN - 0890-5401
UR - http://www.sciencedirect.com/science/article/pii/S0890540101929621
Y2 - 2019/11/26/21:27:24
KW - Coalgebras
KW - Denotational semantics
KW - Probabilistic transition systems
KW - Symbolic logic
KW - Transition systems
ER -