Bibliografías temáticas / Probabilistic preorder

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Literatura académica sobre el tema "Probabilistic preorder"

Autor: Grafiati
Publicado: 11 de marzo de 2023
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Probabilistic preorder"

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BAIER, CHRISTEL, and MARTA KWIATKOWSKA. "Domain equations for probabilistic processes." Mathematical Structures in Computer Science 10, no. 6 (2000): 665–717. http://dx.doi.org/10.1017/s0960129599002984.

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In this paper we consider Milner's calculus CCS enriched by a probabilistic choice operator. The calculus is given operational semantics based on probabilistic transition systems. We define operational notions of preorder and equivalence as probabilistic extensions of the simulation preorder and the bisimulation equivalence respectively. We extend existing category-theoretic techniques for solving domain equations to the probabilistic case and give two denotational semantics for the calculus. The first, ‘smooth’, semantic model arises as a solution of a domain equation involving the probabilis
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HERNANDEZ, ENRIC, and JORDI RECASENS. "ON POSSIBILISTIC AND PROBABILISTIC APPROXIMATIONS OF UNRESTRICTED BELIEF FUNCTIONS BASED ON THE CONCEPT OF FUZZY T-PREORDER." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, no. 02 (2002): 185–200. http://dx.doi.org/10.1142/s0218488502001417.

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This paper presents a new method for approximating an unrestricted belief measure assuring that the "order" defined by the compatibility degree between evidence and the singletons set is preserved. Our approach, based on the concept of fuzzy T-preorder, also allows us to define several equivalence criteria over the set of all basic probability assignment functions on a given domain. Some others related aspects as uniqueness of the approximations are also addressed.
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Grodin, Harrison, Yue Niu, Jonathan Sterling, and Robert Harper. "Decalf: A Directed, Effectful Cost-Aware Logical Framework." Proceedings of the ACM on Programming Languages 8, POPL (2024): 273–301. http://dx.doi.org/10.1145/3632852.

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We present decalf , a d irected, e ffectful c ost- a ware l ogical f ramework for studying quantitative aspects of functional programs with effects. Like calf , the language is based on a formal phase distinction between the extension and the intension of a program, its pure behavior as distinct from its cost measured by an effectful step-counting primitive. The type theory ensures that the behavior is unaffected by the cost accounting. Unlike calf , the present language takes account of effects , such as probabilistic choice and mutable state. This extension requires a reformulation of calf ’
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Cleaveland, Rance, Zeynep Dayar, Scott A. Smolka, and Shoji Yuen. "Testing Preorders for Probabilistic Processes." Information and Computation 154, no. 2 (1999): 93–148. http://dx.doi.org/10.1006/inco.1999.2808.

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Jonsson, Bengt, and Wang Yi. "Testing preorders for probabilistic processes can be characterized by simulations." Theoretical Computer Science 282, no. 1 (2002): 33–51. http://dx.doi.org/10.1016/s0304-3975(01)00044-5.

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Deng, Yuxin, та Alwen Tiu. "Characterisations of testing preorders for a finite probabilistic π-calculus". Formal Aspects of Computing 24, № 4-6 (2012): 701–26. http://dx.doi.org/10.1007/s00165-012-0238-3.

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Aguirre, Alejandro, and Lars Birkedal. "Step-Indexed Logical Relations for Countable Nondeterminism and Probabilistic Choice." Proceedings of the ACM on Programming Languages 7, POPL (2023): 33–60. http://dx.doi.org/10.1145/3571195.

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Developing denotational models for higher-order languages that combine probabilistic and nondeterministic choice is known to be very challenging. In this paper, we propose an alternative approach based on operational techniques. We study a higher-order language combining parametric polymorphism, recursive types, discrete probabilistic choice and countable nondeterminism. We define probabilistic generalizations of may- and must-termination as the optimal and pessimal probabilities of termination. Then we define step-indexed logical relations and show that they are sound and complete with respec
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8

Deng, Yuxin, Robert van Glabbeek, Matthew Hennessy, and Carroll Morgan. "Characterising Testing Preorders for Finite Probabilistic Processes." Logical Methods in Computer Science 4, no. 4 (2008). http://dx.doi.org/10.2168/lmcs-4(4:4)2008.

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Wild, Paul, and Lutz Schröder. "Characteristic Logics for Behavioural Hemimetrics via Fuzzy Lax Extensions." Logical Methods in Computer Science Volume 18, Issue 2 (June 15, 2022). http://dx.doi.org/10.46298/lmcs-18(2:19)2022.

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In systems involving quantitative data, such as probabilistic, fuzzy, or metric systems, behavioural distances provide a more fine-grained comparison of states than two-valued notions of behavioural equivalence or behaviour inclusion. Like in the two-valued case, the wide variation found in system types creates a need for generic methods that apply to many system types at once. Approaches of this kind are emerging within the paradigm of universal coalgebra, based either on lifting pseudometrics along set functors or on lifting general real-valued (fuzzy) relations along functors by means of fu
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Tesis sobre el tema "Probabilistic preorder"

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Sato, Tetsuya. "Identifying All Preorders on the Subdistribution Monad." 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/199080.

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PARMA, Augusto. "Axiomatic and logical characterizations of probabilistic preorders and trace semantics." Doctoral thesis, 2008. http://hdl.handle.net/11562/337598.

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non disponibile<br>Randomization was first introduced in computer science in order to improve the efficiency of several problems that were classified unfeasible or particularly inefficient, by giving algorithms the ability to flip coins, that is, of making probabilistic choices at some point of the computation. In the paper Probabilistic Algorithms, Rabin proposed efficient solutions to the problems of determining the nearest neighbor and to state the primality of a given number, for which there were no efficient non-probabilistic solutions. Later, he applied probability to a problem of d
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Capítulos de libros sobre el tema "Probabilistic preorder"

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Cleaveland, Rance, Scott A. Smolka, and Amy Zwarico. "Testing preorders for probabilistic processes." In Automata, Languages and Programming. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55719-9_116.

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Yuen, Shoji, Rance Cleaveland, Zeynep Dayar, and Scott A. Smolka. "Fully Abstract Characterizations of Testing Preorders for Probabilistic Processes." In CONCUR '94: Concurrency Theory. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-540-48654-1_36.

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Höhle, Ulrich. "Many-Valued Preorders II: The Symmetry Axiom and Probabilistic Geometry." In Enric Trillas: A Passion for Fuzzy Sets. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16235-5_11.

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Gaboardi, Marco, Shin-ya Katsumata, Dominic Orchard, and Tetsuya Sato. "Graded Hoare Logic and its Categorical Semantics." In Programming Languages and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_9.

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AbstractDeductive verification techniques based on program logics (i.e., the family of Floyd-Hoare logics) are a powerful approach for program reasoning. Recently, there has been a trend of increasing the expressive power of such logics by augmenting their rules with additional information to reason about program side-effects. For example, general program logics have been augmented with cost analyses, logics for probabilistic computations have been augmented with estimate measures, and logics for differential privacy with indistinguishability bounds. In this work, we unify these various approa
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Castagnoli, Erio, Marzia De Donno, Gino Favero, and Paola Modesti. "A Different Way to Look at Random Variables." In Analyzing Risk through Probabilistic Modeling in Operations Research. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9458-3.ch008.

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A classical problem in Decision Theory is to represent a preference preorder among random variables. The fundamental Debreu's Theorem states that, in the discrete case, a preference satisfies the so-called Sure Thing Principle if and only if it can be represented by means of a function that can be additively decomposed along the states of the world where the random variables are defined. Such a representation suggests that every discrete random variable may be seen as a “histogram” (union of rectangles), i.e., a set. This approach leads to several fruitful consequences, both from a theoretical
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Actas de conferencias sobre el tema "Probabilistic preorder"

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Deng, Yuxin, Rob van Glabbeek, Matthew Hennessy, Carroll Morgan, and Chenyi Zhang. "Characterising Testing Preorders for Finite Probabilistic Processes." In 2007 22nd Annual IEEE Symposium on Logic in Computer Science. IEEE, 2007. http://dx.doi.org/10.1109/lics.2007.15.

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