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To model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. Stochastic constraint programs contain both decision variables (which we can set) and stochastic variables…

Artificial Intelligence · Computer Science 2009-03-09 Toby Walsh

We present Polaris, a concurrent separation logic with support for probabilistic reasoning. As part of our logic, we extend the idea of coupling, which underlies recent work on probabilistic relational logics, to the setting of programs…

Programming Languages · Computer Science 2018-11-22 Joseph Tassarotti , Robert Harper

In this paper we formulate a probabilistic model for class-specific discriminant subspace learning. The proposed model can naturally incorporate the multi-modal structure of the negative class, which is neglected by existing class-specific…

Machine Learning · Computer Science 2020-10-06 Alexandros Iosifidis

This text provides an introduction to distributed local algorithms -- an area at the intersection of theoretical computer science and discrete mathematics. We collect recent results in the area and demonstrate how they lead to a clean…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-11-22 Václav Rozhoň

A new combinatorial-probabilistic diagnostic entropy has been introduced. It describes the pair-wise sum of probabilities of system conditions that have to be distinguished during the diagnosing process. The proposed measure describes the…

Information Theory · Computer Science 2009-09-29 Henryk Borowczyk

In this chapter, we review a principled way of defining and measuring contextuality in systems with deterministic inputs and random outputs, recently proposed and developed in \citep{KujalaDzhafarovLarsson2015,DKL2015FooP}.

Quantum Physics · Physics 2015-12-02 Janne V. Kujala , Ehtibar N. Dzhafarov

A probabilistic model describes a system in its observational state. In many situations, however, we are interested in the system's response under interventions. The class of structural causal models provides a language that allows us to…

Methodology · Statistics 2020-01-20 Jonas Peters , Stefan Bauer , Niklas Pfister

Determinantal Point Processes (DPPs) are a family of probabilistic models that have a repulsive behavior, and lend themselves naturally to many tasks in machine learning where returning a diverse set of objects is important. While there are…

Statistics Theory · Mathematics 2017-03-03 John Urschel , Victor-Emmanuel Brunel , Ankur Moitra , Philippe Rigollet

This paper investigates applicability of thermodynamic concepts and principles to competitive systems. We show that Tsallis entropies are suitable for characterisation of systems with transitive competition when mutations deviate from Gibbs…

Adaptation and Self-Organizing Systems · Physics 2014-03-10 A. Y. Klimenko

A probabilistic propositional logic, endowed with an epistemic component for asserting (non-)compatibility of diagonizable and bounded observables, is presented and illustrated for reasoning about the random results of projective…

Logic · Mathematics 2018-03-20 A. Sernadas , J. Rasga , C. Sernadas , L. Alcácer , A. B. Henriques

This paper introduces a class of objects called decision rules that map infinite sequences of alternatives to a decision space. These objects can be used to model situations where a decision maker encounters alternatives in a sequence such…

Theoretical Economics · Economics 2022-09-12 Bhavook Bhardwaj , Siddharth Chatterjee

A central paradigm behind process semantics based on observability and testing is that the exact moment of occurring of an internal nondeterministic choice is unobservable. It is natural, therefore, for this property to hold when the…

Logic in Computer Science · Computer Science 2009-07-10 Sonja Georgievska , Suzana Andova

Individual choices often depend on the order in which the decisions are made. In this paper, we expose a general theory of measurable systems (an example of which is an individual's preferences) allowing for incompatible (non-commuting)…

Physics and Society · Physics 2007-06-20 V. I. Danilov , A. Lambert-Mogiliansky

Determinantal point processes (DPPs) are probability models over subsets of a ground set that favor diverse selections while suppressing redundancy. That is, they tend to assign higher likelihood to collections whose elements complement one…

Optimization and Control · Mathematics 2026-04-13 Mohamad H. Kazma , Ahmad F. Taha

This work is an enquiry into the circumstances under which entropy methods can give an answer to the questions of both quantum separability and classical correlations of a composite state. Several entropy functionals are employed to examine…

Quantum Physics · Physics 2009-11-07 A. K. Rajagopal , R. W. Rendell

The notion of "closed systems" in Quantum Mechanics is discussed. For this purpose, we study two models of a quantum-mechanical system $P$ spatially far separated from the "rest of the universe" $Q$. Under reasonable assumptions on the…

Mathematical Physics · Physics 2015-05-06 Jérémy Faupin , Jürg Fröhlich , Baptiste Schubnel

The reverse engineering problem with probabilities and sequential behavior is introducing here, using the expression of an algorithm. The solution is partially founded, because we solve the problem only if we have a Probabilistic Sequential…

Dynamical Systems · Mathematics 2007-08-13 Maria A. Avino-Diaz

We attempt to make superdeterminism more intuitive, notably by simulating a deterministic model system, a billiard game. In this system an initial 'bang' correlates all events, just as in the superdeterministic universe. We introduce the…

Quantum Physics · Physics 2023-01-04 Vitaly Nikolaev , Louis Vervoort

The paper is devoted to the introduction of natural deduction systems for some weak subintuitionistic logics, along with proofs of normalization theorems for these systems.

Logic · Mathematics 2024-12-03 Fatemeh Shirmohammadzadeh Maleki

Determinantal point processes have arisen in diverse settings in recent years and have been investigated intensively. We study basic combinatorial and probabilistic aspects in the discrete case. Our main results concern relationships with…

Probability · Mathematics 2010-04-27 Russell Lyons
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