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This paper concerns parameterized convex infinite (or semi-infinite) inequality systems whose decision variables run over general infinite-dimensional Banach (resp. finite-dimensional) spaces and that are indexed by an arbitrary fixed set T…

Optimization and Control · Mathematics 2011-02-07 M. J. CÁnovas , M. A. LÓpez , B. S. Mordukhovich , J. Parra

This paper develops a unified identification framework for counterfactual analysis in incomplete models characterized by support and moment restrictions. I demonstrate that identifying structural parameters and conducting counterfactual…

Econometrics · Economics 2026-03-10 Lixiong Li

The Jordan structure of finite-dimensional quantum theory is derived, in a conspicuously easy way, from a few simple postulates concerning abstract probabilistic models (each defined by a set of basic measurements and a convex set of…

Quantum Physics · Physics 2019-07-10 Alexander Wilce

In this work, we consider the fundamental problem of deriving quantitative bounds on the probability that a given assertion is violated in a probabilistic program. We provide automated algorithms that obtain both lower and upper bounds on…

Programming Languages · Computer Science 2020-12-02 Jinyi Wang , Yican Sun , Hongfei Fu , Krishnendu Chatterjee , Amir Kafshdar Goharshady

In this paper, we investigate direct solution of FRE and compare their results with expected real consequences. We give an applied example formulated by a FRE problem and show that FRE defined by maximum t-conorm and an arbitrary t-norm can…

Optimization and Control · Mathematics 2022-07-26 Amin Ghodousiana , Sara Zal

Particularly in genomics, but also in other fields, it has become commonplace to undertake highly multiple Student's $t$-tests based on relatively small sample sizes. The literature on this topic is continually expanding, but the main…

Statistics Theory · Mathematics 2010-10-11 Peter Hall , Qiying Wang

We obtain analytical lower bounds on the concurrence of bipartite quantum systems in arbitrary dimensions related to the violation of separability conditions based on local uncertainty relations and on the Bloch representation of density…

Quantum Physics · Physics 2009-11-13 Julio I. de Vicente

The Complex Langevin (CL) method to simulate `complex probabilities', ideally produces expectation values for the observables that converge to a limit equal to the expectation values obtained with the original complex `probability' measure.…

High Energy Physics - Lattice · Physics 2023-12-01 Erhard Seiler , Dénes Sexty , Ion-Olimpiu Stamatescu

In many real-life settings, agents must navigate dynamic environments while reasoning under incomplete information and acting on a corpus of unstable, context-dependent, and often conflicting norms. We introduce a general, non-modal,…

Logic in Computer Science · Computer Science 2025-12-23 Mario Piazza , Andrea Sabatini

In this paper we consider a class of conjugate equations, which generalizes de Rham's functional equations. We give sufficient conditions for existence and uniqueness of solutions under two different series of assumptions. We consider…

Classical Analysis and ODEs · Mathematics 2026-04-15 Kazuki Okamura

Causal inference is known to be very challenging when only observational data are available. Randomized experiments are often costly and impractical and in instrumental variable regression the number of instruments has to exceed the number…

Methodology · Statistics 2018-06-19 Dominik Rothenhäusler , Peter Bühlmann , Nicolai Meinshausen

In this study, Devaney's chaos conditions are revisited within the framework of descriptive proximity. The concepts of descriptive transitivity, the density of descriptive periodic objects, and descriptive sensitivity are defined. The most…

General Topology · Mathematics 2026-04-28 Fatih Ucan , Tane Vergili

Incorporating constraints is a major concern in probabilistic machine learning. A wide variety of problems require predictions to be integrated with reasoning about constraints, from modelling routes on maps to approving loan predictions.…

Machine Learning · Computer Science 2020-01-31 Ioannis Papantonis , Vaishak Belle

Univalent functions are complex, analytic (holomorphic) and injective functions that have been widely discussed in complex analysis. It was recently proposed that the stringent constraints that univalence imposes on the growth of functions…

High Energy Physics - Theory · Physics 2022-11-09 Matteo Baggioli , Sebastian Grieninger , Sašo Grozdanov , Zhenkang Lu

For a first-order theory $T$, the Constraint Satisfaction Problem of $T$ is the computational problem of deciding whether a given conjunction of atomic formulas is satisfiable in some model of $T$. In this article we develop sufficient…

Logic · Mathematics 2020-12-03 Manuel Bodirsky , Johannes Greiner

We systematically investigate the complexity of model checking the existential positive fragment of first-order logic. In particular, for a set of existential positive sentences, we consider model checking where the sentence is restricted…

Logic in Computer Science · Computer Science 2015-03-20 Hubie Chen

The Theory of Functional Connections (TFC) is a functional interpolation framework founded upon the so-called constrained expression: a functional that expresses the family of all possible functions that satisfy some user-specified, linear…

Analysis of PDEs · Mathematics 2021-10-25 Carl Leake

While intersections of convex sets are convex, their unions have rather complicated behavior. Some natural contexts where they appear include duality arguments involving boundaries of convex sets and valuations, which have an Euler…

Combinatorics · Mathematics 2026-02-06 Soohyun Park

We investigate determining the exact bounds of the frequencies of conjunctions based on frequent sets. Our scenario is an important special case of some general probabilistic logic problems that are known to be intractable. We show that…

Computational Complexity · Computer Science 2019-02-05 Nikolaj Tatti

This article describes a robust algorithm to estimate a conditional probability density f(t|x) as a non-parametric smooth regression function. It is based on a neural network and the Bayesian interpretation of the network output as a…

Data Analysis, Statistics and Probability · Physics 2007-05-23 Michael Feindt
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