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A random set is a generalisation of a random variable, i.e. a set-valued random variable. The random set theory allows a unification of other uncertainty descriptions such as interval variable, mass belief function in Dempster-Shafer theory…

Numerical Analysis · Mathematics 2018-11-27 Truong-Vinh Hoang , Hermann G. Matthies

We compute the Galois cohomology of any $p$-adic valuation field extension of a pre-perfectoid field. Moreover, we obtain a generalization and also a new proof of the classical results of Tate and Hyodo on discrete valuation fields, without…

Algebraic Geometry · Mathematics 2025-02-21 Tongmu He

This paper establishes an abstract Korovkin-type approximation theorem in general spaces, extending the framework of approximation theory to accommodate broader contexts. A critical result supporting this theorem is the proof that any…

Functional Analysis · Mathematics 2025-09-03 Dilek Söylemez , Mehmet Ünver

We set up a framework for using algebraic geometry to study the generalised cohomology rings that occur in algebraic topology. This idea was probably first introduced by Quillen and it underlies much of our understanding of complex oriented…

Algebraic Topology · Mathematics 2007-05-23 Neil P. Strickland

The asymptotic study of the conjugacy classes of a random element of the finite affine group leads one to define a probability measure on the set of all partitions of all positive integers. Four different probabilistic understandings of…

Group Theory · Mathematics 2007-05-23 Jason Fulman

We propose another interpretation of well-known derivatives computations from regular expressions, due to Brzozowski, Antimirov or Lombardy and Sakarovitch, in order to abstract the underlying data structures (e.g. sets or linear…

Formal Languages and Automata Theory · Computer Science 2023-01-31 Samira Attou , Ludovic Mignot , Clément Miklarz , Florent Nicart

Non-random sample selection is a commonplace amongst many empirical studies and it appears when an output variable of interest is available only for a restricted non-random sub-sample of data. We introduce an extension of the generalized…

Statistics Theory · Mathematics 2015-08-18 M. Wojtyś , G. Marra

We introduce a notion of generalized homogeneous derivations on graded rings as a natural extension of the homogeneous derivations defined by Kanunnikov. We then define gr-generalized derivations, which preserve the degrees of homogeneous…

Rings and Algebras · Mathematics 2026-03-24 Yassine Ait Mohamed

We revisit a classical theme of (general or translation invariant) valuations on convex polyhedra. Our setting generalizes the classical one, in a ``dual'' direction to previously considered generalizations: while previous research was…

Combinatorics · Mathematics 2026-01-07 Askold Khovanskii , Valentina Kiritchenko , Vladlen Timorin

Probabilistic submeasures generalizing the classical (numerical) submeasures are introduced and discussed in connection with some classes of aggregation functions. A special attention is paid to triangular norm-based probabilistic…

Classical Analysis and ODEs · Mathematics 2012-07-12 Lenka Halčinová , Ondrej Hutník , Radko Mesiar

Here we extend the algebro-geometric approach to free probability, started in~\cite{FMcK4,F14}, to general (non)-commutative probability theories. We show that any universal convolution product of moments of independent (non)-commutative…

Representation Theory · Mathematics 2015-06-24 Roland M. Friedrich , John McKay

We investigate how the topology of attributed graphs influences the distribution of node attributes. This work offers a novel perspective by treating topology and attributes as structurally distinct but interacting components. We introduce…

Machine Learning · Computer Science 2026-02-03 Amirreza Shiralinasab Langari , Leila Yeganeh , Kim Khoa Nguyen

The emergent field of probabilistic numerics has thus far lacked clear statistical principals. This paper establishes Bayesian probabilistic numerical methods as those which can be cast as solutions to certain inverse problems within the…

Methodology · Statistics 2019-11-15 Jon Cockayne , Chris Oates , Tim Sullivan , Mark Girolami

In this article we discuss the formal structure of a generalized information theory based on the extension of the probability calculus of Kolmogorov to a (possibly) non-commutative setting. By studying this framework, we argue that quantum…

Quantum Physics · Physics 2016-01-19 F. Holik , G. M. Bosyk , G. Bellomo

The aim of this article is to promote the use of probabilistic methods in the study of problems in mathematical general relativity. Two new and simple singularity theorems, whose features are different from the classical singularity…

Probability · Mathematics 2011-02-21 Ismael Bailleul

The purpose of this paper is to build a new bridge between category theory and a generalized probability theory known as noncommutative probability or quantum probability, which was originated as a mathematical framework for quantum theory,…

Category Theory · Mathematics 2021-09-07 Hayato Saigo

Many active mathematical research topics nowadays include the concepts of valued fields and local fields, especially the local field of p-adic numbers Qp and the field of formal Laurent series F((X)). Local fields are a notion situated in…

Number Theory · Mathematics 2019-05-07 Mouad Moutaoukil , Abdelkader Benaissat

We consider the topological theory of Witten type for gauge differential p-forms. It is shown that some topological invariants such as linking numbers appear under quantization of this theory. The non-abelian generalization of the model is…

High Energy Physics - Theory · Physics 2015-06-26 S. N. Solodukhin

Implicative algebras have been recently introduced by Miquel in order to provide a unifying notion of model, encompassing the most relevant and used ones, such as realizability (both classical and intuitionistic), and forcing. In this work,…

Category Theory · Mathematics 2023-12-06 Samuele Maschio , Davide Trotta

The purpose of this paper is to look into how central notions in statistical learning theory, such as realisability, generalise under the assumption that train and test distribution are issued from the same credal set, i.e., a convex set of…

Machine Learning · Computer Science 2024-02-23 Fabio Cuzzolin
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