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The problem of minimizing convex functionals of probability distributions is solved under the assumption that the density of every distribution is bounded from above and below. A system of sufficient and necessary first-order optimality…

Information Theory · Computer Science 2018-12-05 Michael Fauss , Abdelhak M. Zoubir

In extracting predictions from theories that describe a multiverse, we face the difficulty that we must assess probability distributions over possible observations, prescribed not just by an underlying theory, but by a theory together with…

History and Philosophy of Physics · Physics 2015-06-18 Feraz Azhar

Classical Kolmogorov's and Rosenthal's inequalities for the maximum partial sums of random variables are basic tools for studying the strong laws of large numbers. In this paper, motived by the notion of independent and identically…

Probability · Mathematics 2019-03-06 Li-Xin Zhang

We establish a comparison principle for viscosity subsolutions and supersolutions of a broad class of second-order quasilinear, maximally subelliptic PDEs on general manifolds. In fact, we prove the comparison theorem for a larger class of…

Analysis of PDEs · Mathematics 2026-04-15 Gautam Neelakantan Memana

This paper addresses the problem of stochastic optimization with decision-dependent uncertainty, a class of problems where the probability distribution of the uncertain parameters is influenced by the decision-maker's actions. While recent…

Optimization and Control · Mathematics 2025-09-12 John Cotrina , Gonzalo Flores , David Salas , Anton Svensson

Recently, continuum Microkinetic Rate Theory (cMRT) has been advanced as a method of studying rates of systems, where deviations between observation and cMRT theory have been found, and it hypothesized that these deviations are linked…

Statistical Mechanics · Physics 2019-01-23 M. F. Francis

Variational Inference is a powerful tool in the Bayesian modeling toolkit, however, its effectiveness is determined by the expressivity of the utilized variational distributions in terms of their ability to match the true posterior…

Machine Learning · Statistics 2019-05-10 Artem Sobolev , Dmitry Vetrov

The current form of Tsallis distribution for a Hamiltonian system with an arbitrary potential is found to represent a simple isothermal situation. In this letter, the q-exponential of a sum can be applied as the product of the q-exponential…

Statistical Mechanics · Physics 2015-08-10 Jiulin Du

Distribution functions of many static transport equations are found using the Maximum Entropy Principle. The equations of constraint which contain the relevant dynamical information are simply the low-lying moments of the distributions.…

Statistical Mechanics · Physics 2020-04-22 J. A. Secrest , J. M. Conroy , H. G. Miller

We show that there is weak distributive law of the Smyth hyperspace monad $\mathcal Q_{\mathsf V}$ (resp., the Hoare hyperspace monad $\mathcal H_{\mathsf V}$, resp. the monad $\mathcal P\ell^{\mathrm q}_{\mathsf V}$ of quasi-lenses, resp.…

Category Theory · Mathematics 2025-07-21 Jean Goubault-Larrecq

We develop new solvability methods for divergence form second order, real and complex, elliptic systems above Lipschitz graphs, with $L_2$ boundary data. The coefficients $A$ may depend on all variables, but are assumed to be close to…

Analysis of PDEs · Mathematics 2010-09-16 Pascal Auscher , Andreas Axelsson

Monotonicity with respect to all arguments is fundamental to the definition of aggregation functions. It is also a limiting property that results in many important non-monotonic averaging functions being excluded from the theoretical…

Artificial Intelligence · Computer Science 2014-08-05 Tim Wilkin , Gleb Beliakov

For a strictly stationary sequence of nonnegative regularly varying random variables $(X_{n})$ we study functional weak convergence of partial maxima processes $M_{n}(t) = \bigvee_{i=1}^{\lfloor nt \rfloor}X_{i},\,t \in [0,1]$ in the space…

Probability · Mathematics 2015-12-16 Danijel Krizmanić

Inferential models (IMs) are data-dependent, imprecise-probabilistic structures designed to quantify uncertainty about unknowns. As the name suggests, the focus has been on uncertainty quantification for inference and on its reliability…

Statistics Theory · Mathematics 2026-05-01 Ryan Martin , Shih-Ni Prim , Jonathan Williams

We introduce the notion of weak minimizer in set optimization. Necessary and sufficient conditions in terms of scalarized variational inequalities of Stampacchia and Minty type, respectively, are proved. As an application, we obtain…

Optimization and Control · Mathematics 2016-12-02 Giovanni P. Crespi , MatteoRocca , Carola Schrage

Maximization of {\it non-submodular} functions appears in various scenarios, and many previous works studied it based on some measures that quantify the closeness to being submodular. On the other hand, many practical non-submodular…

Data Structures and Algorithms · Computer Science 2019-10-04 Shinsaku Sakaue

Assumptions on a likelihood function, including a local Glivenko-Cantelli condition, imply the existence of M-estimators converging to an M-functional. Scatter matrix-valued estimators, defined on all empirical measures on ${\Bbb{R}}^d$ for…

Statistics Theory · Mathematics 2007-06-13 R. M. Dudley

By a classical result of Weyl, for any increasing sequence $(n_k)_{k \geq 1}$ of integers the sequence of fractional parts $(\{n_k x\})_{k \geq 1}$ is uniformly distributed modulo 1 for almost all $x \in [0,1]$. Except for a few special…

Number Theory · Mathematics 2013-07-26 Christoph Aistleitner

The strong maximum principle is proved to hold for weak (in the sense of support functions) sub- and super-solutions to a class of quasi-linear elliptic equations that includes the mean curvature equation for $C^0$ spacelike hypersurfaces…

dg-ga · Mathematics 2008-02-03 L. Andersson , G. J. Galloway , R. Howard

Max-stable processes provide natural models for the modelling of spatial extreme values observed at a set of spatial sites. Full likelihood inference for max-stable data is, however, complicated by the form of the likelihood function as it…

Methodology · Statistics 2022-12-15 Patrik Andersson , Alexander Engberg
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