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I review the classical theory of likelihood based inference and consider how it is being extended and developed for use in complex models and sampling schemes.

Statistics Theory · Mathematics 2013-10-01 Nancy Reid

The law of likelihood underlies a general framework, known as the likelihood paradigm, for representing and interpreting statistical evidence. As stated, the law applies only to simple hypotheses, and there have been reservations about…

Statistics Theory · Mathematics 2009-01-06 Zhiwei Zhang

We explore various combinatorial problems mostly borrowed from physics, that share the property of being continuously or discretely integrable, a feature that guarantees the existence of conservation laws that often make the problems…

Mathematical Physics · Physics 2017-11-22 Philippe Di Francesco

This paper gives game-theoretic versions of several results on "merging of opinions" obtained in measure-theoretic probability and algorithmic randomness theory. An advantage of the game-theoretic versions over the measure-theoretic results…

Probability · Mathematics 2007-05-23 Vladimir Vovk

Relative notions of combinatorial asphericity have been used to prove that injective labeled oriented trees (which encode spines of ribbon 2-knots) are aspherical. This article presents an overview and comparison of the different notions of…

Geometric Topology · Mathematics 2021-01-19 Stephan Rosebrock , Jens Harlander

The theory of probability and the quantum theory, the one mathematical and the other physical, are related in that each admits a number of very different interpretations. It has been proposed that the conceptual problems of the quantum…

Quantum Physics · Physics 2021-08-29 Louis Marchildon

Prediction is a complex notion, and different predictors (such as people, computer programs, and probabilistic theories) can pursue very different goals. In this paper I will review some popular kinds of prediction and argue that the theory…

Machine Learning · Computer Science 2007-05-23 Vladimir Vovk

The major challenge in designing a discriminative learning algorithm for predicting structured data is to address the computational issues arising from the exponential size of the output space. Existing algorithms make different assumptions…

Machine Learning · Computer Science 2010-06-29 Shankar Vembu

We give an introductory account of the general boundary formulation of quantum theory. We refine its probability interpretation and emphasize a conceptual and historical perspective. We give motivations from quantum gravity and illustrate…

High Energy Physics - Theory · Physics 2012-08-06 Robert Oeckl

The aim of this work is to provide a unified framework for ordinal representations of uncertainty lying at the crosswords between possibility and probability theories. Such confidence relations between events are commonly found in monotonic…

Artificial Intelligence · Computer Science 2012-08-07 Didier Dubois , Helene Fargier

I take a quick overview at the recent development of combinatorics and its current directions, as a discipline in its own right, as part of mathematics, and as part of science and wider society.

History and Overview · Mathematics 2011-11-18 Peter J. Cameron

We propose a way to connect complex analysis and convex analysis. As applications, we derive some results about $L^2$-estimate for $d$-equation and prove some curvature positivity related to convex analysis from well known $L^2$-estimate…

Complex Variables · Mathematics 2025-03-04 Fusheng Deng , Jinjin Hu , Weiwen Jiang , Xiangsen Qin

Several recent works have developed a new, probabilistic interpretation for numerical algorithms solving linear systems in which the solution is inferred in a Bayesian framework, either directly or by inferring the unknown action of the…

Computation · Statistics 2018-10-18 Simon Bartels , Jon Cockayne , Ilse C. F. Ipsen , Philipp Hennig

We describe a viewpoint on the Dempster/Shafer 'Theory of Evidence', and provide an interpretation which regards the combination formulas as statistics of the opinions of "experts". This is done by introducing spaces with binary operations…

Artificial Intelligence · Computer Science 2013-04-12 Robert Hummel , Michael Landy

This paper has been withdrawn. With the advancement of statistical theory and computing power, data sets are providing a greater amount of insight into the problems of today. Statisticians have an ever increasing number of tools to attack…

Statistics Theory · Mathematics 2012-12-20 Derek S. Young

A combinatorial methods are used to investigate some properties of certain generalized Stirling numbers, including explicit formula and recurrence relations. Furthermore, an expression of these numbers with symmetric function is deduced.

Combinatorics · Mathematics 2014-11-25 Hacène Belbachir , Amine Belkhir , Imad Eddine Bousbaa

Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further…

Social and Information Networks · Computer Science 2014-09-18 Fei Tan , Yongxiang Xia , Boyao Zhu

In this short review we introduce group field theory, a particular class of random tensor models, which represents nowadays one of the candidates for a fundamental theory of quantum gravity. We insist on the combinatorial richness of…

Combinatorics · Mathematics 2012-04-11 Adrian Tanasa

We present a few combinatorial identities which were encountered in our work on the spectral theory of quantum graphs. They establish a new connection between the theory of random matrix ensembles and combinatorics.

Mathematical Physics · Physics 2007-05-23 Holger Schanz , Uzy Smilansky

Evaluating joint probabilities of potential outcomes and observed variables, and their linear combinations, is a fundamental challenge in causal inference. This paper addresses the bounding and identification of these probabilities in…

Machine Learning · Statistics 2026-02-24 Naoya Hashimoto , Yuta Kawakami , Jin Tian