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This paper describes a method for creating compelling safety cases. The method seeks to help improve safety case practice in order to address the weaknesses identified in current practice, in particular confirmation bias, after-the-fact…

Software Engineering · Computer Science 2025-02-04 Richard Hawkins

In logic there is a clear concept of what constitutes a proof and what not. A proof is essentially defined as a finite sequence of formulae which are either axioms or derived by proof rules from formulae earlier in the sequence.…

Artificial Intelligence · Computer Science 2010-05-28 Manfred Kerber

Admissable weight is an important tool for studying spectral invariance in operator algebra. Common admissable weights include polynomial weights and sub exponential weights. This article mainly provides a proof that polynomial weights are…

Functional Analysis · Mathematics 2025-06-25 Hao Yu

A significant amount of research has considered mathematical proofs, the students who learn them, and the instructors that teach them, from a variety of perspectives. This paper considers this topic from four main perspectives: students'…

History and Overview · Mathematics 2018-06-20 Christopher M. van Bommel

We reveal a connection between the incompressibility method and the Lovasz local lemma in the context of Ramsey theory. We obtain bounds by repeatedly encoding objects of interest and thereby compressing strings. The method is demonstrated…

Combinatorics · Mathematics 2008-04-07 Pascal Schweitzer

Bayesian methods are useful for statistical inference. However, real-world problems can be challenging using Bayesian methods when the data analyst has only limited prior knowledge. In this paper we consider a class of problems, called…

Methodology · Statistics 2019-11-20 Yixuan Qiu , Lingsong Zhang , Chuanhai Liu

While proof is a central component of postsecondary mathematical study, proof construction has historically posed significant difficulties for students who intend to earn mathematics degrees at the undergraduate level. This work is…

History and Overview · Mathematics 2023-03-01 Bolanle Salaam

Plausible reasoning concerns situations whose inherent lack of precision is not quantified; that is, there are no degrees or levels of precision, and hence no use of numbers like probabilities. A hopefully comprehensive set of principles…

Artificial Intelligence · Computer Science 2017-04-05 David Billington

A brief discussion is given of the traditional version of the Maximum Entropy Method, including a review of some of the criticism that has been made in regard to its use in statistical inference. Motivated by these questions, a modified…

Data Analysis, Statistics and Probability · Physics 2007-09-12 Robert Kariotis

I present a critique of the methods used in a typical paper. This leads to three broad conclusions about the conventional use of statistical methods. First, results are often reported in an unnecessarily obscure manner. Second, the null…

Applications · Statistics 2013-03-05 Michael Wood

The main aim of this paper is to promote a certain style of doing coinductive proofs, similar to inductive proofs as commonly done by mathematicians. For this purpose, we provide a reasonably direct justification for coinductive proofs…

Logic in Computer Science · Computer Science 2019-05-24 Łukasz Czajka

The method of exhaustion is generalized to a simple formula that can be used to integrate functions under very general conditions, provided that the integral exists. Both a geometric proof (following the usual procedure for the method of…

Classical Analysis and ODEs · Mathematics 2007-05-23 Anthony A. Ruffa

Empirical science needs to be based on facts and claims that can be reproduced. This calls for replicating the studies that proclaim the claims, but practice in most fields still fails to implement this idea. When such studies emerged in…

Other Statistics · Statistics 2025-08-27 Werner A. Stahel

A wide variety of model explanation approaches have been proposed in recent years, all guided by very different rationales and heuristics. In this paper, we take a new route and cast interpretability as a statistical inference problem. We…

Machine Learning · Computer Science 2024-01-01 Hugo Henri Joseph Senetaire , Damien Garreau , Jes Frellsen , Pierre-Alexandre Mattei

Current machine learning models are evaluated through behavioral snapshots, with benchmark accuracies, win rates and outcome-based metrics. Model explanations and evaluations, however, are fundamentally intertwined: understanding why a…

Computers and Society · Computer Science 2026-05-08 Isabelle Lee , Emmy Liu , Cathy Jiao , Brihi Joshi , Dani Yogatama , Fazl Barez , Michael Saxon

We present a method to simplify expressions in the context of an equational theory. The basic ideas and concepts of the method have been presented previously elsewhere but here we tackle the difficult task of making it efficient in…

Logic in Computer Science · Computer Science 2020-03-16 Baudouin Le Charlier

Explainability is one of the key ethical concepts in the design of AI systems. However, attempts to operationalize this concept thus far have tended to focus on approaches such as new software for model interpretability or guidelines with…

Computers and Society · Computer Science 2020-10-06 Ben Zevenbergen , Allison Woodruff , Patrick Gage Kelley

We consider several examples of probabilistic existence proofs using compressibility arguments, including some results that involve Lov\'asz local lemma.

Discrete Mathematics · Computer Science 2017-03-10 Alexander Shen

Argumentation is the process of constructing arguments about propositions, and the assignment of statements of confidence to those propositions based on the nature and relative strength of their supporting arguments. The process is modelled…

Artificial Intelligence · Computer Science 2013-03-08 John Fox , Paul J. Krause , Morten Elvang-Gøransson

Recently, there has been considerable progress on designing algorithms with provable guarantees -- typically using linear algebraic methods -- for parameter learning in latent variable models. But designing provable algorithms for inference…

Machine Learning · Computer Science 2016-05-30 Sanjeev Arora , Rong Ge , Frederic Koehler , Tengyu Ma , Ankur Moitra
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