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We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to a large class of discrete inference problems, even with infinite support and continuous priors. To express such models, we…

Programming Languages · Computer Science 2023-11-08 Fabian Zaiser , Andrzej S. Murawski , Luke Ong

Automated theorem provers are now commonly used within interactive theorem provers to discharge an increasingly large number of proof obligations. To maintain the trustworthiness of a proof, the automatically found proof must be verified…

Logic in Computer Science · Computer Science 2019-08-27 Mathias Fleury , Hans-Jörg Schurr

We describe a "top down" approach for automated theorem proving (ATP). Researchers might usefully investigate the forms of the theorems mathematicians use in practice, carefully examine how they differ and are proved in practice, and code…

Logic in Computer Science · Computer Science 2023-08-09 C. E. Larson , N. Van Cleemput

This monograph offers a toolbox of mathematical techniques, which have been effective and widely applicable in information-theoretic analysis. The first tool is a generalization of the method of types to Gaussian settings, and then to…

Information Theory · Computer Science 2024-06-04 Neri Merhav , Nir Weinberger

The use of deductive techniques, such as theorem provers, has several advantages in safety verification of hybrid sys- tems; however, state-of-the-art theorem provers require ex- tensive manual intervention. Furthermore, there is often a…

Logic in Computer Science · Computer Science 2021-09-08 Nikos Arechiga , James Kapinski , Jyotirmoy Deshmukh , Andre Platzer , Bruce Krogh

Parikh's Theorem is a fundamental result in automata theory with numerous applications in computer science: software verification (e.g. infinite-state verification, string constraints, and theory of arrays), verification of cryptographic…

Formal Languages and Automata Theory · Computer Science 2024-08-01 Matthew Hague , Artur Jeż , Anthony W. Lin

In this paper we use the Vandermonde matrices and their properties to give a new proof of the classical result of Karl Weierstrass about the approximation of continuous functions $f$ on closed intervals, using a sequence of polynomials. The…

Classical Analysis and ODEs · Mathematics 2025-07-02 José M. González Barrios , Alberto Contreras-Cristán , Patricia I. Romero-Mares

In this present paper, I propose a derivation of unified interpolation and extrapolation function that predicts new values inside and outside the given range by expanding direct Taylor series on the middle point of given data set.…

Numerical Analysis · Mathematics 2020-02-27 Nijat Shukurov

In this paper, we derive a variant of the Taylor theorem to obtain a new minimized remainder. For a given function $f$ defined on the interval $[a,b]$, this formula is derived by introducing a linear combination of $f'$ computed at $n+1$…

Numerical Analysis · Mathematics 2023-08-04 J. Chaskalovic , F. Assous

We present the Verse library with the aim of making hybrid system verification more usable for multi-agent scenarios. In Verse, decision making agents move in a map and interact with each other through sensors. The decision logic for each…

Software Engineering · Computer Science 2023-01-24 Yangge Li , Haoqing Zhu , Katherine Braught , Keyi Shen , Sayan Mitra

We present LISA, a proof system and proof assistant for constructing proofs in schematic first-order logic and axiomatic set theory. The logical kernel of the system is a proof checker for first-order logic with equality and schematic…

Logic in Computer Science · Computer Science 2025-07-16 Simon Guilloud , Sankalp Gambhir , Viktor Kunčak

FV Time is a small-scale verification project developed in the Coq proof assistant using the Mathematical Components libraries. It is a library for managing conversions between time formats (UTC and timestamps), as well as commonly used…

In the context of data-driven control of nonlinear systems, many approaches lack of rigorous guarantees, call for nonconvex optimization, or require knowledge of a function basis containing the system dynamics. To tackle these drawbacks, we…

Systems and Control · Electrical Eng. & Systems 2023-10-05 Tim Martin , Frank Allgöwer

Model counting is the problem of computing the number of models that satisfy a given propositional theory. It has recently been applied to solving inference tasks in probabilistic logic programming, where the goal is to compute the…

Artificial Intelligence · Computer Science 2014-11-21 Rehan Abdul Aziz , Geoffrey Chu , Christian Muise , Peter Stuckey

Theorem provers has been used extensively in software engineering for software testing or verification. However, software is now so large and complex that additional architecture is needed to guide theorem provers as they try to generate…

Software Engineering · Computer Science 2021-01-11 Jianfeng Chen , Xipeng Shen , Tim Menzies

Proving lemmas in synthetic geometry is often a time-consuming endeavour since many intermediate lemmas need to be proven before interesting results can be obtained. Improvements in automated theorem provers (ATP) in recent years now mean…

Logic in Computer Science · Computer Science 2019-04-03 Maximilian Doré , Krysia Broda

In interactive theorem provers (ITPs), extensible syntax is not only crucial to lower the cognitive burden of manipulating complex mathematical objects, but plays a critical role in developing reusable abstractions in libraries. Most ITPs…

Programming Languages · Computer Science 2023-06-22 Sebastian Ullrich , Leonardo de Moura

This book is about conformal prediction and related inferential techniques that build on permutation tests and exchangeability. These techniques are useful in a diverse array of tasks, including hypothesis testing and providing uncertainty…

Statistics Theory · Mathematics 2026-03-09 Anastasios N. Angelopoulos , Rina Foygel Barber , Stephen Bates

The matrix exponential is a fundamental operator in scientific computing and system simulation, with applications ranging from control theory and quantum mechanics to modern generative machine learning. While Pad\'e approximants combined…

Machine Learning · Computer Science 2026-01-12 Jorge Sastre , Daniel Faronbi , José Miguel Alonso , Peter Traver , Javier Ibáñez , Nuria Lloret

We describe an extension of the Taylor method for the numerical solution of ODEs that uses Pad\'e approximants to obtain extremely precise numerical results. The accuracy of the results is essentially limited only by the computer time and…

Computational Physics · Physics 2022-05-04 Paolo Amore