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Many representation schemes combining first-order logic and probability have been proposed in recent years. Progress in unifying logical and probabilistic inference has been slower. Existing methods are mainly variants of lifted variable…

Artificial Intelligence · Computer Science 2012-02-20 Vibhav Gogate , Pedro Domingos

Attempts to replicate probabilistic reasoning in expert systems have typically overlooked a critical ingredient of that process. Probabilistic analysis typically requires extensive judgments regarding interdependencies among hypotheses and…

Artificial Intelligence · Computer Science 2013-04-15 Marvin S. Cohen

This chapter presents probability logic as a rationality framework for human reasoning under uncertainty. Selected formal-normative aspects of probability logic are discussed in the light of experimental evidence. Specifically, probability…

Artificial Intelligence · Computer Science 2019-10-16 Niki Pfeifer

Within the Kolmogorov theory of probability, Bayes' rule allows one to perform statistical inference by relating conditional probabilities to unconditional probabilities. As we show here, however, there is a continuous set of alternative…

Probability · Mathematics 2014-12-05 Samuel G. Rodriques

We study approximation in the unit interval by rational numbers whose numerators are selected randomly with certain probabilities. Previous work showed that an analogue of Khintchine's Theorem holds in a similar random model and raised the…

Number Theory · Mathematics 2019-09-17 Laima Kaziulytė , Felipe A. Ramírez

Computing the probability of a formula given the probabilities or weights associated with other formulas is a natural extension of logical inference to the probabilistic setting. Surprisingly, this problem has received little attention in…

Artificial Intelligence · Computer Science 2012-03-19 Vibhav Gogate , Pedro Domingos

Recently, topic modeling has been widely used to discover the abstract topics in text corpora. Most of the existing topic models are based on the assumption of three-layer hierarchical Bayesian structure, i.e. each document is modeled as a…

Computation and Language · Computer Science 2017-04-10 Yi-Kun Tang , Xian-Ling Mao , Heyan Huang , Guihua Wen

This report introduces general ideas and some basic methods of the Bayesian probability theory applied to physics measurements. Our aim is to make the reader familiar, through examples rather than rigorous formalism, with concepts such as:…

Data Analysis, Statistics and Probability · Physics 2009-11-10 G. D'Agostini

We consider the relation between various permutation statistics and properties of permutation tableaux. We answer some of the questions of Steingrimsson and Williams (math.CO/0507149), in particular, on the distribution of the bistatistic…

Combinatorics · Mathematics 2007-05-23 Alexander Burstein

In this article we provide a substantial discussion on the statistical concept of conditional independence, which is not routinely mentioned in most elementary statistics and mathematical statistics textbooks. Under the assumption of…

Other Statistics · Statistics 2020-03-10 Jun Hu , Xianggui Qu

Advances in the general capabilities of large language models (LLMs) have led to their use for information retrieval, and as components in automated decision systems. A faithful representation of probabilistic reasoning in these models may…

Artificial Intelligence · Computer Science 2025-04-21 Gabriel Freedman , Francesca Toni

As the amount of linked data published on the web grows, attempts are being made to describe and measure it. However even basic statistics about a graph, such as its size, are difficult to express in a uniform and predictable way. In order…

Databases · Computer Science 2014-10-21 William Waites

This is a contribution for the discussion on "A Gibbs sampler for a class of random convex polytopes" by Pierre E. Jacob, Ruobin Gong, Paul T. Edlefsen and Arthur P. Dempster to appear in the Journal of American Statistical Association.

Computation · Statistics 2021-04-16 Persi Diaconis , Guanyang Wang

Maximum entropy principle identifies forces conjugated to observables and the thermodynamic relations between them, independent upon their underlying mechanistic details. For data about state distributions or transition statistics, the…

Statistical Mechanics · Physics 2023-12-08 Ying-Jen Yang , Hong Qian

Although the specification of bivariate probability models using a collection of assumed conditional distributions is not a novel concept, it has received considerable attention in the last decade. In this study, a bivariate…

Methodology · Statistics 2025-03-20 Indranil Ghosh , Mina Norouzirad , Filipe J. Marques

A new multivariate distribution possessing arbitrarily parametrized and positively dependent univariate Pareto margins is introduced. Unlike the probability law of Asimit et al. (2010) [Asimit, V., Furman, E. and Vernic, R. (2010) On a…

Risk Management · Quantitative Finance 2016-07-19 Jianxi Su , Edward Furman

We prove a multivariate version of Bernstein's inequality about the probability that degenerate $U$-statistics take a value larger than some number $u$. This is an improvement of former estimates for the same problem which yields an…

Probability · Mathematics 2007-05-23 P. Major

This paper is an attempt to bring together two approaches to language analysis. The possible use of probabilistic information in principle-based grammars and parsers is considered, including discussion on some theoretical and computational…

cmp-lg · Computer Science 2008-02-03 Andrew Fordham , Matthew Crocker

A popular scientific contribution should not contradict any established facts and ought to be understandable. I complied with both these requirements and am offering a sufficiently full introduction to probability theory. Furthermore, I…

History and Overview · Mathematics 2018-02-13 Oscar Sheynin

Comment on ``Gibbs Sampling, Exponential Families, and Orthogonal Polynomials'' [arXiv:0808.3852]

Methodology · Statistics 2008-08-29 Galin L. Jones , Alicia A. Johnson