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Related papers: Naive probability

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The primary research questions of this paper center on defining the amount of context that is necessary and/or appropriate when investigating the relationship between language model probabilities and cognitive phenomena. We investigate…

Computation and Language · Computer Science 2026-01-07 Cassandra L. Jacobs , Andrés Buxó-Lugo , Anna K. Taylor , Marie Leopold-Hooke

We develop a random model for relation algebras. We prove some preliminary results and pose questions that lay out a new direction of research.

Combinatorics · Mathematics 2018-02-20 Jeremy F. Alm

Fusion is defined for arbitrary lowest weight representations of $W$-algebras, without assuming rationality. Explicit algorithms are given. A category of quasirational representations is defined and shown to be stable under fusion.…

High Energy Physics - Theory · Physics 2011-07-18 Werner Nahm

This is an introduction to some of the most probabilistic aspects of free probability theory.

Probability · Mathematics 2016-09-07 Philippe Biane

The language of probability is used to define several different types of conditional statements. There are four principal types: subjunctive, material, existential, and feasibility. Two further types of conditionals are defined using the…

Logic · Mathematics 2014-09-29 Joseph W. Norman

Works, briefly surveyed here, are concerned with two basic methods: Maximum Probability and Bayesian Maximum Probability; as well as with their asymptotic instances: Relative Entropy Maximization and Maximum Non-parametric Likelihood.…

Statistics Theory · Mathematics 2008-04-25 M. Grendar

Coherent lower previsions are general probabilistic models allowing incompletely specified probability distributions. However, for complete description of a coherent lower prevision -- even on finite underlying sample spaces -- an infinite…

Probability · Mathematics 2022-09-29 Damjan Škulj

Parametric inference posits a statistical model that is a specified family of probability distributions. Restricted inference, e.g., restricted likelihood ratio testing, attempts to exploit the structure of a statistical submodel that is a…

Statistics Theory · Mathematics 2019-03-22 Michael W. Trosset , Carey E. Priebe

In this paper, I will demonstrate a new perspective on the Two Envelope Problem. I hope to show with convincing clarity how the paradox results from an inherent problem pertaining to the interpretation of Bayesian probability. Specifically,…

Other Statistics · Statistics 2012-08-27 Eric Bliss

Given a probability distribution $\mu$ a set $\Lambda (\mu)$ of positive real numbers is introduced, so that $\Lambda (\mu)$ measures the "divisibility" of $\mu$. The basic properties of $\Lambda (\mu)$ are described and examples of…

Probability · Mathematics 2007-05-23 S. Albeverio , H. Gottschalk , J. -L. Wu

The systematic biases seen in people's probability judgments are typically taken as evidence that people do not reason about probability using the rules of probability theory, but instead use heuristics which sometimes yield reasonable…

Data Analysis, Statistics and Probability · Physics 2014-05-01 Fintan Costello , Paul Watts

We propose a likelihood ratio statistic for forming hypothesis tests and confidence intervals for a nonparametrically estimated univariate regression function, based on the shape restriction of concavity (alternatively, convexity). Dealing…

Statistics Theory · Mathematics 2018-09-11 Charles R. Doss

Specifying a Bayesian prior is notoriously difficult for complex models such as neural networks. Reasoning about parameters is made challenging by the high-dimensionality and over-parameterization of the space. Priors that seem benign and…

Machine Learning · Statistics 2020-10-22 Eric Nalisnick , Jonathan Gordon , José Miguel Hernández-Lobato

(l) I have enough evidence to render the sentence S probable. (la) So, relative to what I know, it is rational of me to believe S. (2) Now that I have more evidence, S may no longer be probable. (2a) So now, relative to what I know, it is…

Artificial Intelligence · Computer Science 2016-11-26 Henry E. Kyburg

Likelihood profiling is an efficient and powerful frequentist approach for parameter estimation, uncertainty quantification and practical identifiablity analysis. Unfortunately, these methods cannot be easily applied for stochastic models…

Ontological models are attempts to quantitatively describe the results of a probabilistic theory, such as Quantum Mechanics, in a framework exhibiting an explicit realism-based underpinning. Unlike either the well known quasi-probability…

Quantum Physics · Physics 2008-07-02 Nicholas Harrigan , Terry Rudolph , Scott Aaronson

In this paper we present three simple applications of probability and highlight and discuss their paradoxical flavour.

History and Overview · Mathematics 2007-05-24 Germano D'Abramo , Barbara D'Abramo

Statistical inference with nonresponse is quite challenging, especially when the response mechanism is nonignorable. The existing methods often require correct model specifications for both outcome and response models. However, due to…

Methodology · Statistics 2018-09-12 Hejian Sang , Kosuke Morikawa

This paper presents a thoroughgoing interpretation of a weak relevant logic built over the Dunn-Belnap four-valued semantics in terms of the communication of information in a network of sites of knowledge production (laboratories). The…

Logic · Mathematics 2021-01-12 Timothy Childers , Ondrej Majer , Peter Milne

We relate a previous result of ours on families of Diophantine equations having only trivial solutions with a result on the approximation of an algebraic number by products of rational numbers and units. We compare this approximation with a…

Number Theory · Mathematics 2013-12-30 Claude Levesque , Michel Waldschmidt