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Related papers: Notes on risk theory

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A key issue in the handling of temporal data is the treatment of persistence; in most approaches it consists in inferring defeasible confusions by extrapolating from the actual knowledge of the history of the world; we propose here a…

Artificial Intelligence · Computer Science 2013-03-08 Dimiter Driankov , Jerome Lang

This is the logical foundation for for Relativity Theory, Probability Theory, and for Quantum Theory. Contents is the following: 1 Introduction. 2 Classical logic. 3 Time and space. 3.1 Recorders. 3.2 Time. 3.3 Space. 3.4 Relativity. 4.…

General Physics · Physics 2007-05-23 G. A. Quznetsov

Causal theory is now widely developed with many applications to medicine and public health. However within the discipline of reliability, although causation is a key concept in this field, there has been much less theoretical attention. In…

Artificial Intelligence · Computer Science 2020-02-17 Xuewen Yu , Jim Q. Smith , Linda Nichols

This paper deals with sequences of random variables belonging to a fixed chaos of order $q$ generated by a Poisson random measure on a Polish space. The problem is investigated whether convergence of the third and fourth moment of such a…

Probability · Mathematics 2016-08-10 Tobias Fissler , Christoph Thaele

The perturbation theory based on typicality introduced in Ref. [1] and further refined in Refs. [2, 3] provides a powerful tool since it is intended to be applicable to a wide range of scenarios while relying only on a few parameters. Even…

Quantum Physics · Physics 2022-12-07 Mats H. Lamann , Jochen Gemmer

After experimenting with a number of non-probabilistic methods for dealing with uncertainty many researchers reaffirm a preference for probability methods [1] [2], although this remains controversial. The importance of being able to form…

Artificial Intelligence · Computer Science 2013-04-11 Thomas Slack

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

We consider a classical risk process with arrival of claims following a non-stationary Hawkes process. We study the asymptotic regime when the premium rate and the baseline intensity of the claims arrival process are large, and claim size…

Risk Management · Quantitative Finance 2019-08-22 Zailei Cheng , Youngsoo Seol

We describe a statistical hypothesis test for the presence of a signal based on the likelihood ratio statistic. We derive the test for a special case of interest. We study extensions of the test to cases where there are multiple channels…

Data Analysis, Statistics and Probability · Physics 2008-07-31 Wolfgang A. Rolke , Angel M. Lopez

In this paper we propagate a large deviations approach for proving limit theory for (generally) multivariate time series with heavy tails. We make this notion precise by introducing regularly varying time series. We provide general large…

Statistics Theory · Mathematics 2015-09-02 T. Mikosch , O. Wintenberger

In a previous work, two of the authors proposed a new proof of a well known convergence result for the scaled elementary connected vacant component in the high intensity Boolean model towards the Crofton cell of the Poisson hyperplane…

Probability · Mathematics 2009-05-29 Pierre Calka , Julien Michel , Katy Paroux

A birth-death process is a continuous-time Markov chain that counts the number of particles in a system over time. In the general process with $n$ current particles, a new particle is born with instantaneous rate $\lambda_n$ and a particle…

Populations and Evolution · Quantitative Biology 2012-10-11 Forrest W. Crawford , Marc A. Suchard

The paper studies a probabilistic notion of causes in Markov chains that relies on the counterfactuality principle and the probability-raising property. This notion is motivated by the use of causes for monitoring purposes where the aim is…

Logic in Computer Science · Computer Science 2021-07-09 Christel Baier , Florian Funke , Simon Jantsch , Jakob Piribauer , Robin Ziemek

We establish a link between the phenomenon of Taylor dispersion and the theory of empirical distributions. Using this connection, we derive, upon applying the theory of large deviations, an alternative and much more precise description of…

Statistical Mechanics · Physics 2017-02-01 Marcel Kahlen , Andreas Engel , Christian Van den Broeck

We establish a large deviation principle for the trajectories of Wiener processes subject to random resets to the origin occurring according to a Poisson process. In addition to the pathwise large deviation principle, we identify the rate…

Probability · Mathematics 2025-12-09 A. V. Logachov , O. M. Logachova , A. A. Yambartsev , K. A. Zaykov

In this short note, we derive explicit formulas for the joint densities of the time to ruin and the number of claims until ruin in perturbed classical risk models, by constructing several auxiliary random processes.

Probability · Mathematics 2016-08-22 Peng Liu , Chunsheng Zhang , Lanpeng Ji

We establish stability criterion for a two-class retrial system with Poisson inputs, general class-dependent service times and class-dependent constant retrial rates. We also characterise an interesting phenomenon of partial stability when…

Probability · Mathematics 2021-10-20 Konstantin Avrachenkov , Evsey Morozov , Ruslana Nekrasova

In this paper we give few expressions and asymptotics of ruin probabilities for a Markov modulated risk process for various regimes of a time horizon, initial reserves and a claim size distribution. We also consider few versions of the ruin…

Probability · Mathematics 2021-10-05 Zbigniew Palmowski

The probability axioms by R. T. Cox can be regarded as the modern foundations of Bayesian inference, the idea of assigning degrees of belief to logical propositions in a manner consistent with Boolean logic. In this work it is shown that…

Probability · Mathematics 2016-07-28 Sergio Davis

A new variant of Newton's method for empirical risk minimization is studied, where at each iteration of the optimization algorithm, the gradient and Hessian of the objective function are replaced by robust estimators taken from existing…

Machine Learning · Statistics 2023-07-18 Eirini Ioannou , Muni Sreenivas Pydi , Po-Ling Loh
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