绊住
发表于 2025-3-28 17:46:47
Maximum Entropy Distributions with Prescribed Marginals and Normal Score Correlations,ces of uncertainty are for instance: initial values, boundary conditions, parameter values, exogenous variables. Often, uncertainties are accounted for by randomness of these sources. Thus, the specification of the multivariate probability distribution of the sources constitutes a basic problem of u
为敌
发表于 2025-3-28 22:35:36
,On Bivariate Distributions with Polya-Aeppli or Lüders-Delaporte Marginals,erpretation of random phenomena in biological and actuarial problems, among others. When a univariate distribution arises as a mixture, ‘natural’ bivariate versions of this distribution can often be introduced, using ‘mixing’ procedures.
假装是我
发表于 2025-3-29 02:41:30
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Bombast
发表于 2025-3-29 03:41:28
On Approximation of Copulas,most commonly used limit for copulas is the uniform limit. However the uniform limit is not completely satisfactory in that, on the one hand, any copula can be approximated arbitrarily closely by invertible copulas and, on the other hand, the * operation of Darsow, Nguyen, and Olsen is not jointly c
CHURL
发表于 2025-3-29 09:10:22
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Intractable
发表于 2025-3-29 11:55:52
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Cantankerous
发表于 2025-3-29 18:48:59
Continuous Scaling on a Bivariate Copula,ability of a random variable in a countable set of principal axes. (1996a) introduced related metric scaling, a technique to construct a joint distance on a finite set from two marginal distances, with applications to representing categorical data. In this contribution related metric scaling is exte
TRUST
发表于 2025-3-29 22:59:18
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blackout
发表于 2025-3-30 01:47:53
How to Construct a Two Dimensional Random Vector with a Given Conditional Structure,is called a .-vector, a rich .-vector and an extremal .-vector if (almost surely w.r.t . has the maximal possible support among the distributions in . is an extremal measure in ., respectively. The purpose of the paper is to find sufficient conditions for the existence of .-vectors, to cover the sit
PANIC
发表于 2025-3-30 07:41:48
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