Generative quantum learning of joint probability distribution functions [PDF]
Physical Review Research, 2022 Modeling joint probability distributions is an important task in a wide variety of fields. One popular technique for this employs a family of multivariate distributions with uniform marginals called copulas.
Elton Yechao Zhu +11 more
doaj +6 more sources
Bayes Classification using an approximation to the Joint Probability Distribution of the Attributes [PDF]
Delta, 2022 The Naive-Bayes classifier is widely used due to its simplicity, speed and accuracy. However this approach fails when, for at least one attribute value in a test sample, there are no corresponding training samples with that attribute value. This is known
Patrick Hosein, Kevin Baboolal
openalex +3 more sources
Construction of Joint Probability Distributions [PDF]
The Annals of Mathematical Statistics, 1968 Let Fi(x) and F2(y) be the distribution functions of two random variables. Frechet proved that the family of joint distributions having Fi(x) and F2(y) as marginal distributions collapses to F1(x)F2(y) if and only if either F,(x) or F2(y) is a unit step function.
L. F. Kemp
openalex +3 more sources
Fast Hadamard transforms for compressive sensing of joint systems: measurement of a 32 million-dimensional bi-photon probability distribution [PDF]
Optics Express, 2015 We demonstrate how to efficiently implement extremely high-dimensional compressive imaging of a bi-photon probability distribution. Our method uses fast-Hadamard-transform Kronecker-based compressive sensing to acquire the joint space distribution.
Daniel J. Lum +2 more
openalex +2 more sources
Joint probability calculation of the lateral velocity distribution in strong field ionization process [PDF]
Scientific Reports, 2022 We describe an approach to the description of the time-development of the process of strong field ionization of atoms based on the calculation of the joint probability of occurrence of two events, event B being finding atom in the ionized state after the
I. A. Ivanov, Kyung Taec Kim
doaj +2 more sources
Journal of Hydrology and Hydromechanics, 2014 River runoff and sediment transport are two related random hydrologic variables. The traditional statistical analysis method usually requires those two variables to be linearly correlated, and also have an identical marginal distribution.
Zhang Jinping, Ding Zhihong, You Jinjun
doaj +2 more sources
Copula-based multivariate analysis of hydrological drought over jiabharali sub-basin of Brahmaputra River, India [PDF]
Scientific ReportsIn this study, copula-based multivariate hydrological drought analysis was carried out in the Jiabharali (Kameng) River in Arunachal Pradesh, a sub-tributary of Brahmaputra River, India. Different drought characteristics – severity (S), duration (D), and
Bivek Chakma +7 more
doaj +2 more sources
Some Properties of Joint Probability Distributions [PDF]
, 2013 Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994)
Marek J. Drużdżel
openalex +4 more sources
Nonextensive distribution and factorization of the joint probability [PDF]
Chaos, Solitons & Fractals, 2000 The problem of factorization of a nonextensive probability distribution is discussed. It is shown that, in general, the correlation energy between the correlated subsystems in the canonical composite system can not be neglected even in the thermodynamic limit.
Q.A. Wang +3 more
openalex +4 more sources
Unifying approach for fluctuation theorems from joint probability distributions [PDF]
Physical Review E, 2010 Any decomposition of the total trajectory entropy production for Markovian systems has a joint probability distribution satisfying a generalized detailed fluctuation theorem, when all the contributing terms are odd with respect to time reversal. The expression of the result does not bring into play dual probability distributions, hence easing potential
Reinaldo García‐García +3 more
openalex +6 more sources