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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. While the theory of modeling joint distributions via copulas is well understood, it gets practically challenging to accurately model real data ...
Elton Yechao Zhu +11 more
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Characterizing a Joint Probability Distribution by Conditionals
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1993 SUMMARY We derive conditions under which a set of conditional and marginal probability distributions will uniquely specify an all-positive joint distribution. Our theoretical result may yield insights into the construction and simulation of multivariate probability models.
Gelman, Andrew, Speed, T. P.
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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
semanticscholar +1 more source
Brain PET Synthesis from MRI Using Joint Probability Distribution of Diffusion Model at Ultrahigh Fields [PDF]
ISMRM Annual Meeting, 2022 MRI and PET are important modalities and can provide complementary information for the diagnosis of brain diseases because MRI can provide structural information of brain and PET can obtain functional information of brain. However, PET is usually missing.
Taofeng Xie +10 more
semanticscholar +1 more source
How to find a joint probability distribution of minimum entropy (almost) given the marginals [PDF]
International Symposium on Information Theory, 2017 Given two discrete random variables X and Y, with probability distributions p = (p1, …, pn) and q = (q1, …, qm), respectively, denote by C(p, q) the set of all joint distributions of X and Y that have p and q as marginals.
F. Cicalese, L. Gargano, U. Vaccaro
semanticscholar +1 more source
Wind-ice Joint Probability Distribution Analysis based on Copula Function
Journal of Physics: Conference Series, 2020 Taking the wind speed and ice thickness field measurement data in Southwest China during November 2016 to March 2018 as analytical sample, the probability distribution of the wind speed and ice intensity were analyzed and fitted by using Gumbel ...
Fengli Yang +3 more
semanticscholar +1 more source
Joint probability distributions and fluctuation theorems [PDF]
Journal of Statistical Mechanics: Theory and Experiment, 2012 We derive various exact results for Markovian systems that spontaneously relax to a non-equilibrium steady-state by using joint probability distributions symmetries of different entropy production decompositions. The analytical approach is applied to diverse problems such as the description of the fluctuations induced by experimental errors, for ...
García-García, Reinaldo +3 more
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Proceedings of the Combustion Institute, 2019 The method of uncertainty minimization by polynomial chaos expansions is extended to Arrhenius prefactor and activation energy co-optimization and uncertainty minimization.
Yujie Tao, Hai Wang
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Fast Hadamard transforms for compressive sensing of joint systems: measurement of a 3.2 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
semanticscholar +1 more source
Joint Distributions for TensorFlow Probability
, 2020 Based on extended abstract submitted to PROBPROG ...
Piponi, Dan +2 more
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