Results 21 to 30 of about 1,381,183 (287)
Quantum Fourier transform, Heisenberg groups and quasiprobability distributions [PDF]
, 2011 This paper aims to explore the inherent connection among Heisenberg groups, quantum Fourier transform and (quasiprobability) distribution functions. Distribution functions for continuous and finite quantum systems are examined first as a semiclassical ...
Arthurs E +20 more
core +2 more sources
IEEE Access, 2021 Distribution locational marginal pricing (DLMP) is an increasingly popular pricing signal that can be used to incentivize grid-friendly behavior of distributed energy resources (DER) to optimize economic efficiency in distribution grids. In this paper, a
Uvin Liyanapathirane +2 more
doaj +1 more source
Marginally Calibrated Deep Distributional Regression [PDF]
Journal of Computational and Graphical Statistics, 2020 Deep neural network (DNN) regression models are widely used in applications requiring state-of-the-art predictive accuracy. However, until recently there has been little work on accurate uncertainty quantification for predictions from such models. We add to this literature by outlining an approach to constructing predictive distributions that are ...
Nadja Klein +2 more
openaire +3 more sources
Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors
Sensors, 2017 Issues of missing data have become increasingly serious with the rapid increase in usage of traffic sensors. Analyses of the Beijing ring expressway have showed that up to 50% of microwave sensors pose missing values.
Xiaolei Ma, Sen Luan, Bowen Du, Bin Yu
doaj +1 more source
Selection of the Best Copula Function in Bivariate Analysis of Water Resources Components (Case study: Siminehrood River Basin, Iran) [PDF]
Water Harvesting ResearchThe copula function is a joint distribution of correlated random variables that are defined based on univariate marginal distributions. The aim of the present study is to select the best copula function to create joint probability distributions between ...
Fahimeh Sharifan +3 more
doaj +1 more source
Log-mean linear models for binary data [PDF]
, 2012 This paper introduces a novel class of models for binary data, which we call log-mean linear models. The characterizing feature of these models is that they are specified by linear constraints on the log-mean linear parameter, defined as a log-linear ...
A. Roverato +4 more
core +3 more sources
New bivariate and multivariate log-normal distributions as models for insurance data
Results in Applied Mathematics, 2022 The body of most multivariate financial data sets can be well modeled by log-normal distributions. Yet not many multivariate log-normal distributions are available in the literature.
Saralees Nadarajah, Jiahang Lyu
doaj +1 more source
Multivariate Distributions with Fixed Marginals and Correlations [PDF]
Journal of Applied Probability, 2015 Consider the problem of drawing random variates (X 1, …, X n ) from a distribution where the marginal of each X i is specified, as well as the correlation between every pair X i and X
Huber, Mark, Marić, Nevena
openaire +4 more sources
Simulating the binary variates for the components of a socio - economical system [PDF]
Computational Methods in Social Sciences, 2013 Often in practice the components Wj of a sociological or an economical system W take discrete 0-1 values. We talk about how to generate arbitrary observations from a binary 0-1 system B when is known the multidimensional distribution of the discrete ...
Stefan V. Stefanescu
doaj
The Bayesian Estimate of Vector Autoregressive Model Parameters Adopt Informative Prior Information
Tikrit Journal of Pure Science, 2023 This research included the bayesian estimate for vector Autoregressive model with rank (p) in addition to statistical tests and predict Bayesian when the random error of model followed generalized multivariate modified Bessel distribution.
Haifaa Abdulgawwad Saeed +2 more
doaj +1 more source