One of the ultimate goals of climate studies is to provide projections of future scenarios: for this purpose, sophisticated models are conceived, involving lots of parameters calibrated via observed ...
Abstract: In recent years, the significant success of deep learning (DL) in computer vision has contributed to its continuous development in the field of hyperspectral image (HSI) anomaly detection ...
🔵 On the Multivariate Normal Distribution 👇 🔸 Introduction: The multivariate normal distribution is an extension of the univariate normal distribution to multiple dimensions. It describes a vector ...
Abstract: The settling/rising velocity is of key importance in the vertical distribution of microplastics in marine environment. It is generally parameterized with semi-empirical laws dependent on the ...
ABSTRACT: We start with analyzing stochastic dependence in a classic bivariate normal density framework. We focus on the way the conditional density of one of the random variables depends on ...
The coincidence of flood flows in a mainstream and its tributaries may lead to catastrophic floods. In this paper, we investigated the flood coincidence risk under nonstationary conditions arising ...
The quantum state preparation of probability distributions is an important subroutine for many quantum algorithms. When embedding D-dimensional multivariate probability distributions by discretizing ...
ABSTRACT: Singh, Gewali, and Khatiwada proposed a skewness measure for probability distributions called Area Skewness (AS), which has desirable properties but has not been widely applied in practice.
The three important aspects of the statistical properties of financial and economic data that all financial data scientists must know are distributions, stochastic processes, and the anomalous ...
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