GitHub

paulxing1/EMATM0061

java c Assignment 6 EMATM0061: Statistical Computing and Empirical Methods, TB1, 2024 Introduction This is the sixth assignment for Statistical Computing and Empirical Methods. This assignment is ...
IEEE

Supervised Gaussian process latent variable model based on Gaussian mixture model

Abstract: In this paper, we propose a supervised Gaussian Process Latent Variable Model(GPLVM) based on Gaussian mixture model(GMM). In this model, we assume that latent variables satisfy the Gaussian ...
IEEE

Capacity of a Gaussian MIMO channel with nonzero mean

Abstract: We characterize the input covariance that maximizes the ergodic capacity of a flat-fading, multiple-input-multiple-output (MIMO) channel with additive white Gaussian noise, when the entries ...
Scientific Research Publishing

A Special Weight for Inverse Gaussian Mixing Distribution in Normal Variance Mean Mixture with Application ()

1 Department of Mathematics and Actuarial Science, Kisii University, Kisii, Kenya. 2 School of Mathematics, University of Nairobi, Nairobi, Kenya. Normal Inverse Gaussian (NIG) distribution introduced ...
mccormick.northwestern.edu

ELEC_ENG 422: Random Processes in Communications and Control I

CATALOG DESCRIPTION: Fundamentals of random variables; mean-squared estimation; limit theorems and convergence; definition of random processes; autocorrelation and stationarity; Gaussian and Poisson ...
Risk.net

Copulas find new role in derivatives pricing

But not all copulas are created equal. Normal mean-variance mixture copulas can capture heavy tails and asymmetries in the ...
Simon Fraser University

Analysis of Variance for Categorical Data and Generalized Linear Models

A categorical variable is defined as one that can assume only a limited number of values. For example, a person's sex is a categorical variable that can assume one of two values. Variables with levels ...