Statistical estimation of a mixture of Gaussian distributions

1995 ◽  
Vol 38 (1) ◽  
pp. 37-54 ◽  
Author(s):  
R. Rudzkis ◽  
M. Radavičius
Keyword(s):  
Statistical Estimation ◽  
Gaussian Distributions ◽  
Mixture Of Gaussian

Protein local conformations arise from a mixture of Gaussian distributions

2007 ◽  
Vol 32 (S1) ◽  
pp. 899-908 ◽  
Author(s):  
Ashish V. Tendulkar ◽  
Babatunde Ogunnaike ◽  
Pramod P. Wangikar
Keyword(s):  
Gaussian Distributions ◽  
Local Conformations ◽  
Mixture Of Gaussian

scholarly journals Statistical Forecasting : Third Wave of COVID-19-With an Application to India

2021 ◽  
Author(s):  
Sabara Parshad Rajeshbhai ◽  
Subhra Sankar Dhar ◽  
Shalabh Shalabh
Keyword(s):  
Gaussian Mixture Model ◽  
Gaussian Mixture ◽  
Third Wave ◽  
Statistical Methodology ◽  
Gaussian Distributions ◽  
Wave Modelling ◽  
The Third ◽  
Entire World ◽  
The Impact ◽  
Mixture Of Gaussian

The pandemic due to the SARS-CoV-2 virus impacted the entire world in different waves. An important question that arise after witnessing the first and second waves of COVID-19 is - Will the third wave also arrive and if yes, then when. Various types of methodologies are being used to explore the arrival of third wave. A statistical methodology based on the fitting of mixture of Gaussian distributions is explored in this paper and the aim is to forecast the third wave using the data on the first two waves of pandemic. Utilizing the data of different countries that are already facing the third wave, modelling of their daily cases data and predicting the impact and timeline for the third wave in India is attempted in this paper. The Gaussian mixture model based on algorithm for clustering is used to estimate the parameters.

$\ell_{1}$ -Norm Heteroscedastic Discriminant Analysis Under Mixture of Gaussian Distributions

2019 ◽  
Vol 30 (10) ◽  
pp. 2898-2915 ◽  
Author(s):  
Wenming Zheng ◽  
Cheng Lu ◽  
Zhouchen Lin ◽  
Tong Zhang ◽  
Zhen Cui ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Gaussian Distributions ◽  
Mixture Of Gaussian

Mixed Bayesian Networks: A Mixture of Gaussian Distributions

1994 ◽  
Vol 33 (05) ◽  
pp. 535-542 ◽  
Author(s):  
J. P. Chevrolat ◽  
F. Rutigliano ◽  
J. L. Golmard
Keyword(s):  
Bayesian Networks ◽  
Probabilistic Models ◽  
Graphical Representation ◽  
Expectation Maximization Algorithm ◽  
Estimation Algorithm ◽  
Continuous Variables ◽  
Gaussian Distributions ◽  
Stochastic Version ◽  
Comprehensive Method ◽  
Mixture Of Gaussian

Abstract:Mixed Bayesian networks are probabilistic models associated with a graphical representation, where the graph is directed and the random variables are discrete or continuous. We propose a comprehensive method for estimating the density functions of continuous variables, using a graph structure and a set of samples. The principle of the method is to learn the shape of densities from a sample of continuous variables. The densities are approximated by a mixture of Gaussian distributions. The estimation algorithm is a stochastic version of the Expectation Maximization algorithm (Stochastic EM algorithm). The inference algorithm corresponding to our model is a variant of junction three method, adapted to our specific case. The approach is illustrated by a simulated example from the domain of pharmacokinetics. Tests show that the true distributions seem sufficiently fitted for practical application.

Analyze of Maldi-TOF Proteomic Spectra with Usage of Mixture of Gaussian Distributions

2009 ◽  
pp. 113-120 ◽  
Author(s):  
Małgorzata Plechawska ◽  
Joanna Polańska ◽  
Andrzej Polański ◽  
Monika Pietrowska ◽  
Rafał Tarnawski ◽  
...  
Keyword(s):  
Gaussian Distributions ◽  
Maldi Tof ◽  
Mixture Of Gaussian

A learning from demonstration framework for implementation of a feeding task

2018 ◽  
Vol 02 (01) ◽  
pp. 1850001 ◽  
Author(s):  
Nabil Ettehadi ◽  
Aman Behal
Keyword(s):  
Expectation Maximization ◽  
Optimization Problem ◽  
Gaussian Mixture ◽  
The Other ◽  
Learning From Demonstration ◽  
Constrained Optimization Problem ◽  
Gaussian Distributions ◽  
End Effector ◽  
Mixture Regression ◽  
Mixture Of Gaussian

In this paper, a learning from demonstration (LFD) approach is used to design an autonomous meal-assistance agent. The feeding task is modeled as a mixture of Gaussian distributions. Using the data collected via kinesthetic teaching, the parameters of the Gaussian mixture model (GMM) are learnt using Gaussian mixture regression (GMR) and expectation maximization (EM) algorithm. Reproduction of feeding trajectories for different environments is obtained by solving a constrained optimization problem. In this method we show that obstacles can be avoided by robot’s end-effector by adding a set of extra constraints to the optimization problem. Finally, the performance of the designed meal assistant is evaluated in two feeding scenario experiments: one considering obstacles in the path between the bowl and the mouth and the other without.

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