Gaussian Mixture Models - Learn Data Science with Travis

Gaussian-Mixture-Models

Gaussian Mixture Models (GMMs) are a powerful statistical tool used in unsupervised learning to model complex data distributions. They assume that the data points are generated from a mixture of several Gaussian distributions, each characterized by its own mean and covariance. GMMs extend the k-means clustering algorithm by incorporating information about the shape and spread of the data, allowing for more flexible clustering. The model estimates the parameters of these Gaussian components using techniques like the Expectation-Maximization (EM) algorithm, making it suitable for various applications, including image processing, finance, and pattern recognition.

2.1. Gaussian mixture models

Scikit-learn User Guide

sklearn.mixture is a package which enables one to learn Gaussian Mixture Models (diagonal, spherical, tied and full covariance matrices supported), sample them, and estimate them from data. Facilit......

Gaussian Mixture Models (GMMs): from Theory to Implementation

Towards Data Science

Mixture Models A mixture model is a probability model for representing data that may arise from several different sources or categories, each of which is modeled by a separate probability distribution...

In Depth: Gaussian Mixture Models

Python Data Science Handbook

The k -means clustering model explored in the previous section is simple and relatively easy to understand, but its simplicity leads to practical challenges in its application. In particular, the non-...

Gaussian Mixture Models(GMM)

Level Up Coding

Brief: Gaussian mixture models is a popular unsupervised learning algorithm. The GMM approach is similar to K-Means clustering algorithm, but is more robust and therefore useful due to…

Gaussian Mixture Models(GMM)

Analytics Vidhya

Brief: Gaussian mixture models is a popular unsupervised learning algorithm. The GMM approach is similar to K-Means clustering algorithm, but is more robust and therefore useful due to…

Gaussian Mixture Models with Python

Towards Data Science

The Gaussian mixture model (GMM) is well-known as an unsupervised learning algorithm for clustering. Here, “Gaussian” means the Gaussian distribution, described by mean and variance; mixture means…

Gaussian Mixture Model Selection

Scikit-learn Examples

Gaussian Mixture Model Selection This example shows that model selection can be performed with Gaussian Mixture Models using information-theoretic criteria (BIC) . Model selection concerns both the co...

Model-Based Clustering using GMM-Gaussian Mixture Models

Level Up Coding

A Gaussian Mixture Model (GMM) is a clustering technique that assumes data is generated from a mixture of several Gaussian distributions, each with its own mean and covariance. GMMs are widely used in...

Gaussian Mixture Model Ellipsoids

Scikit-learn Examples

Gaussian Mixture Model Ellipsoids Plot the confidence ellipsoids of a mixture of two Gaussians obtained with Expectation Maximisation ( GaussianMixture class) and Variational Inference ( BayesianGauss...

Gaussian Mixture Model Sine Curve

Scikit-learn Examples

Gaussian Mixture Model Sine Curve This example demonstrates the behavior of Gaussian mixture models fit on data that was not sampled from a mixture of Gaussian random variables. The dataset is formed ...

Demystifying Gaussian Mixture Models and Expectation Maximization

Towards Data Science

Explanation of Gaussian Mixture Models and its underlying algorithm of Expectation Maximization in a simplified manner

Gaussian Mixture Model Clearly Explained

Towards Data Science

The only guide you need to learn everything about GMM Photo by Planet Volumes on Unsplash When we talk about Gaussian Mixture Model (later, this will be denoted as GMM in this article), it's essentia...