Expectation maximization provides an iterative solution to maximum likelihood estimation with latent variables. Gaussian mixture models are an approach to density estimation where the parameters of ...

The expectation-maximization (EM) algorithm is a popular approach for parameter estimation of finite mixture model (FMM). A drawback of this approach is that the number of components of the finite ...

Let’s talk about the Gaussian Mixture model. Gaussian Mixture Model. The Gaussian Mixture Model is an important concept in machine learning which uses the concept of expectation-maximization. A ...

During this Practical work, we are going to first generate data following a GMM model, and then we are going to implement an EM algorithm in order to infer the GMM model parameters. First, let's ...

Gaussian mixture models are a very useful tool for modeling data distribution. While estimating parameters using the expectation-maximization algorithm, this approach does not scale well with big ...