Gaussian Mixture Model (GMM)

Here is a detailed explanation:

1. Mixture Model: The GMM assumes that the data can be modeled as a mixture of several Gaussian distributions. Each component of the mixture is a multivariate normal distribution with its own mean and covariance matrix.
2. Probability Density Function: The probability density function of the GMM is a weighted sum of the density functions of each Gaussian component. The mixture weights represent the probability that a data point comes from each Gaussian component.
3. Parameter Estimation: The parameters of the GMM (means, covariance matrices, and mixture weights) are typically estimated using the Expectation-Maximization (EM) algorithm. This algorithm alternates between two steps:
   • Expectation (E) Step: Calculates the probability that each data point belongs to each Gaussian component, given the current parameters.
   • Maximization (M) Step: Updates the parameters of the Gaussian components to maximize the likelihood of the data, given the weights calculated in the E step.
4. Class Assignment: After training the model, each data point can be assigned to the Gaussian component with the highest probability of generating it. This allows the GMM to be used for classification or unsupervised clustering tasks.

The GMM is particularly useful in situations where the data do not fit well to a single normal distribution and can be better represented by a combination of multiple distributions. It is widely used in fields such as pattern recognition, computer vision, and bioinformatics, among others.