GMM

Kernel Density Estimation (KDE) for estimating probability distribution function

Leave a Comment / Machine Learning, Python, Tutorials /

There are several approaches for estimating the probability distribution function of a given data: 1)Parametric 2)Semi-parametric 3)Non-parametric A parametric one is GMM via algorithm such as expectation maximization. Here is my other post for expectation maximization. Example of Non-parametric is the histogram, where data are assigned to only one bin and depending on the number bins that fall within […]

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Expectation Maximization algorithm to obtain Gaussian mixture models for ROS

Leave a Comment / C++, Machine Learning, ROS, Tutorials /

I found a really good code at GitHub for fitting a Gaussian Mixture Model (GMM) with Expectation Maximization (EM) for ROS. There are so many parameters that you can change. Some of the most important ones are:

To find the optimal number of components, it uses Bayesian information criterion (BIC). There are other methods to find

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