Maximum a posteriori with Tensorflow

In the past, a method for parameter estimation was covered, named maximum likelihood estimation (MLE). If you want to know more about it, visit the following blog post. There is another parameter estimation method that it's worth mentioning. It's called maximum a posteriori, shortened by MAP. To see how that works we'll revisit the Bayes' … Continue reading Maximum a posteriori with Tensorflow

Expectation-Maximization algorithm for the masses

Expectation-Maximization (EM) algorithm is widely known for being used in clustering or segmentation problems. Despite its usefulness, not so many know how the algorithm works. In this post, I will present the internals of the EM algorithm. Then, I'll walk you through an example where I'll develop the EM algorithm from scratch. And finally, I'll … Continue reading Expectation-Maximization algorithm for the masses

Maximum likelihood estimation with Tensorflow

We are given a data set and we are told to model its distribution. But, how to proceed? A reasonable first step would be to plot in a histogram the distribution of values. After looking at the histogram, you recognize its shape. It looks like a Normal, or... maybe it's a Gamma distribution? Maximum likelihood … Continue reading Maximum likelihood estimation with Tensorflow

Introduction to random variables for non-data scientists

Photo by Morgan Harris on Unsplash Me: Hey, just took a pic in the library! Look how old and dusty are these books! You: Are these the oldest ones you found in there? Me: No, I didn't inspect all of them, just picked those five. You: Look! In the background you can see plenty of … Continue reading Introduction to random variables for non-data scientists