# Tag: Probability

• ## Generalized Linear Models in TensorFlow

Linear regression is by far, one of the most used statistical models in the industry. To the extent that many times it’s used because… why the fuck not? But in the end: All models are wrong, but some are useful George E. P. Box This statement remarks there are neither good or bad models. It’s […]

• ## 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’ […]

• ## 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 […]

• ## 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 […]

• ## Introduction to random variables for non-data scientists

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 them. How many books do you […]