## Theoretically rigorous introduction to EM, Mixture of Gaussians and K-means

This post ties together EM (Expectation Maximization), GMM (Gaussian Mixture Models), K means and variational inference. If you have taken an introductory ma...

This post ties together EM (Expectation Maximization), GMM (Gaussian Mixture Models), K means and variational inference. If you have taken an introductory ma...

Multivariate normal (MVN) is used everywhere in machine learning, from simple regressions, linear discriminant analysis, Kalman filters to gaussian processes...

There is nothing in statistics that is as easy as regressions to use but as hard to make a correct interpretation. Especially in causal inference, even the c...

Science experiments, social experiments, thought experiments, … We use the word “experiment” somewhat often in real life. But have you ever wondered what exp...

If you’ve been working in the tech industry, or have thought about doing so, you’ve probably heard of AB testing. Some of you have even conducted one. The id...

Bell Curve = Great Intellectual Fraud? I recently read a New York Times best-seller titled “Black Swan” by Nicholas Taleb. The book discusses how hard it is ...

There are countless numbers of probability distributions. Some of them are so widely used and beautiful that they deserve a name. Surprisingly, all of those ...

We come across probability not just in statistics classrooms but also in real life. But, have you thought about what probability really means? I would like t...

I found an interesting blog post recently, titled: 20 Questions to Detect Fake Data Scientists. I could answer some of them, but not all. So I decided to ans...