bayes
The intuitive way of A/B testing. The advantages of the Bayesian approach and how to do it.
Learn how to build MMMs for different countries the right way
Wayfair has a huge catalog with over 14 million items. Our site features a diverse array of products for people’s homes, with product categories ranging from “appliances” to “décor and pillows” to “outdoor storage sheds.” Some of these categories include hundreds of thousands of products; this broad offering ensures that we have something for every style and home. However, the large size of our product catalog also makes it hard for customers to find the perfect item among all of the possible options.
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All i...
Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).
There are multiple ways to doing the same thing in Pandas, and that might make it troublesome for the beginner user.This post is about handling most of the data manipulation cases in Python using a straightforward, simple, and matter of fact way.
by Carles Gelada and Jacob Buckman WARNING: This is an old version of this blogpost, and if you are a Bayesian, it might make you angry. Click here for an updated post with the same content. Context: About a month ago Carles asserted on Twitter that Bayesian Neural Networks make...
What is it to be Bayesian? The (pretty simple) math modelling behind a Big Data buzzword
A blog about Compressive Sensing, Computational Imaging, Machine Learning. Using priors to avoid the curse of dimensionality arising in Big Data.
Recently I’ve started using PyMC3 for Bayesian modelling, and it’s an amazing piece of software! The API only exposes as much of heavy machinery of MCMC as you need — by which I mean, just the pm.sample() method (a.k.a., as Thomas Wiecki puts it, the Magic Inference Button™). This really frees up your mind to think about your data and model, which is really the heart and soul of data science! That being said however, I quickly realized that the water gets very deep very fast: I explored my data set, specified a hierarchical model that made sense to me, hit the Magic Inference Button™, and… uh, what now? I blinked at the angry red warnings the sampler spat out.
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