Why would I ever NEED Bayesian Statistics?

Probabilistic Programming versus Machine Learning In the past ten years, we’ve seen an explosion in Machine Learning applications, these applications have been particularly successful in search, e-commerce, advertising, social media and other verticals. These applications have been particularly focused on predictive accuracy and often involve large amounts of data — sometimes in the region of terabytes — in fact this… Continue reading Why would I ever NEED Bayesian Statistics?

How do I visualise the results of a Bayesian Model: Rugby models in Arviz

I’ve been recently playing around with ‘arviz’. For those of you who don’t know Arviz is a library for exploratory analysis Bayesian Models. I’ve got a Bayesian model built – or someone has built one for me, how do I explore it? How do I plot it? This is the fundamental question that Arviz answers.… Continue reading How do I visualise the results of a Bayesian Model: Rugby models in Arviz

Why Probabilistic Programming is the next big thing in Data Science

TLDR: This is an opinionated post, but based on recent trends. What is Probabilistic Programming? I recently wrote a course teaching this. Probabilistic Programming is a newish paradigm used in Quantitative Finance, Biology, Insurance and Sports Analytics – it allows you to build generative models to infer latent parameters and the uncertainty of those parameters. It’s been… Continue reading Why Probabilistic Programming is the next big thing in Data Science

Marketing data with PyMC3

My friend Erik put up an example of conversion analysis with PyMC2 recently. I decided to reproduce this with PyMC3. We want a good model with uncertainty estimates of various marketing channels. I’ll restate his assumptions for the model and then show the gist. Let’s make some assumptions about the model: The cost per transaction… Continue reading Marketing data with PyMC3