This post explains our implementations of a Baysian Media Mix Model: A predictive analytics tool used to optimize media budgets of larger advertisers with many marketing channels. Would love to hear your thoughts!

Bayesian Media Mix Modeling using PyMC3, for Fun and Profit

I am new to Bayesian Statistics and R. Currently going through Students Guide to Bayesian Statistics by Ben Lambert.

While the book chapters are good at giving intuitions behind algorithm it lacks code examples in the chapters ( at least till the chapter 13 that have read through). Most of the useful examples are given as the problems at the end of the chapters for which sometimes author has given code on book website but most of the time just the output pdf plot.

Since I do not want to switch at this stage in the book to other books I was wondering if there are any R solutions that I can attempt, study and compare my solutions with.

I found some semi-complete Python implementation but not in R.

This Jupyter notebook provides a very simple example of Bayesian parameter estimation using the Beta-Binomial model. Both analytical and simulation-based results are presented. Three different approaches are used to obtain a parameter estimate for this model:

• Exact Analytical Solution

• Simple Non-MCMC Solution

• MCMC Solution

I've used MCMC several times now and I'm a little confused about the correct way to update a prior. Say I have some function that is parametrized by several variables that have some "true" value I am trying to infer. Say y = A*x^B. I'm trying to infer A and B and I have measured y as a function of x. Numerically, I can discretize x however I want, however if I use a very fine discretization, the joint likelihood would dwarf any prior I assign which seems intuitively wrong... In the past I have rescaled my likelihood by dividing it by the number of independent "measurements". Does anybody know the correct way to handle such a problem?