pymc3

This is Part 2 in a series on Bayesian optimal pricing. Part 1 is here. Introduction In Part 1 we used PyMC3 to build a Bayesian model for sales. By the end we had this result: A common advantage of Bayesian analysis is the understanding it gives us of the distribution of a given result. For example, we very easily analyze a sample from the posterior distribution of profit for a given price. [Read More]

Pricing is a common problem faced by businesses, and one that can be addressed effectively by Bayesian statistical methods. We'll step through a simple example and build the background necessary to extend get involved with this approach. Let's start with some hypothetical data. A small company has tried a few different price points (say, one week each) and recorded the demand at each price. We'll abstract away some economic issues in order to focus on the statistical approach. [Read More]

A client comes to you with this problem: The coal company I work for is trying to make mining safer. We made some change around 1900 that seemed to improve things, but the records are all archived. Tracking down such old records can be expensive, and it would help a lot if we could narrow the search. Can you tell us what year we should focus on? Also, it would really help to know this is a real effect, and not just due to random variability - we don't want to waste resources digging up the records if there's not really anything there. [Read More]