Category Archives: Math

Bayesian updating with PYMC

I’ve been looking for neat ways to update a Bayesian Prior from a posterior sample for a while, and just the other day managed to find what I was looking for: a code example that shows how to make a … Continue reading

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Poor Man’s Betting Shop – Using Baysian Inference to setup your own Betting Shop

Further exploration of Bayesian Inference, applied to the upcoming 2018 Ice Hockey World Championships. This time, I’m trying to understand how the professional betting shops set their odds, and how they make a profit. It took some ‘research’ into the … Continue reading

Posted in Bayes, Complex Systems, Data Analytics, development, Gambling, Math, Numpy, Probability, PYMC, Python, Statistics | Tagged , , , , , , , , , | Leave a comment

2018 Ice Hockey World Championships – ‘Raw’ Odds Qualifying Round

[logo copyright IIHF] Below the probability distributions from previous post  converted to odds, or more specifically, “Raw” odds, that is, odds based purely on the underlying posterior distributions, not taking into account other aspects, such as the betting distribution, or the … Continue reading

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2018 Ice Hockey World Championships – predicted outcomes

Here’s the first real prediction from my Bayesian Inference Engine, on results for the upcoming Ice Hockey World Championships, starting in about 3 weeks from now. As usual, there are 16 teams, split into two groups: Group A : [‘RUS’,’SWE’,’CZE’,’SUI’,’BLR’,’SVK’,’FRA’,’AUT’] … Continue reading

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Bayesian Inference – what good is the Prior, anyway…?

A brief example on the effect of Bayesian priors (I’m going to use my Ice Hockey Championship Prediction hack under development for this example): Assume you would like to bet on the outcome of some particular game, for instance, Sweden … Continue reading

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Tiny Data – Bayesian Dirty Socks Revisited (Bayesian Inference using MCMC)

For some time ago, I posted an article on estimating the total number of socks in your laundy machine, after initially  having pulled out 11 odd (non-pair) socks.  The post was inspired by Rasmus Bååth’s posting where he uses R … Continue reading

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Distribution of distributions in 3D

Just a quick add-on to my previous post on yet another way to present multidimensional data: To recap, we have a “distribution of distributions”, where each distribution has two dimensions, mu and sigma. In the previous post, I chose to present … Continue reading

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Bayesian A/B-testing, part III

This final part on Bayesian A/B-testing will continue looking at the various assumptions, implicit or explicit, that always are in play when building statistical models.  In part II, we looked at what impact larger data sets have on our inferences, … Continue reading

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Bayesian A/B-testing, part II

Continuing my example by examining how the different assumptions – yes, in any model there are always assumptions, explicit or implicit – of the model impact the end result, that is, the prediction of the sought after signup-rate, a.k.a our posterior … Continue reading

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A/B-testing with Bayesian Inference

The example Inspired by Rasmus Bååth’s lectures on Bayesian Inference, I’ve implemented a simple Python example demostrating how Bayesian Inference can be used for A/B-testing, that is, evidence based testing. This methodology, i.e. A/B-testing, is useful in most domains, e.g. … Continue reading

Posted in A/B Testing, Bayes, Behavioral Economics, Big Data, Data Analytics, Data Driven Management, Math, Numpy, performance, Probability, Pystan, Python, Simulation, STAN, Statistics | Tagged , , , , , , , , , , , | Leave a comment