Category Archives: Management

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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Tiny Data – Bayesian dirty socks – but how many were there in the laundry machine…?

I found this very illuminating short tutorial video on Approximate Bayesian Computation, by Rasmus Bååth, on youtube, and since Rasmus example uses R as the implementation language, I decided to implement the example in Python. The problem at hand is … Continue reading

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Size Matters, particularly in prediction

A follow-up on my previous post on statistical significance and hypothesis-testing: Let’s say we pull a number of samples, as in the previous post, from both a control group and a test group. Let’s also say that for the samples from … Continue reading

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Lies,damn lies, statistics,predictions – the world is more random than you might think…

For a number of years ago, John Ionnidis published a paper claiming to prove that most research papers are in fact wrong.  That is, the findings of many/most research papers can actually not be reproduced by other, independent teams. According … Continue reading

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A/B-testing on trendline data using Bayesian Inference

Asume we are in the process of doing evidence based testing of two competing strategies, “A” and “B”, and we want to evaluate these competing strategies over several days, weeks, months or whatever timespan we have decided upon. That is, … Continue reading

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Val2018 – trendlinje

Nedan trendlinjer för samtliga 8 nuvarande riksdagspartier, från valet 2014 till januari 2018. Först en odämpad, “noisy” presentation: Den här typen av odämpad trendlinje blir snabbt oläslig, så om man misstänker – som jag gör – att det underliggande datat … 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

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Bayesian Inference for upcoming elections

In a couple of previous posts, I’ve tried to wear out my swedish speaking audience with predictions regarding the upcoming national elections, using Bayesian Inference. This post will be a bit more technical, perhaps of interest for a larger audience … Continue reading

Posted in Bayes, Big Data, Data Analytics, Data Driven Management, Math, Numpy, performance, Politik, Probability, Pystan, Python, Simulation, Society, STAN, Statistics, Sverige | Tagged , , , , , , , , , , | Leave a comment