Tag Archives: Numpy

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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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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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

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Val2018 – Prognosuppdatering

Fortfarande samma data, dvs Sentios opinionsundersökningar, men dels lite tweakning av den Bayesianska inferensmodellen, och dessutom förstorad skala på grafiken. Som synes av graferna, så ger ett 95% konfidensintervall ett brett resultat, för brett för att vara till verklig nytta. … Continue reading

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Val 2018 – prognos mha Bayesian Inference för samtliga riksdagspartier

Så, nu har mitt program tuggat sig igenom datat för samtliga 8 riksdagspartier, och för varje parti tagit fram en prognos över partiets röstandel i det kommande valet i september. Prognoserna är gjorda med “Bayesian Inference”, dvs en statistisk metod … Continue reading

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Val 2018 – prognos mha Bayesian Inference

(For my non-swedish readers – sorry, but this article is in swedish, and probably not much of interest to you anyway, being about the upcoming swedish elections. In case you are looking for general info on Bayesian inference, there are … Continue reading

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