Month: January 2020
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Another go at modelling the Labour leadership election
I started doing this for fun and that’s still my motivation – so please do not take this seriously and even if I do slip into using the word “prediction”, above all – this is not a prediction. Anyway my aim is to model the potential outcome of the first round of the ballot of […]
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Mathematically modelling the overall Labour result
The Zipf model I outlined here looks to be reasonably robust – though maybe the coefficient needs to drop to somewhere between 1.25 and 1.29 – but can we use this result to draw any conclusions on the actual result itself? That’s what I am going to try to do here – but be warned […]
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Mathematically modelling the Labour leadership nomination race
No politics here – just some maths. But if we use a Zipf distribution (see here for more about that) we get a pretty good fit for the three front runners – Keir Starmer, who currently has 43 nominations from constituency labour parties, Rebecca Long-Bailey who has 17 and Lisa Nandy who has 10 – […]
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Convolutional network (again)
With time on my hands I have returned to working on an old project – attempting to build a convolutional network that will solve chess puzzles. (A convolutional network is a type of neural network – a modelled ‘artificial intelligence’ that can be used to detect patterns or undertake similar tasks.) Here I am not […]
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The problem with parallelisation
Just over 15 years ago a big change in general computing occurred – computer hardware more or less stopped getting faster. The previous three decades of ever-faster computer hardware had been supported by the ability to etch ever-smaller transistors on silicon. This, together with an ability to use bigger silicon wafers leads to what is […]
cartesian product ... stuff about computing mostly 