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Introduction This blog started off life as a blog post on how I use nix but somehow transformed itself into a “how I do data visualisation” blog post. The nix is still here though quietly doing its work in the background. Suppose you want to analyze your local election results and visualize them using a choropleth but you’d like to use Haskell. You could try using the shapefile package but you wil
Introduction Summary Back in January, a colleague pointed out to me that GHC did not produce very efficient code for performing floating point abs. I have yet to produce a write-up of my notes about hacking on GHC: in summary it wasn’t as difficult as I had feared and the #ghc folks were extremely helpful. But maybe getting GHC to produce high performance numerical code is “swimming uphill”. Below
Introduction In most presentations of Riemannian geometry, e.g. O’Neill (1983) and Wikipedia, the fundamental theorem of Riemannian geometry (“the miracle of Riemannian geometry”) is given: that for any semi-Riemannian manifold there is a unique torsion-free metric connection. I assume partly because of this and partly because the major application of Riemannian geometry is General Relativity, con
Introduction In their paper Betancourt et al. (2014), the authors give a corollary which starts with the phrase “Because the manifold is paracompact”. It wasn’t immediately clear why the manifold was paracompact or indeed what paracompactness meant although it was clearly something like compactness which means that every cover has a finite sub-cover. It turns out that every manifold is paracompact
We wish to determine the position and velocity of the car given noisy observations of the position. In general we need the distribution of the hidden path given the observable path. We use the notation to mean the path of starting a and finishing at . Haskell Preamble > {-# OPTIONS_GHC -Wall #-} > {-# OPTIONS_GHC -fno-warn-name-shadowing #-} > {-# OPTIONS_GHC -fno-warn-type-defaults #-} > {-# OPTI
Introduction We previously used importance sampling in the case where we did not have a sampler available for the distribution from which we wished to sample. There is an even more compelling case for using importance sampling. Suppose we wish to estimate the probability of a rare event. For example, suppose we wish to estimate where . In this case, we can look up the answer . But suppose we could
Introduction Let us see if we can estimate the parameter for population growth using MCMC in the example in which we used Kalman filtering. We recall the model. Preamble > {-# OPTIONS_GHC -Wall #-} > {-# OPTIONS_GHC -fno-warn-name-shadowing #-} > {-# OPTIONS_GHC -fno-warn-type-defaults #-} > {-# OPTIONS_GHC -fno-warn-unused-do-bind #-} > {-# OPTIONS_GHC -fno-warn-missing-methods #-} > {-# OPTIONS_
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