I’ve been meaning to put my musings on the books I read on this site for a while. In most instances it will be in areas where I am not an expert.
First off, the title refers to common thread in a person’s character that evokes the common (aka vulgar), or working class. In this case, it refers to mannerisms in the protagonist’s behavior. It does not, as I was expecting, refer to a habit of exploding into profanity-laced tirades by a character.
In this lecture, we’ll discuss why python is good language to learn for scientific computing. We’ll also cover the use of strings in Python 3 as well as the topics of branching (if statements) as well as simple iteration with while loops.
Here’s a link to the lecture. For the iPython notebook source, click here.
Yes, epistemic uncertainty (that is, uncertainty due to a lack of knowledge) is likely the biggest thorn in the side of a modeler/computational physicist/ computational engineer (I would make a bolder statement, but, at the risk of being redundant, I just don’t know man). One way of dealing with this is the concept of Probability Boxes. The basic idea is to treat aleatory uncertainties as distributional and epistemic as to be an interval. On the one hand, this is conservative because we have no assumptions about the underlying distribution, but the
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In this post we’ll look at some actual experimental data (crazy, I know) and use simulation data from the code Hyades2D to try and produce calibrated results. The data is the very same used in Stripling, McClarren, et al., and we show how Gaussian process models can be used to make sense of simulation and experimental data.
If you’re in the business of sampling from a distribution that you only know up to a constant normalization, Markov-Chain Monte Carlo (and the Metropolis Algorithm) are for you. The Metropolis algorithm is name after a scientist, and not the adopted hometown of an illegal alien, but it can leap unruly distributions in a (burn-in + n) bound. In particular, Bayes’ Theorem gives us an unnormalized distribution we would like to sample from.
In this example, we do some basic orthogonal expansions of 1-D functions of random variables and then use sampling from the approximation and the true function to evaluate the expansion. In future work we will show how to compute the expansions using quadrature. The source for the markdown is here.
In this example I demonstrate how to quasi-random number generators behave in different circumstances. The methods investigated are van der Corput seuqnces, Halton sequences, Sobol sequences, and torus sequences. The random numbers are produced using the randtoolbox available in R.
This document (created via R markdown), shows how one can propagate uncertain parameters through a model via simple random sampling and stratified sampling.
In this markdown (source here), we work through an example where we have a lot of variables, but only a few are significant. It demonstrates how different regularized regression techniques, namely ridge and lasso, can be used to tackle this problem with fewer data points than unknowns.