# 1267 search results for "latex"

## Monty Hall (oh no, not again)

September 13, 2013
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$A$

Quite frequently, someone on the internet discovers the Monty Hall paradox, and become so enthusiastic that it becomes urgent to publish an article – or a post – about it. The latest example can be http://www.bbc.co.uk/news/magazine-24045598. I won’t blame them, I did the same a few years ago (see http://freakonometrics.hypotheses.org/776, or http://freakonometrics.hypotheses.org/775, in French). My point today is that the...

## Non-observable vs. observable heterogeneity factor

September 11, 2013
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$X$

This morning, in the ACT2040 class (on non-life insurance), we’ve discussed the difference between observable and non-observable heterogeneity in ratemaking (from an economic perspective). To illustrate that point (we will spend more time, later on, discussing observable and non-observable risk factors), we looked at the following simple example. Let  denote the height of a person. Consider the following dataset >...

## The Beta Prior, Likelihood, and Posterior

September 4, 2013
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The Beta distribution (and more generally the Dirichlet) are probably my favorite distributions.  However, sometimes only limited information is available when trying set up the distribution.  For example maybe you only know the lowest likely value, the highest likely value and the median, as a measure of center.  That information is sufficient to construct a

## Latent Variable Analysis with R: Getting Setup with lavaan

September 1, 2013
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Getting Started with Structural Equation Modeling Part 1Getting Started with Structural Equation Modeling: Part 1 Introduction For the analyst familiar with linear regression fitting structural equation models can at first feel strange. In the R environment, fitting structural equation models involves learning new modeling syntax, new plotting...

## Introducing ‘propagate’

August 31, 2013
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$Introducing ‘propagate’$

With this post, I want to introduce the new ‘propagate’ package on CRAN. It has one single purpose: propagation of uncertainties (“error propagation”). There is already one package on CRAN available for this task, named ‘metRology’ (http://cran.r-project.org/web/packages/metRology/index.html). ‘propagate’ has some additional functionality that some may find useful. The most important functions are: * propagate: A

## Encouraging citation of software – introducing CITATION files

August 30, 2013
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Summary: Put a plaintext file named CITATION in the root directory of your code, and put information in it about how to cite your software. Go on, do it now – it’ll only take two minutes! Software is very important in science – but good software takes time and effort that could be used to do

## ECVP tutorial on classification images

August 30, 2013
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The slides for my ECVP tutorial on classification images are available here. Try this alternative version if the equations look funny. (image from Mineault et al. 2009) The slides are in HTML and contain some interactive elements. They’re the result of experimenting with R Markdown, D3 and pandoc. You write the slides in R Markdown,

## predictNLS (Part 2, Taylor approximation): confidence intervals for ‘nls’ models

August 26, 2013
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$predictNLS (Part 2, Taylor approximation): confidence intervals for ‘nls’ models$

Initial Remark: Reload this page if formulas don’t display well! As promised, here is the second part on how to obtain confidence intervals for fitted values obtained from nonlinear regression via nls or nlsLM (package ‘minpack.lm’). I covered a Monte Carlo approach in http://rmazing.wordpress.com/2013/08/14/predictnls-part-1-monte-carlo-simulation-confidence-intervals-for-nls-models/, but here we will take a different approach: First- and second-order

## From SVG to probability distributions [with R package]

August 25, 2013
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$From SVG to probability distributions [with R package]$

Hey, To illustrate generally complex probability density functions on continuous spaces, researchers always use the same examples, for instance mixtures of Gaussian distributions or a banana shaped distribution defined on with density function: If we draw a sample from this distribution using MCMC we obtain a plot like this one: Clearly it doesn’t really look

## Electronic lab notebook

August 20, 2013
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I was interested to read C. Titus Brown‘s recent post, “Is version control an electronic lab notebook?” I think version control is really important, and I think all computational scientists should have something equivalent to a lab notebook. But I think of version control as serving needs orthogonal to those served by a lab notebook.