1285 search results for "LateX"

The Beta Prior, Likelihood, and Posterior

September 4, 2013
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The Beta Prior, Likelihood, and Posterior

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

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Latent Variable Analysis with R: Getting Setup with lavaan

September 1, 2013
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Latent Variable Analysis with R: Getting Setup with lavaan

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...

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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

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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

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ECVP tutorial on classification images

August 30, 2013
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ECVP tutorial on classification images

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,

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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

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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

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Electronic lab notebook

August 20, 2013
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Electronic lab notebook

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.

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Weak Learners

August 20, 2013
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Weak Learners

Tonight's session of R bore just enough fruit that I finally am writing my sophomore entry. I was browsing the slides from a machine learning lecture given by Leo Breiman and came across a relatively simple example he used to introduce the notions...

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Endogenous Spatial Lags for the Linear Regression Model

August 18, 2013
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Endogenous Spatial Lags for the Linear Regression Model

Over the past number of years, I have noted that spatial econometric methods have been gaining popularity. This is a welcome trend in my opinion, as the spatial structure of data is something that should be explicitly included in the empirical modelling procedure. Omitting spatial effects assumes that the location co-ordinates for observations are unrelated

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