1115 search results for "latex"

simulating correlated Binomials [another Bernoulli factory]

April 20, 2015
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simulating correlated Binomials [another Bernoulli factory]

This early morning, just before going out for my daily run around The Parc, I checked X validated for new questions and came upon that one. Namely, how to simulate X a Bin(8,2/3) variate and Y a Bin(18,2/3) such that corr(X,Y)=0.5. (No reason or motivation provided for this constraint.) And I thought the following (presumably

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AusDM 2015 submission deadline extended to Thursday 30 April

April 20, 2015
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AusDM 2015 submission deadline extended to Thursday 30 April

The 13th Australasian Data Mining Conference (AusDM 2015) Sydney, Australia, 8–9 August 2015 (co-located with SIGKDD’15) URL: http://ausdm15.ausdm.org/ The Australasian Data Mining Conference has established itself as the premier Australasian meeting for both practitioners and researchers in data mining. It … Continue reading →

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R 3.2.0 is released (+ using the installr package to upgrade in Windows OS)

April 17, 2015
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R 3.2.0 is released (+ using the installr package to upgrade in Windows OS)

R 3.2.0 (codename “Full of Ingredients”) was released yesterday. You can get the latest binaries version from here. (or the .tar.gz source code from here). The full list of new features and bug fixes is provided below. Upgrading to R 3.2.0 on Windows If you are using Windows you can easily upgrade to the latest version of R using the installr … Continue reading...

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Statistics journals network

April 16, 2015
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Statistics journals network

Xian blogged recently on the incoming RSS read paper: Statistical Modelling of Citation Exchange Between Statistics Journals, by Cristiano Varin, Manuela Cattelan and David Firth. Following the last JRSS B read paper by one of us! The data that are used in the paper (and can be downloaded here) are quite fascinating for us, academics fascinated by academic rankings,

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Knitr with Bio7

April 16, 2015
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16.04.2015 In Bio7 it is possible to write a documentation in knitr. A simple visual and textual HTML editor (based on JavaFX) and an embedded Latex editor (based on TeXclipse) helps in the creation of a *.html or *.pdf documentation. For the *.pdf creation a TeX environment has to be available (Windows e.g. MiKTeX). Overview

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Bernouilli, Montmort and Waldegrave

April 14, 2015
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Bernouilli, Montmort and Waldegrave

In the last issue of Statistical Science, David Belhouse   and Nicolas Fillion published an accounting of a discussion between Pierre Rémond de Montmort, Nicolaus Bernoulli—”the” Bernoulli associated with the St. Petersburg paradox—, and Francis Waldegrave, about the card game of Le Her (or Hère, for wretch). Here is the abridged

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Beautiful plots while simulating loss in two-part procrustes problem

April 14, 2015
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Beautiful plots while simulating loss in two-part procrustes problem

Today I was working on a two-part procrustes problem and wanted to find out why my minimization algorithm sometimes does not converge properly or renders unexpected results. The loss function to be minimized is with denoting the Frobenius norm, is an unknown scalar and an unknown rotation matrix, i.e. . , and are four real

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Write an R Journal article with knitr

April 11, 2015
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Write an R Journal article with knitr

R Journal The R Journal publishs peer-reviewed short to medium length articles covering topics that might be of interest to users or developers of R. It is a welcome platform to spread word of new packages. As most academic journals, it has strict guidelines regarding the format of submitted articles. Traditionally, these have been provided with a The post

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Classification with Categorical Variables (the fuzzy side)

April 9, 2015
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Classification with Categorical Variables (the fuzzy side)

The Gaussian and the (log) Poisson regressions share a very interesting property, i.e. the average predicted value is the empirical mean of our sample. > mean(predict(lm(dist~speed,data=cars))) 42.98 > mean(cars$dist) 42.98 One can prove that it is also the prediction for the average individual in our sample > predict(lm(dist~speed,data=cars), + newdata=data.frame(speed=mean(cars$speed))) 42.98 The geometric interpretation is that the...

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an email exchange about integral representations

April 7, 2015
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an email exchange about integral representations

I had an interesting email exchange with a (German) reader of Introducing Monte Carlo Methods with R in the past days, as he had difficulties with the validation of the accept-reject algorithm via the integral in that it took me several iterations to realise the

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