Posts Tagged ‘ Simulation ’

R midterms

November 9, 2012
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R midterms

Here are my R midterm exams, version A and version B in English (as students are sitting next to one another in the computer rooms), on simulation methods for my undergrad exploratory statistics course. Nothing particularly exciting or innovative! Dedicated ‘Og‘s readers may spot a few Le Monde puzzles in the lot… Two rather entertaining

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Variable probability Bernoulli outcomes – Fast and Slow

November 1, 2012
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Variable probability Bernoulli outcomes – Fast and Slow

I am working on a project that requires the generation of Bernoulli outcomes. Typically, I would go about this using the built in sample() function like so: This works great and is fast, even for large n. Problem is, I want to generate each sample with its own unique probability. Seems straight forward enough, I

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On weather forecasts, Nate Silver, and the politicization of statistical illiteracy

October 30, 2012
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On weather forecasts, Nate Silver, and the politicization of statistical illiteracy

As you know, we have a thing for statistical literacy here at Simply Stats. So of course this column over at Politico got our attention (via Chris V. and others). The column is an attack on Nate Silver, who has a blog where he tries to predict the outc...

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slides for my simulation course

October 18, 2012
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slides for my simulation course

Similar to last year, I am giving a series of lectures on simulation jointly as a Master course in Paris-Dauphine and as a 3rd year course in ENSAE. The course borrows from both the books Monte Carlo Statistical Methods and from Introduction to Monte Carlo Methods with R, with George Casella. Here are the three

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ISBA towards higher computing goals [yet another new section!!!]

September 19, 2012
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ISBA towards higher computing goals [yet another new section!!!]

Surrounding the great and exciting gathering of Bayesian statisticians in Kyoto last June, several ISBA sections have appeared in the past weeks, as already mentioned on the ‘Og. Along with Anto Mira and Nicolas Chopin (who did most of the organisational work while I was wandering down under!), we discussed about a Bayesian computation section

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Review of “Numerical Methods and Optimization in Finance” by Gilli, Maringer and Schumann

September 12, 2012
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Review of “Numerical Methods and Optimization in Finance” by Gilli, Maringer and Schumann

Previously This book and the associated R package were introduced before. Executive Summary A very nice — and enlightening — discussion of a wide range of topics. Principles The Introduction to the book sets out 5 principles.  This is probably the most important part of the book.  The principles are: We don’t know much in … Continue reading...

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Suicide statistics and the Christchurch earthquake

September 5, 2012
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Suicide statistics and the Christchurch earthquake

Suicide is a tragic and complex problem. This week New Zealand’s Chief Coroner released its annual statistics on suicide, which come with several tables and figures. One of those figures refers to monthly suicides in the Christchurch region (where I … Continue reading →

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Why trust some supposed laws of statistical sampling and…

August 15, 2012
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Why trust some supposed laws of statistical sampling and convergence when you can just test them yourself? If you have a computer with R installed (also recommended: Rstudio) then you can stop dithering about whether these n=1000 studies cited in the n...

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Simulation: The modeller’s laboratory

August 10, 2012
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Simulation: The modeller’s laboratory

In his 2004 paper in Trends in Ecology and Evolution, Steven Peck argues: Simulation models can be used to mimic complex systems, but unlike nature, can be manipulated in ways that would be impossible, too costly or unethical to do in natural systems. Simulation can add to theory development and testing, can offer hypotheses about the

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Using discrete-event simulation to simulate hospital processes

July 12, 2012
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Using discrete-event simulation to simulate hospital processes

Discrete-event simulation is a very useful tool when it comes to simulating alternative scenario’s for current of future business operations. Let’s take the following case; Patients of an outpatient diabetes clinic are complaining about long waiting times, this seems to have an adverse effect on patient satisfaction and patient retention.  Read more »