Posts Tagged ‘ Probability ’

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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Introduction to Bayesian lecture: Accompanying handouts and demos

October 19, 2012
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Introduction to Bayesian lecture: Accompanying handouts and demos

I recently posted the slides from a guest lecture that I gave on Bayesian methods for biologists/ecologist. In an effort to promote active learning, the class was not a straight forward lecture, but rather a combination of informational input from me and opportunities for students to engage with the concepts via activities and discussion of

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Introduction to Bayesian Methods guest lecture

October 18, 2012
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Introduction to Bayesian Methods guest lecture

This is a talk I gave this week in Advanced Biostatistics at McGill. The goal was to provide an gentle introduction to Bayesian methodology and to demonstrate how it is used for inference and prediction. There is a link to an accompanying R script in the slides

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Observing Dark Worlds – Visualizing dark matter’s distorting effect on galaxies

October 13, 2012
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Observing Dark Worlds – Visualizing dark matter’s distorting effect on galaxies

Some people like to do crossword puzzles. I like to do machine learning puzzles. Lucky for me, a new contest was just posted yesterday on Kaggle. So naturally, my lazy Saturday was spent getting elbow deep into the data. The training set consists of a series of ‘skies’, each containing a bunch of galaxies. Normally,

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Continuous dispersal on a discrete lattice

September 27, 2012
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Continuous dispersal on a discrete lattice

Dispersal is a key process in many domains, and particularly in ecology. Individuals move in space, and this movement can be modelled as a random process following some kernel. The dispersal kernel is simply a probability distribution describing the distance travelled in a given time frame. Since space is continuous, it is natural to use

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The future of Artificial Intelligence – as imagined in 1989

September 6, 2012
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The future of Artificial Intelligence – as imagined in 1989

This image comes from the cover of Preliminary Papers of the Second International Workshop on Artificial Intelligence and Statistics (1989). Someone abandoned it in the lobby of my building at school. Whatever for, I’ll never know. I just love the idea of machine learning/AI/Statistics evoking a robot hand drawing a best fit line through some

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An update on visualizing Bayesian updating

August 17, 2012
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An update on visualizing Bayesian updating

A while ago I wrote this post with some R code to visualize the updating of a beta distribution as the outcome of Bernoulli trials are observed. The code provided a single plot of this process, with all the curves overlayed on top of one another. Then John Myles White (co-author of Machine Learning for

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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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Simulating Euro 2012

June 11, 2012
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Simulating Euro 2012

Why settle for just one realisation of this year’s UEFA Euro when you can let the tournament play out 10,000 times in silico? Since I already had some code lying around from my submission to the Kaggle hosted 2010 Take on the Quants challenge, I figured I’d recycle it for the Euro this year. The

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R Workshop: Reproducible Research using Sweave for Beginers

April 27, 2012
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R Workshop: Reproducible Research using Sweave for Beginers

Monday, April 30, 2012  14h-16h. Stewart Biology Rm w6/12 (Montreal) guRu: Denis Haine (Université de Montréal) Topics Reproducible research was first coined by Pr. Jon Claerbout, professor of geophysics at Stanford University, to describe that the results from researches can be replicated by other scientists by making available data, procedures, materials and the computational environment

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