In a previous post, I demonstrated how to use my R package MHadapive to do general MCMC to estimate Bayesian models. The functions in this package are an implementation of  the Metropolis-Hastings algorithm. In this post, I want to provide an intuitive way to picture what is going on ‘under ... [Read more...]

Some of us over at McGill’s Biology Graduate Student Association have been developing and delivering R/Statistics workshops over the last few years. Through invited graduate students and faculty, we have tackled  everything from multi-part introductory workshops to get your feet wet, to special topics such as GLMs, GAMs, ... [Read more...]

If you can write the likelihood function for your model, MHadaptive will take care of the rest (ie. all that MCMC business). I wrote this R package to simplify the estimation of posterior distributions of arbitrary models. Here’s how it works: 1) Define your model (ie the likelihood * prior). In ... [Read more...]

(Almost) every introductory course in probability introduces conditional probability using the famous Monte Hall problem. In a nutshell, the problem is one of deciding on a best strategy in a simple game. In the game, the contestant is asked to select one of three doors. Behind one of the doors ... [Read more...]

I love board games. Over the holidays, I came across this interesting post over at Arthur Charpentier’s Freakonometrics blog about the classic game of snakes and ladders. The post is a nice little demonstration of how the game can be formulated completely as a Markov chain, and can be ... [Read more...]

Teacher: “How variable is your estimate of the mean?” Student: “Uhhh, it’s not. I took a sample and calculated the sample mean. I only have one number.” Teacher: “Yes, but what is the standard deviation of sample means?” Student: “What do you mean means, I only have the one ... [Read more...]

One of the most straightforward examples of how we use Bayes to update our beliefs as we acquire more information can be seen with a simple Bernoulli process. That is, a process which has only two  possible outcomes. Probably the most commonly thought of example is that of a coin ... [Read more...]

Check them out. Here are thirty homoskedastic ones: __ homo.wiener for (j in 1:30) {  for (i in 2:length(homo.wiener)) {          homo.wiener[i,j] for (j in 1:30) {        plot( homo.wiener[,j],           type = "l", col = rgb(.1,.... [Read more...]

One of my teaching roles is in an introductory Genetics course, where first year students are presented with a wide range of new ideas at a relatively fast pace.  It seems that often, students choose to take a memorization approach to learning the material, rather than taking the chance to ... [Read more...]

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Today, at lunch, Matthieu told us a nice story (or call it a paradox if you like) about the probability to find you seat empty when you get in a place.  a plane full of nuns Assume that you are in the line to get in the airplane, you are ... [Read more...]

Simple root finding and one dimensional integrals algorithms were implemented in previous posts. These algorithms can be used to estimate the cumulative probabilities and quantiles. Here, take normal distribution as an example. Read More: 281 Words Totally [Read more...]

cumsum ( rnorm(50), lend="butt", lwd=12, type="h" ) Cumulative sum of 50 draws from a normal distribution. File this under mysteries of the Central Limit Theorem. [Read more...]

SOME EMPIRICAL BASES FOR CHOOSING CERTAIN RISK REPRESENTATIONS OVER OTHERS This week DSN posts some thoughts (largely inspired by the work of former colleagues Stephanie Kurzenhäuser, Ralph Hertwig, Ulrich Hoffrage, and Gerd Gigerenzer) about communicating risks to the general public, providing references and delicious downloads where possible. Representations to ... [Read more...]

Yes, I did just mix English, Spanish and French. And no, I living the “fishy” life, popular opinion to the contrary. Here’s the story. As someone who spends the majority of his time working online, with no oversight, I notice that I tend to drift a lot. I don’... [Read more...]

50 days ago, I published a post, here, on forecasting techniques. I was wondering what could be the probability to have, by the end of this year, one million pages viewed (from Google Analytics) on this blog. Well, initially, it was on my blog at t... [Read more...]

Central Limit Theorem A nice illustration of the Central Limit Theorem by convolution.in R: Heaviside 0,1,0) }HH [Read more...]

Toss one hundred different balls into your basket. Shuffle them up and select one with equal probability amongst the balls. That ball you just selected, it’s special. Before you put it back, increase its weight by 1/100th. Then put it back, mix up the balls and pick again. If ... [Read more...]

Imagine a unit square. Every side has length 1, perfectly square. Now imagine this square was really a fence, and you picked two spots at random along the fence, with uniform probability over the length of the fence. At each of these two locations, set down a special kind of cannon. ... [Read more...]

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