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 ... [Read more...]

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 [Read more...]

As the term has progressed, my LSM2241 lectures are getting more consistent. I’m aiming to use 45 slides for what is officially a two hour lecture, although in reality it lasts about 90 minutes. We take a break at about 50 … Continue reading → [Read more...]

Eric and I have been exchanging emails about potential analyses for the school data and he published a first draft model in Offsetting Behaviour. I have kept on doing mostly data exploration while we get a definitive full dataset, and … Continue reading → [Read more...]

Editor’s Note: This post written by Roger Peng and Jeff Leek.  A couple of weeks ago, we announced that we would be teaching free courses in Computing for Data Analysis and Data Analysis on the Coursera platform. At the same time, a number of ot... [Read more...]

When running stats labs I like to allocate a slightly different subset of data to each student, which acts as an incentive for people to do their own work (rather than copying the same results from a fellow student). We … Continue reading → [Read more...]

Disappeared for a while collecting frequent flyer points. In the process I ‘discovered’ that I live in the middle of nowhere, as it took me 36 hours to reach my conference destination (Estoril, Portugal) through Christchurch, Sydney, Bangkok, Dubai, Madrid … Continue reading → [Read more...]

Chaos. Hectic, seemingly unpredictable, complex dynamics. In a word: fun. I usually stick to the warm and fuzzy world of stochasticity and probability distributions, but this post will be (almost) entirely devoid of randomness. While chaotic dynamics are entirely deterministic, their sensitivity to initial conditions can trick the observer into ... [Read more...]

I like statistics and I struggle with statistics. Often times I get frustrated when I don’t understand and I really struggled to make sense of Krushke’s Bayesian analysis of a split-plot, particularly because ‘it didn’t look like’ a split-plot to … Continue reading → [Read more...]

Forestry is the province of variability. From a spatial point of view this variability ranges from within-tree variation (e.g. modeling wood properties) to billions of trees growing in millions of hectares (e.g. forest inventory). From a temporal point of view … Continue reading → [Read more...]

Some people will say ‘you have to learn R if you want to get a job doing statistics/data science’. I say bullshit, you have to learn statistics and learn to work in a variety of languages if you want to … Continue reading → [Read more...]

Monday, March 26, 2012  14h-16h, Stewart Biology N4/17 Corey Chivers, Department of Biology McGill University This is a meetup of the Montreal R User Group. Be sure to join the group and RSVP. More information about the workshop here. Topics Why would we want to be Bayesian in the first place?  ... [Read more...]

In honour of π day (03.14 – can’t wait until 2015~) , I thought I’d share this little script I wrote a while back for an introductory lesson I gave on using Monte Carlo methods for integration. The concept is simple – we can estimate the area of an object which is inside another ... [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...]

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(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 was thinking about new material that I will use for teaching this coming semester (starting the third week of February) and suddenly compiled the following list of links: William Briggs writes It is time to stop teaching Frequentism to … Continue reading → [Read more...]

I am not a statistician but I use statistics, teach some statistics and write about applications of statistics in biological problems. Last week I was in this biostatistics conference, talking with a Ph.D. student who was surprised about this situation … Continue reading → [Read more...]

Things have been a bit quiet at Quantum Forest during the last ten days. Last Monday (Sunday for most readers) I flew to Australia to attend a couple of one-day workshops; one on spatial analysis (in Sydney) and another one … Continue reading → [Read more...]

This post is somewhat marginal to R in that there are several statistical systems that could be used to tackle the problem. Bayesian statistics is one of those topics that I would like to understand better, much better, in fact. … Continue reading → [Read more...]