Articles by Corey Chivers

Montreal R Workshop: Likelihood Methods and Model Selection

March 16, 2012 | Corey Chivers

Monday, March 19, 2012  14h-16h, Stewart Biology N4/17 Corey Chivers, McGill University Department of Biology This workshop will introduce participants to the likelihood principal and its utility in statistical inference.  By learning how to formalize models through their likelihood function, participants will learn how to confront these models with data in ... [Read more...]

π Day Special! Estimating π using Monte Carlo

March 14, 2012 | Corey Chivers

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...]

Montreal R workshop on Causal Inference

March 5, 2012 | Corey Chivers

Monday, March 05, 2012  14h-16h N4/17 Stewart Biology Building, McGill University Prof. Bill Shipley from Université de Sherbrooke Topics Structural equation modelling Graphical models for understanding causal analysis Testing for goodness of fit...
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Visualising the Metropolis-Hastings algorithm

February 10, 2012 | Corey Chivers

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...]

Gauging Interest in a Montreal R User Group

February 7, 2012 | Corey Chivers

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...]

General Bayesian estimation using MHadaptive

February 6, 2012 | Corey Chivers

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...]
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