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.
Why would we want to be Bayesian in the first place? In this workshop we will examine the types of questions which we are able to ask when we view the world through a Bayesian perspective.This workshop will introduce Bayesian approaches to both statistical inference and model based prediction/forecasting. By starting with an examination of the theory behind this school of statistics through a simple example, the participant will then learn why we often need computationally intensive methods for solving Bayesian problems. The participant will also be introduced to the mechanics behind these methods (MCMC), and will apply them in a biologically relevant example.
The participant will:
1) Contrast the underlying philosophies of the Frequentist and Bayesian perspectives.
2) Estimate posterior distributions using Markov Chain Monte Carlo (MCMC).
3) Conduct both inference and prediction using the posterior distribution.
We will build on ideas presented in the workshop on Likelihood Methods. If you did not attend this workshop, it may help to have a look at the slides and script provided on this page.
The goal of this workshop is to demystify the potentially ‘scary‘ topic of Bayesian Statistics, and empower participants (of any preexisting knowledge level) to engage in statistical reasoning when conducting their own research. So come one, come all!
That being said, a basic working understanding of R is assumed. Knowledge of functions and loops in R will be advantageous, but not a must.
This workshop will be conducted entirely in R. We will not be using any external software such as winBUGS.
We will use a package I have written which is available on CRAN:
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 order to make statistical inference.
The concepts of using maximum likelihood to fit model parameters, and model comparison using information theoretic approaches will also be covered.
The workshop will explore these topics through worked examples and exercises using the R statistical computing environment.
This is the first official meetup of the Montreal R User Group. Be sure to join the group and RSVP. More information about the workshop here.
The Montreal R User Group is now official. You can join the group by visiting the meetup site.
The group has existed since 2010 in a narrower incarnation as the BGSA R/Stats Workshop Series. Previous workshops have featured invited facilitators on topics such as Causal Analysis, GLMs, GAMs, Multi-model inference, Phylogenetic analysis, Bayesian modeling, Meta-analysis, Ordination, Programming and more. Our goal is to broaden the scope of the workshops to incorporate topics from a wide variety of applied fields.
We are kicking off with a meetup on Monday, March 19th. At the meeting, I will be facilitating a workshop on building and estimating Maximum Likelihood models and doing model selection using AIC.
We look forward to seeing you at the next meetup!
Montreal R User Group Organizers
Zofia Ecaterina Taranu