Model Selection and Multimodel Inference Workshop

Date: 29 - 30 March 2012
Location: Days Inn Centre-Ville, Montréal, QC 
Room: Soprano conference room
Time: 08:30 - 16:30 (Thursday - Friday)

Workshop instructor: Dr. David Anderson 
Workshop organizer: Dr. Marc Mazerolle 

For more information regarding the location or registration details, contact Marc Mazerolle .

Workshop agenda


8:30 - 12:00 Morning session

12:00 - 13:00 Lunch (choose your own restaurant)

13:00 - 16:30 Afternoon session

Evening - optional informal dinner to chat with Dr. Anderson


8:30 - 12:00 Morning session

12:00 - 13:00 Lunch (choose your own restaurant)

13:00 - 16:00 Afternoon session

Evening - optional informal dinner to chat with Dr. Anderson

Workshop material

Participants can bring either a calculator or laptop for the workshop. Class notes will be distributed at the beginning of the workshop as well as a copy of the book Anderson, D. R. 2008. Model based inference in the life sciences: a primer on evidence. Springer, New York.

The course will cover the following topics:

  1. Science and Analysis Paradigms Over The Past 115 Years: A Provocative Perspective?
  2. Comparison of Null Hypothesis Testing vs. Information-Theoretic Methods in Very Simple Problems
  3. Some Thoughts on Data Dredging
  4. Kullback-Leibler information
  5. Science hypotheses and their models (the a priori model set, gi)
  6. Akaike’s Information Criterion AIC and AICc
  7. Formal evidence and entropy
  8. Δ values, a measure information loss and the basic quantity of interest
  9. likelihood of a model, given the data L(gi|data)
  10. The probability of a model, given the data Prob(gi|data)
  11. Evidence ratios
  12. Making formal inference from all the models in the set (multimodel inference)
  13. Model averaging
  14. Unconditional variances
  15. Overdispersion and QAICc
  16. Evolving hypothesis sets

This course is based on the reference book, Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. 2nd Ed., Springer- Verlag, New York, NY. 488pp. and on the recent textbook (supplied as part of the registration fee), Anderson, D. R. 2008. Model based inference in the life sciences: a primer on evidence. Springer, New York, NY. 184pp.

About the venue

The workshop will take place in downtown Montréal at the Days Inn Montréal Centre-Ville hotel  and is hosted by the Center for Forest Research . The workshop venue is a 5-min. walk from many restaurants and shops and metro stations.

Getting to Montréal

Workshop participants can get to Montréal by plane. The Pierre-Elliott Trudeau International Airport (YUL)  features regular service from major Canadian and American cities. Daily train service is also available from VIA Rail  between Montréal and Québec city. An express bus line (Orléans Express ) also operates between Montréal and Québec city with hourly departures. Participants arriving by car can plan their road trip with the Mapquest tool .

Staying in Montréal

Besides the Days Inn  where the workshop is taking place, a number of hotels are located in the same neighborhood, namely on boulevard René-Lévesque. These include Holiday Inn Express , Candlewood Suites , and Travelodge , among others. As always, before booking your room at the establishment of your choice, it can be a good idea to check the reviews on Trip Advisor .

Moving around in Montréal

Montréal has a reliable subway and bus service to get to and from different portions of the city. To get the metro layout, bus routes, schedules, and the nearest station, check the Société de transport de Montréal site  which features a handy travel planner.

Montréal is renowned for its great food. Aside from high-end restaurants, many bistros and casual restaurants offer a wide array of cuisines. You can also use this search tool  to find something inspiring in your neighborhood while in Montréal.

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La référence vedette !

Jérémie Alluard (2016) Les statistiques au moments de la rédaction 

  • Ce document a pour but de guider les étudiants à intégrer de manière appropriée une analyse statistique dans leur rapport de recherche.

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