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Introduction to Frequentist and Bayesian mixed (Hierarchical) models (IFBM01)
8 October 2018 - 12 October 2018
This course will cover introductory mixed or hierarchical modelling (fixed and random effects models) for real-world data sets from both a Frequentist and Bayesian perspective. These methods lie at the forefront of statistics research and are a vital tool in the scientist’s toolbox. The course focuses on introducing concepts and demonstrating good practice in mixed modelling. All methods are demonstrated with data sets which participants can run themselves. Participants will be taught how to fit hierarchical models using both the standard lme4 mixed effects models library in R, together with the Bayesian modelling framework via rstanarm. The course covers the full gamut from simple regression models through to full generalised multivariate mixed structures. The relevant advantages and disadvantages of both the Frequentist and Bayesian approaches will be presented.. Participants are encouraged to bring their own data sets for discussion with the course tutors.
To find out more or to book online via our sister company (PR informatics) use the link below…
The instructors were excellent and clearly were the reasons for my previous comments. They both combined a deep understanding of statistics and ecology at the same level.Any questions or queries I’ve had, were thus first answered with an ecological point of view and then translated into statistical consideration thereby making much more sense on both side.In addition the course was very well organised, the course director and the two instructors were very friendly as well as professional. On the top of learning many useful things, I’ve also had a very good time during the week there.” Clement Garcia,
Spatial ecologist, Centre For Environment, Fisheries & Aquaculture Science (CEFAS), England
(Attended ADVR course)