Introduction to Bayesian hierarchical modelling using R (IBHM04) FREE ACCOMMODATION AVAILABLE
23 March 2020 - 27 March 2020£275 - £570.00
This course will cover introductory hierarchical modelling for real-world data sets from a 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 hierarchical models. All methods are demonstrated with data sets which participants can run themselves. Participants will be taught how to fit hierarchical models using the Bayesian modelling software Jags and Stan through the R software interface. The course covers the full gamut from simple regression models through to full generalised multivariate hierarchical structures. A Bayesian approach is taken throughout, meaning that participants can include all available information in their models and estimates all unknown quantities with uncertainty. Participants are encouraged to bring their own data sets for discussion with the course tutors.
Research postgraduates, practicing academics and professionals in government and industry.
Venue – PS statistics head office, 53 Morrison Street, Glasgow, G5 8LB – Google map
Availability – 15 places
Duration – 5 days
Contact hours – Approx. 35 hours
ECT’s – Equal to 3 ECT’s
Language – English
Other payment options are available please email email@example.com
PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact firstname.lastname@example.org. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees (and accommodation fees if booked through PR statistics) will be credited. However, PS statistics will not be held responsible/liable for any travel fees, accommodation costs or other expenses incurred to you as a result of the cancellation. Because of this PS statistics strongly recommends any travel and accommodation that is booked by you or your institute is refundable/flexible and to delay booking your travel and accommodation as close the course start date as economical viable.
There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
A basic understanding of regression methods and generalised linear models.
Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.
Equipment and software requirements
A laptop/personal computer with a working version or R, RStudio, JAGS and stan installed. All are supported by both PC and MAC and can be downloaded for free by following these links.
It is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) internet access may not always be available.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK email@example.com
Meet at Flat 2/1, 43 Cook Street, Glasgow G5 8JN at approx. 17:00 onwards
Monday 8th – Classes from 09:00 to 17:00
Module 1: Introduction to Bayesian Statistics
Module 2: Linear and generalised linear models (GLMs)
Practical: Using R, Jags and Stan for fitting GLMs
Round table discussion: Understanding Bayesian models
Tuesday 9th – Classes from 09:00 to 17:00
Module 3: Simple hierarchical regression models
Module 4: Hierarchical models for non-Gaussian data
Practical: Fitting hierarchical models
Round table discussion: Interpreting hierarchical model output
Wednesday 10th – Classes from 09:00 to 17:00
Module 5: Hierarchical models vs mixed effects models
Module 6: Multivariate and multi-layer hierarchical models
Practical: Advanced examples of hierarchical models
Round table discussion: Issues of continuous vs discrete time
Thursday 11th – Classes from 09:00 to 16:00
Module 7: Shrinkage and variable selection
Module 8: Hierarchical models and partial pooling
Practical: Shrinkage modelling
Round table discussion Bring your own data set
Friday 12th – Classes from 09:00 to 16:00
Final day for recap session, catch up time and bring your own data set