[R-sig-ME] Intro to Bayesian mixed (hierarchical) modelling

[Oliver Hooker]

Introduction to Bayesian hierarchical modelling using R (IBHM02)

https://www.prstatistics.com/course/introduction-to-bayesian-hierarchical-modelling-using-r-ibhm02/

29th January 2018 - 2nd February 2018

Course Overview:
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.

Monday 29th – 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 30th – 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 31st – 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 1st – 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 2nd – Classes from 09:00 to 16:00
Final day for recap session, catch up time and bring your own data set

-- 
Oliver Hooker PhD.
PR statistics

2017 publications -

Ecosystem size predicts eco-morphological variability in post-glacial 
diversification. Ecology and Evolution. In press.

The physiological costs of prey switching reinforce foraging 
specialization. Journal of animal ecology.

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