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Introduction to Bayesian hierarchical modelling using R (IBHM04)
martes, 03 de marzo de 2020

Introduction to Bayesian hierarchical modelling using R (IBHM04)

https://www.psstatistics.com/course/introduction-to-bayesian-hierarchical-modelling-using-r-ibhm04/

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This course will be delivered by Dr Andrew Parnell form the 23rd - 27th March in Glasgow City Centre.

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 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

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