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, especially 

in the analysis of complex data sets such as those encountered in the study 

of marine mammals where the collection of multiple and auto-correlating 

environmental variables is unavoidable. 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.  

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Please not we will also be offering the following online;

1) Python for data science, machine learning, and scientific computing
(PDMS02) 4th-8th May
>
www.psstatistics.com/course/python-for-data-science-machine-learning-and-scientific-computing-
pdms02/

2) Generalised Linear (MIXED) (GLMM), Nonlinear (NLGLM) And General
Additive Models (MIXED) (GAMM) (GNAM01) 25th-29th May
>
www.psstatistics.com/course/generalised-linear-glm-nonlinear-nlglm-and-general-additive-models-gam-
gnam02/

3) Reproducible Data Science and R Package Design (RDRP01) 29th June - 3rd
July
>
www.psstatistics.com/course/reproducible-data-science-and-r-package-design-rdrp01/
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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.

Course Programme
Tuesday 21st – 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

Wednesday 22nd – 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

Thursday 23rd – 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

Friday 24th – Classes from 09:00 to 17:00

Module 7: Shrinkage and variable selection
Module 8: Hierarchical models and partial pooling
Practical: Shrinkage modelling

Please email oliverhooker@psstatistics.com with any questions.

Oliver Hooker PhD.
PR statistics

2020 publications;

Parallelism in eco-morphology and gene expression despite variable
evolutionary and genomic backgrounds in a Holarctic fish. PLOS Genetics (in
press) (2020).

www.PSstatistics.com

53 Morrison Street
Glasgow
G5 8LB
+44 (0) 7966500340

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Oliver Hooker PhD.
PS statistics  
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