ONLINE COURSE – Introduction to Bayesian hierarchical modelling using
R (IBHM04) This course will be delivered live
https://www.psstatistics.com/course/introduction-to-bayesian-hierarchical-modelling-using-r-ibhm04/
This course will be delivered via video link from the 21st-24th April
In light of travel restrictions due to the COVID-19 (Coronavirus)
outbreak this course will now be delivered live by video link.
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures
and coaching attendees through the accompanying computer practical’s
via video link, a good internet connection is essential.
-----------------------------------------------------------------------------------------------------
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/
-----------------------------------------------------------------------------------------------------
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 [log in to unmask] with any questions.
########################################################################
To unsubscribe from the BIODIVERSITY list, click the following link:
https://www.jiscmail.ac.uk/cgi-bin/webadmin?SUBED1=BIODIVERSITY&A=1
|
