ONLINE COURSE – Introduction to Bayesian hierarchical modelling using R (IBHM04) This course will be delivered live 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. 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. ----------------------------------------------------------------------------------------------------- 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 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 <http://www.psstatistics.com/> 53 Morrison Street Glasgow G5 8LB +44 (0) 7966500340 -- Oliver Hooker PhD. PS statistics -- Oliver Hooker PhD. PS statistics 2019 publications; A way forward with eco evo devo: an extended theory of resource polymorphism with postglacial fishes as model systems. Biological Reviews (2019). www.PSstatistics.com facebook.com/PSstatistics/ twitter.com/PSstatistics 6 Hope Park Crescent Edinburgh EH8 9NA +44 (0) 7966500340 Generalised Linear (MIXED) (GLMM), Nonlinear (NLGLM) And General Additive Models (MIXED) (GAMM) (GNAM01) https://www.psstatistics.com/course/generalised-linear-glm-nonlinear-nlglm-a... Structural Equation Models, Path Analysis, Causal Modelling and Latent Variable Models Using R (SMPA01) https://www.psstatistics.com/course/structural-equation-modelling-and-path-a... Python for data science, machine learning, and scientific computing (PDMS01) https://www.psstatistics.com/course/python-for-data-science-machine-learning... Statistical modelling of time-to-event data using survival analysis: an introduction for animal behaviourists, ecologists and evolutionary biologists (TTED02) https://www.psstatistics.com/course/statistical-modelling-of-time-to-event-d... Behavioural data analysis using maximum likelihood in R (BDML02) https://www.psstatistics.com/course/behavioural-data-analysis-using-maximum-... Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan (BDRS02) https://www.psstatistics.com/course/introduction-to-bayesian-data-analysis-f... Introduction to statistical modelling for psychologists in R (IPSY03) https://www.psstatistics.com/course/introduction-to-statistics-using-r-for-p... Introduction to Bayesian hierarchical modelling using R (IBHM03) https://www.psstatistics.com/course/introduction-to-bayesian-hierarchical-mo...