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January 2018
£260 - £520

Introduction to Bayesian hierarchical modelling using R (IBHM02)

29 January 2018 - 2 February 2018
SCENE, Loch Lomond, Rowardennan,
Glasgow, Scotland, G63 0AW, United Kingdom

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…

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March 2018
£275.00 - £475.00

Behavioural data analysis using maximum likelihood in R (BDML01)

19 March 2018 - 23 March 2018
PS statistics head office, 53 Morrison Street
Glasgow, Scotland, G5 8LB United Kingdom

Course Overview: This 5-day course will involve a combination of lectures and practical sessions. Students will learn to build and fit custom models for analysing behavioural data using maximum likelihood techniques in R. This flexible approach allows a researcher to a) use a statistical model that directly represents their hypothesis, in cases where standard models are not appropriate and b) better understand how standard statistical models (e.g. GLMs) are fitted, many of which are fitted by maximum likelihood. Students will…

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April 2018
£275.00 - £500.00

Introduction to statistical modelling for psychologists in R (IPSY01)

16 April 2018 - 20 April 2018
PS statistics head office, 53 Morrison Street
Glasgow, Scotland, G5 8LB United Kingdom

Course Overview: This course will provide an introduction to working with real-life data typical of those encountered in the field of psychology. The course will be delivered by Dr. Dale Barr and Dr. Luc Bussière, who are practicing academics in the fields of psychology and evolutionary biology respectively, with many years of expertise with R and statistical modelling as both scientists and instructors.  This five-day course will consist of series of modules (each lasting roughly half a day) covering topics including the…

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July 2018
£220.00 - £530.00

Social Network Analysis for Behavioural Scientists using R (SNAR01)

2 July 2018 - 5 July 2018
PS statistics head office, 53 Morrison Street
Glasgow, Scotland, G5 8LB United Kingdom

Course Overview: This workshop will provide students with the opportunity to learn how to use social network analysis to analyze social relational datasets such as human friendship networks or animal social networks. Attendees will learn how to use R and several R packages including igraph, sna, network, asnipe, timeordered, tsna to create network graphs, calculate descriptive network metrics, use randomization and random models to evaluate the significance of these metrics, determine graph structural properties including community structures, use QAP and…

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October 2018
£275.00 - £500

Introduction to Frequentist and Bayesian mixed (Hierarchical) models (IFBM01)

8 October 2018 - 12 October 2018
PS statistics head office, 53 Morrison Street
Glasgow, Scotland, G5 8LB United Kingdom

Course Overview: This course will cover introductory mixed or hierarchical modelling (fixed and random effects models) for real-world data sets from both a Frequentist and 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 mixed modelling. All methods are demonstrated with data sets which participants can run themselves. Participants will be taught how to fit hierarchical models using both the standard lme4…

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December 2018
£275.00 - £510.00

Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan (BDRS01)

3 December 2018 - 7 December 2018
PS statistics head office, 53 Morrison Street
Glasgow, Scotland, G5 8LB United Kingdom

Course Overview: This course provides a general introduction to Bayesian data analysis using R and the Bayesian probabilistic programming language Stan. We begin with a gentle introduction to all the fundamental principles and concepts of Bayesian data analysis: the likelihood function, prior distributions, posterior distributions, high posterior density intervals, posterior predictive distributions, marginal likelihoods, Bayes factors, etc. We will do this using some simple probabilistic models that are easy to understand and easy to work with. We then proceed to…

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