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July 2019
£275 - £520.00

Introduction to Bayesian hierarchical modelling using R (IBHM03)

8 July 2019 - 12 July 2019
PS statistics head office, 53 Morrison Street
Glasgow, Scotland, G5 8LB 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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October 2019
£275.00 - £520.00

Introduction to Behavioural data analysis using R (IBDA01)

14 October 2019 - 18 October 2019
PS statistics head office, 53 Morrison Street
Glasgow, Scotland, G5 8LB United Kingdom

Course Overview: To follow... Intended Audience Any researchers (from postgraduate students to senior investigators) interested in analysing behavioural data. Examples will be primarily from non-human animal behaviour studies, but the methods will also be applicable to many researchers studying human behaviour. Venue - PS statistics head office, 53 Cook Street, Glasgow, G5 8LB - Google map Availability - 20 places Duration – 5 days Contact hours – Approx. 35 hours ECT’s – Equal to 3 ECT’s Language - English Packages We offer COURSE ONLY and…

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November 2019
£275.00 - £530.00

Behavioural data analysis using maximum likelihood in R (BDML02)

4 November 2019 - 8 November 2019
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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