'Prior Exposure' Bayesian data analysis workshop 1: Bayes for beginners

Description:

Workshop 1: Bayes for Beginners

(Note that this is part of a series of four workshops that can be attended as stand-olone sessions or in sequence. Workshop 2 will run on the following day in the same venue. Workshops 3 and 4 will run later this year. We plan to run similar workshops in 2017).

Software:  Some parts of the workshop will use the statistical software environment R. Attendees interested in attending an introductory R workshop on 30th March should contact the organizers.

Bursary information A limited number of ESRC funded bursaries (to cover travel and subsistence costs) are available to UK social science doctoral .students

Prerequisites The only prerequisite for the initial workshop will be familiarity with the standard repertoire of statistical tools that are used in psychology and other social sciences – for example, tests such as t tests, ANOVA, correlation and regression – as well as fundamental concepts of classical statistical inference such as p values and null hypothesis significance tests.

Content This workshop aims to be a general introduction to BDA (Bayesian Data Analysis) and how it differs from the more familiar classical approaches to data analysis. We will start by providing a brief historical overview of statistical infer- ence and introduce Bayes’s theorem. The fundamental concepts of Bayesian statistical inference will follow, contrasted with frequentist methods of inference. To provide a bridge between Bayesian and classical methods, we will describe likelihood function approaches to inference and introduce both the likelihood principle and the law of the likelihood as the general precepts of likelihood based inference. During this workshop, there will also be practical exercises including using Bayes’s rule to calculate posterior probabilities and posterior distributions, choosing priors in probabilistic models and illustrating their role on the posterior distributions, calculating likelihood ratios and Bayes factors to compare evidence for different parameters in a probabilistic model, and calculating marginal likelihoods for comparing distinct probabilistic models.

Learning outcomes On completion of this workshop, attendees will be familiar with the philosophical and practical issues of both the classical and Bayesian approaches to statistical inference. They should be able to apply this knowledge to simple practical research questions and be able to engage with work using Bayesian methods in their area (e.g., as a reviewer or editor).

Indicative reading

Dienes, Z. (2008). Understanding psychology as a science: An introduction to scientific and statistical inference. Palgrave Macmillan.

Baguley, T. (2012). Serious stats: A guide to advanced statistics for the behavioral sciences. Palgrave Macmillan. 

Cost:

£10 (postgraduate students) or £20 (others)

Website and registration:

http://onlinestore.ntu.ac.uk/short-courses/school-of-social-sciences/workshops

Region:

East Midlands

Keywords:

Quantitative Data Handling and Data Analysis, Statistical Theory and Methods of Inference

Related publications and presentations:

Quantitative Data Handling and Data Analysis
Statistical Theory and Methods of Inference

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