Introduction to Bayesian Analysis using Stan - Virtual Classroom

Date: Tuesday 07 July 2020 10.30AM - Wednesday 08 July 2020 5.30PM
Location: Online
CPD: 12.0 hours
RSS Training
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This two-day, tutor lead, virtual course is ideal for beginners or intermediate users of Bayesian modelling, who want to learn how to use Stan software within R (the material we cover can easily be applied to other Stan interfaces, such as Python or Julia). We will learn about constructing a Bayesian model in a flexible and transparent way, and the benefits of using a probabilistic programming language for this. The language in question, Stan, provides the fastest and most stable algorithms available today for fitting your model to your data. Participants will get lots of hands-on practice with real-life data, and lots of discussion time. We will also look at ways of validating, critiquing and improving your models. All software will be provided on a web server.
 
This two-day course is ideal for beginners or intermediate users of Bayesian modelling, who want to learn how to use Stan software within R (the material we cover can easily be applied to other Stan interfaces, such as Python or Julia). We will learn about constructing a Bayesian model in a flexible and transparent way, and the benefits of using a probabilistic programming language for this. The language in question, Stan, provides the fastest and most stable algorithms available today for fitting your model to your data. Participants will get lots of hands-on practice with real-life data, and lots of discussion time. We will also look at ways of validating, critiquing and improving your models.

Learning Outcome

  • Use Stan to fit various models to data
  • Check outputs for computational problems, and know what to do to fix them
  • Compare and critique competing models
  • Justify their modelling choices, including prior probability distributions
  • Understand what Stan can and cannot do
 

Topics Covered

  • A quick overview of Bayesian analysis
  • Simulation is useful for statistical inference
  • What is a probabilistic programming language?
  • Parts of a Stan model
  • Univariate models; exploring priors and likelihoods
  • Prior predictive checking
  • Bivariate regression models
  • Predictions and posterior predictive checking
  • Hierarchical models
  • Latent variable models including item-response theory
  • Working with missing and coarse data
  • Gaussian processes
  • Limitations of Stan
 

Target Audience

Anyone with some statistics training who is aware of the advantages of Bayesian modelling could benefit from attending. Fields where this may be most popular are: insurance, political pollsters, finance, marketing, healthcare, education research, psychology, econometrics.

Assumed Knowledge

Attendees should be comfortable with using R, Python, Julia or Stata. They should understand probability distributions and basic regression models, though this can be intuitive and doesn’t have to be mathematically rigorous. They do not need to have used Stan before.
 

Robert Grant

Robert is a trainer, coach and writer on statistics and working in data science, especially data visualisation and Bayesian models. His book, "Data Visualization: charts, maps and interactive graphics," was published by CRC Press.

He is one of the Stan development team, responsible for the Stata interface to Stan. He has worked extensively with Bayesian models in R and Stan on a variety of challenging real-life data analyses.

He worked on clinical audits, analysing hospital quality and safety indicators, at the Royal College of Physicians (1998-2010), then joined and later chaired the NHS England committee overseeing such audit projects. During this time, he also contributed project management and statistical advice and analysis to six guidelines published by the National Institute for Health and Care Excellence (NICE).

He taught statistics and research methods to postgraduate clinical research students at St George's Medical School and Kingston University (2010-2017), and contributed to many health services and biomedical research projects in this time.

 

Fees

   

Registration before
 7 June 2020

 

Registration on/after
 7 June 2020

                                  

Non Member 

RSS Fellow 

RSS CStat/Gradstat also MIS & FIS

 

£611.00+vat 

£520.00+vat 

£490.00+vat

£680.00+vat 

£577.00+vat 

£543.00+vat

 
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