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Introduction to Bayesian Analysis using Stan (Online)
Royal Statistical Society

23 & 24 September 2025, and 28 September to 1 October 2026 - Certificate of attendance included!

Summary

Price
£768 - £1,002 inc VAT
Study method
Online + live classes
Duration
2 days · Part-time or full-time
Qualification
No formal qualification
Certificates
  • Certificate of Attendance - Free
Additional info
  • Tutor is available to students

Add to basket or enquire

Dates

Start date
End date
28/09/2026
01/10/2026

Overview

This course is being delivered online on 24 & 25 September 2025 and 28 September to 1 October 2026

By the end of the course, delegates will be able to:

  • 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

Certificates

Certificate of Attendance

Digital certificate - Included

Course media

Description

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.

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

Who is this course for?

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.

Requirements

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.

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Legal information

This course is advertised on Reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

FAQs

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