
ONLINE COURSE – Introduction to Stan for Bayesian Data Analysis (ISBD01) This course will be delivered live
2 June 2021 - 3 June 2021
£275.00
This course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE – UK local time (GMT+0) – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).
Course Overview:
Stan (https://mc-stan.org) is “a state-of-the-art platform for statistical modeling and high-performance statistical
computation. Thousands of users rely on Stan for statistical modeling, data analysis, and prediction in the social,
biological, and physical sciences, engineering, and business.” Stan is a powerful programming language for developing
and fitting custom Bayesian statistical models. In this course, we provide a general introduction to the Stan language,
and describe how to use it to develop and run Bayesian models. We begin by first covering the theory behind Stan,
which covers Bayesian inference, Markov Chain Monte Carlo (MCMC) for sampling from probability distributions, and
the efficient Hamiltonian Monte Carlo (HMC) method that Stan implements. Next, we learn how to write Stan models by
creating simple Bayesian such as binomial models and models using normal distributions. In so doing, the basics of the
Stan language will be apparent. Although Stan can be used with multiple different type of statistical programs (Python,
Julia, Matlab, Stata), we will use Stan with R exclusively, specifically using the rstan or cmdstanr packages. Using these
packages, we will can compile and sample from a HMC sampler for the Bayesian models we defined, plot and
summarize the results, evaluate the models, etc. We then cover some widely used and practically useful models
including linear regression, logistic regression, multilevel and mixed effects models. We will end by covering some
more complex models, including probabilistic mixture models.
THIS IS ONE COURSE IN OUR R SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES
Intended Audience
This course is aimed at anyone who is in interested in doing advanced Bayesian data analysis using Stan. Stan is a state
of the art tool for advanced analysis across all academic scientific disciplines, engineering, and business, and other
sectors.
Venue – Delivered remotely
Time zone – GMT+0
Availability – TBC
Duration – 2 days
Contact hours – Approx. 15 hours
ECT’s – Equal to 1 ECT’s
Language – English
PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.