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ONLINE COURSE – Introduction to Stan for Bayesian Data Analysis (ISBD01) This course will be delivered live

18th January 2022 - 20th January 2022

£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 thesepackages, 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 – EST

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.

Dr. Mark Andrews
Teaching Format
Course Programme

Details

Start:
18th January 2022
End:
20th January 2022
Cost:
£275.00
Event Category:
Event Tags:

Venue

Delivered remotely (United Kingdom)
Western European Time, United Kingdom + Google Map

Tickets

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ISBD01 COURSE ONLY
ISBD01 COURSE ONLY
£ 275.00
Unlimited