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Introduction to Bayesian Inference with Stan: Sydney, 27 February–1 March 2019
27 February 2019 @ 9:30 am - 1 March 2019 @ 5:00 pm
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Despite the promise of big data, inferences are often limited not by the size of data but rather by its systematic structure. Only by carefully modeling this structure can we take full advantage of the data—big data must be complemented with big models and the algorithms that can fit them. Stan is a platform for facilitating this modeling, providing an expressive modeling language for specifying bespoke models and implementing state-of-the-art algorithms to draw subsequent Bayesian inferences.
In this three-day course, we will introduce how to implement a robust Bayesian workflow in Stan, from constructing models to analyzing inferences and validating the underlying modeling assumptions. The course will emphasize interactive exercises run through RStan, the R interface to Stan, and PyStan, the Python interface to Stan.
We will begin by surveying probability theory, Bayesian inference, Bayesian computation, and a robust Bayesian workflow in practice, culminating in an introduction to Stan and the implementation of that workflow. With a solid foundation we will continue with a discussion of regression modeling techniques along with their efficient implementation in Stan, spanning linear regression, discrete regression, and homogeneous and heterogeneous logistic regression. Time permitting, we will consider the practical implementation of advanced modeling techniques at the state of the art of applied statistics research—such as Gaussian process priors and the horseshoe prior.
Prerequisites
The course will assume familiarity with the basics of calculus and linear algebra.
To participate in the interactive exercises, attendees must provide a laptop with the latest version of RStan or PyStan installed. Users are encouraged to report any installation issues at the Stan forum as early as possible.
The instructor: Michael Betancourt
Michael Betancourt is a research scientist with Symplectomorphic, where he develops theoretical and methodological tools to support practical Bayesian inference. He is also a core developer of Stan, where he implements and tests these tools. In addition to hosting tutorials and workshops on Bayesian inference with Stan, he also collaborates on analyses in epidemiology, pharmacology, and physics, among others. Before moving into statistics, Michael earned a BS from the California Institute of Technology and a PhD from the Massachusetts Institute of Technology, both in physics. Find out more at Michael’s website.
To read what students are saying about Michael’s courses, please scroll to the bottom of his consulting page.
Earlybird pricing is available until 13 February 2019.
Group discounts also apply during the earlybird period: 5% for 2–4 people, 10% for 5–6 people, 15% for 7–8 people, and 20% for 9 or more people. Select your desired quantity of tickets and click “Add to cart” to see the discount calculated before checkout.
Please contact us at academy.anz@alphazetta.ai to find out more about these special rates.
About our training
Eugene Dubossarsky’s courses are unlike those offered in universities, online, or by private providers. His data-science classes, in particular, give clients not just knowledge of a process, but the real power of understanding the underlying concepts, allowing them to confidently practice, manage, promote and risk-assess data science.
Dr Dubossarsky says “the way many courses teach data science is like teaching people to memorise and recite poetry in a language they do not understand”. By contrast, he confers an understanding of that language, taught in an intuitive, accessible way that leaves trainees with an instinct for data science. Keeping formulae and mathematics to a bare minimum and taking an intuitive, visual approach, Eugene’s courses deliver a compressed mentoring experience as much as they do content. This is difficult for an average trainer to replicate. Trainees benefit from his extensive knowledge and over 20 years of commercial data-science experience, as well as his unique teaching style.
The resulting testimonials speak for themselves, and candidates come from all walks of life: CEOs, general managers, salespeople, IT professionals, marketing staff, public servants and of course people from many functions in the finance world. These testimonials are extensive, and many more are available on request. With specific regard to finance, Eugene has mentored and advised senior leaders and their teams in a number of major Australian banks.
Questions and further details
Meals and refreshments
Catered morning tea and lunch are provided on both days of the course. Please notify us at least a week ahead if you have any special dietary requirements.
Feedback
Use academy.anz@alphazetta.ai to email us any questions about the course, including requests for more detail, or for specific content you would like to see covered, or queries regarding prerequisites and suitability.
If you would like to attend but for any reason cannot, please also let us know.
Variation
Course material may vary from advertised due to demands and learning pace of attendees. Additional material may be presented, along with or in place of advertised.
Cancellations and refunds
You can get a full refund if you cancel 2 weeks or more before the course starts. No refunds will be issued for cancellations made less than 2 weeks before the course starts.
Frequently asked questions (FAQ)
Do I need to bring my own computer?
To participate in the interactive exercises, attendees must provide a laptop with the latest version of RStan or PyStan installed. Users are encouraged to report any installation issues at the Stan forum as early as possible.
I'm lost! How do I find the venue?
Please call +61 4 1457 3322 or email academy.anz@alphazetta.ai if you can’t find the venue.
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Please ask about tailored, in-house training courses, coaching analytics teams, executive mentoring and strategic advice and other services to build your organisation's strategic and operational analytics capability.
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- Predictive Analytics, Machine Learning, Data Science and AI
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- Advanced R
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- Introduction to Machine Learning
- Introduction to Data Science
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