BayesCamp is a new startup from Bayesian statistician and Stan contributor Robert Grant. The goal is to provide the best face-to-face training and coaching in the world for Bayesian methods and software.

We provide bespoke on-site courses in Bayesian models or data visualisation, from beginners to specialists. A half-day session can demystify and explain the jargon for managers, recruiters and researchers. A one-day session will give a broad introduction for beginners and get them started in one of the main software packages. Or browse through the more specialised Bayesian topics below. Get in touch to discuss your ideal course.

There are training courses, websites and books aplenty to learn data science skills, but if you are serious about building a career, you need some one-to-one coaching. Discuss and discover your priorities, strengths and weaknesses with an experienced coach. Build a plan to get yourself ahead of the pack, and monitor your progress. Get in touch to start your personal journey.

Throw out everything you thought you knew about data analysis, and start thinking about it in a new, focussed, successful way. Understand and solve each problem as it comes, rather than picking old tools out of the box. Build effective models and know when and why they will work. Stand out as an expert user of Bayesian methods. Join us for an immersive three-day BayesCamp in the country, away from distractions, with your fellow future leaders in data science. Get in touch to find out more.


Structural equation models
SEMs and related latent variable models put your data in the context of the theory and understanding that you already have. Unobservable latent variables drive the observed manifest varibles in ways that are naturally understood, explained and communicated. Bayesian methods are a perfect fit to these methods because estimating the latent variables is done with probability like any other parameters. Combine that with estimation of missing or imprecise data, and hierarchical structure, seasonality or geographical details, and you have a world-beating methodology. Learn how to think it through, implement it in Stan through R or Python, and explain it to others.

When you combine statistical results from multiple studies, there's a lot of things that can and do go wrong. You can include all of those problems in a Bayesian model: reporting bias, publication bias, measurement error, dichotomania, different measures, different study designs, indirect comparisons. Get reliable and realistic inferences and predictions. Or you can brush them under the carpet and pretend it's all fine. Your choice.

Non-parametric models
Bayesian non-parametrics are this year's must-have skill. Only losers are still interested in deep learning. Get flexible, data-driven predictions by combining many models in one Bayesian framework. Let the data shine through without imposing some unrealistic assumptions. Learn how to do it, how to critique it, and how to communicate it.

Adaptive clinical trials
Get the skills you need to cope with the increasing demand for adaptive designs in new clinical trials. Learn how Bayesian methods allow flexible analyses with statistical results that are easy to communicate and meaningful to clinical audiences and policy makers.


The world-leading Bayesian software comes with interfaces for R, Python, Julia and more. It is tried and trusted for almost every kind of model you might want to fit to your data. It is used by every serious data science organisation you can name. But maybe you find it a little scary, a little unapproachable. Let's break through that barrier with an introductory or specialist course.

There are many ways to access powerful Bayesian tools in R, from user-friendly, high-level packages like brms and rethinking through to the NUTS and bolts of Stan, JAGS and BUGS. An overview course will show you the options and let you weigh up which one works best for you and your data.

Bayesian inference Using the Gibbs Sampler is still the world's favourite Bayesian software, and Just Another Gibbs Sampler is close behind. An introductory course will get you up and running with these tools and show you many of the models that have been effectively programmed in them already.

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