Small-group training and one-to-one coaching for data visualisation and Bayesian analysis.
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.
Bayesian non-parametrics are not just a trendy, must-have skill, but also offer a combination of the non-linearity of black boxes like deep learning, without the mystery. 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.
BUGS / JAGS
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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