Configuring Anacondas for Bayesian Analytics with STAN

It’s not as popular as it once was, but Bayesian Analytics remains a powerful tool for more supervised learning exercises.

Despite all the hype around Deep Learning Models, and AI as a Service APIs, there’s still a need for Data Scientists to explain – in simple terms – what factors influence a given prediction. And even more importantly, sometimes we want to construct a model that represents real world process, rather than have a input values feed into a programmatically optimized series of neural networks and produce a predicted value.

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