Bayesian inference

A Julia library for building and training hybrid dynamic models that combine mechanistic and data-driven components.

In this tutorial, you will learn about different techniques to infer parameters of a (differentiable) process-based model against data.

In this post, I explore the benefits and drawbacks of using empirical (ML)-based models versus mechanistic models for predicting ecosystem responses to perturbations, and further develeop a hybrid approach combining their strengths.

In this tutorial, you’ll learn the basics of approximate Bayesian computation (ABC). ABC is an inference method with very little requirements in terms of the model structure - yet it can be very powerful. It is very simple to apply to any model, and to understand. We’ll play around with Julia, and we will visualize graphically the inference results, so that you can build an intuition of the inference method.