Advanced examples

This section contains a set of examples for Bayesian Inference with RxInfer package in various probabilistic models.

Advanced examples contain more complex inference problems.

• Active Inference Mountain car: This notebooks covers RxInfer usage in the Active Inference setting for the simple mountain car problem.
• Advanced Tutorial: This notebook covers the fundamentals and advanced usage of the RxInfer.jl package.
• Assessing People’s Skills: The demo is inspired by the example from Chapter 2 of Bishop's Model-Based Machine Learning book. We are going to perform an exact inference to assess the skills of a student given the results of the test.
• Chance-Constrained Active Inference: This notebook applies reactive message passing for active inference in the context of chance-constraints.
• Conjugate-Computational Variational Message Passing (CVI): This example provides an extensive tutorial for the non-conjugate message-passing based inference by exploiting the local CVI approximation.
• Global Parameter Optimisation: This example shows how to use RxInfer.jl automated inference within other optimisation packages such as Optim.jl.
• Solve GP regression by SDE: In this notebook, we solve a GP regression problem by using 'Stochastic Differential Equation' (SDE). This method is well described in the dissertation 'Stochastic differential equation methods for spatio-temporal Gaussian process regression.' by Arno Solin and 'Sequential Inference for Latent Temporal Gaussian Process Models' by Jouni Hartikainen.
• Infinite Data Stream: This example shows RxInfer capabilities of running inference for infinite time-series data.
• Nonlinear Sensor Fusion: Nonlinear object position identification using a sparse set of sensors