Keanu is a general-purpose probabilistic programming library. The main design goal is to build a Bayesian inference tool that scales to a point where we can model the world and therefore optimize it.

Learn how to install Keanu in a new or existing project.

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Learn what sort of problems Keanu can solve.

Learn how to describe a model in Keanu.

Learn how to ingest and write out data to/from CSV.

Learn how to use vertices - the fundamental components of models in Keanu.

Learn about how tensors can be used to simplify your Bayesian network.

Learn how to use inference to find the most probable values for random variables given your data.

Learn how to infer the probability of some event given your data.

Learn how to quickly construct linear/logistic regression models.

Learn how to use a Particle Filter to find probable states for your network.

Learn how to use sequence templates to describe repeating subgraphs in Keanu or to do time series modelling.

Learn how to calculate autocorrelation on your samples.

Learn how to Save and Load constructed & trained models

Learn from a real-world example.

Learn how to do inference even on chaotic systems