The tutorials below were generated from Jupyter notebooks, which are available in the Gen Quickstart repository.
This tutorial introduces the basics of modeling in Gen. It shows how to perform inference using generic inference algorithms. It does not explore custom inference programming.
This tutorial introduces the basics of inference programming in Gen using iterative inference programs, which include Markov chain Monte Carlo algorithms.
Data-driven proposals use information in the observed data set to choose the proposal distibution for latent variables in a generative model. This tutorial shows you how to use custom data-driven proposals to accelerate Monte Carlo inference. It also demonstrates how ‘black-box’ code, like algorithms and simulators written in Julia, can be included in probabilistic models that are expressed as generative functions.
This tutorial shows how generative function combinators and the static modeling language are used to achieve good asymptotic scaling time of inference algorithms.
This tutorial shows how to implement a particle filter for tracking the location of an object from measurements of its relative bearing.
This tutorial shows how to use Gen’s automated involutive MCMC features to implement reversible-jump proposals, for models that have unknown structure (and not just unknown parameters).
This tutorial shows how to write a generative function that invokes TensorFlow code, and how to perform basic supervised training of a generative function.
This tutorial extends our tutorial on data-driven inference by using PyTorch to build a neural network for amortized inference.
This tutorial describes the reasoning behind some of the basic concepts in Gen.
This tutorial explains some of the mathematical details of MCMC in Gen.