Tutorials & Examples


The following tutorials provide a step-by-step introduction to Infer.NET.  Can be viewed through the Examples Browser.

  1. Two coins - a first tutorial, introducing the basics of Infer.NET.
  2. Truncated Gaussian - using variables and observed values to avoid unnecessary compilation.
  3. Learning a Gaussian - using ranges to handle large arrays of data; visualising your model.
  4. Bayes Point Machine - demonstrating how to train and test a Bayes point machine classifer.
  5. Clinical trial - using if blocks for model selection to determine if a new medical treatment is effective.
  6. Mixture of Gaussians - constructing a multivariate mixture of Gaussians.

String Tutorials

The following tutorials provide an introduction to an experimental Infer.NET feature: inference over string variables. The first two tutorials can be viewed through the Examples Browser, and the third one is available as a separate project.

  1. Hello, Strings! - introduces the basics of performing inference over string variables in Infer.NET.
  2. StringFormat Operation - demonstrates a powerful string operation supported in Infer.NET, StringFormat.
  3. Motif Finder - defining a complex model combining string, arrays, integer arithmetic and control flow statements.

Short Examples

Short examples of using Infer.NET to solve a variety of different problems.  Can be viewed through the Examples Browser.

  • Bayesian PCA and Factor Analysis - how to build a low dimensional representation of some data by linearly mapping it into a low dimensional manifold.
  • Rats example from BUGS - a hierarchical normal model, used to illustrate Gibbs sampling.
  • Click model - an information retrieval example which builds a model to reconcile document click counts and human relevance judgements of documents.
  • Difficulty versus ability - a model of multiple-choice tests and crowdsourcing.
  • Gaussian Process classifier - a Bayes point machine that uses kernel functions to do nonlinear discrimination.
  • Recommender System - a matrix factorization model for collaborative filtering.
  • Student skills - cognitive assessment models for inferring the skills of a test-taker.
  • Chess Analysis - comparing the strength of chess players over time.
  • Discrete Bayesian network - uses Kevin Murphy's Wet Grass/Sprinkler/Rain example to illustrate how to construct a discrete Bayesian network, and how to do parameter learning within such a model.

Longer Examples

How-to Guides

How to achieve various general tasks in Infer.NET.

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