Tutorials & Examples
The following tutorials provide a step-by-step introduction to Infer.NET. Can be viewed through the Examples Browser.
- Two coins - a first tutorial, introducing the basics of Infer.NET.
- Truncated Gaussian - using variables and observed values to avoid unnecessary compilation.
- Learning a Gaussian - using ranges to handle large arrays of data; visualising your model.
- Bayes Point Machine - demonstrating how to train and test a Bayes point machine classifer.
- Clinical trial - using if blocks for model selection to determine if a new medical treatment is effective.
- Mixture of Gaussians - constructing a multivariate mixture of Gaussians.
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.
- Hello, Strings! - introduces the basics of performing inference over string variables in Infer.NET.
- StringFormat Operation - demonstrates a powerful string operation supported in Infer.NET, StringFormat.
- Motif Finder - defining a complex model combining string, arrays, integer arithmetic and control flow statements.
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.
How to achieve various general tasks in Infer.NET.