Documentation
Case Studies
Extensions
Support
Team


Tutorials & Examples
Tutorials
The following tutorials provide a stepbystep 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.
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.
 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
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 multiplechoice 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 testtaker.
 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
Howto Guides
How to achieve various general tasks in Infer.NET.

false,false,1



