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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.

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