Infer.NET

Tutorials & Examples

Tutorials

The following tutorials provide a step-by-step introduction to Infer.NET. Tutorials and examples can be viewed through the Examples Browser or directly in the Visual Studio project Tutorials.sln. Both of these can be found in the start menu under Infer.NET.

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.

Examples

Examples of using Infer.NET to solve problems in a variety of real world domains, which will include machine vision, information retrieval, bioinformatics and gaming.

Latent Dirichlet Allocation - this example provides Infer.NET implementations of the popular LDA model for topic modeling. The implementations pay special attention to scalability with respect to vocabulary size, and with respect to the number of documents. As such, they provide good examples for how to scale Infer.NET models in general.
Multi-class classification - a series of examples of using Infer.NET for multi-class classification using a Bayes point machine
- Bayes point machine - Perform classification using Multi-class Bayes Point machine
- Bayes point machine shared - Perform classification using Multi-class Bayes Point machine when data is available in chunks
- Sparse Bayes point machine - Perform classification using multi-class Bayes point machine when data is sparse.
Bayesian PCA and Factor Analysis - how to build a low dimensional representation of some data by linearly mapping it into a low dimensional manifold.
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.
Mixed Membership Stochastic Block Model - models relational information among objects (for example individuals in an social network).
Gaussian Process classifier - a Bayes point machine that uses kernel functions to do nonlinear discrimination.
Click through model - a web search example where you convert a sequence of clicks by the user into inferences about the relevance of documents.
Click model - an information retrieval example which builds a model to reconcile document click counts and human relevance judgements of documents.
Rats example from BUGS - a hierarchical normal model, used to illustrate Gibbs sampling.
Image classifier example - an image search example where you classify tagged images by example.
Clinical trial - the clinical trial tutorial example with an interactive user interface. There is a Silverlight version of this also.
Monty Hall problem - an Infer.NET implementation of the Monty Hall problem, along with a graphical user interface.
From the forums and blogs:

Bayesian Linear Regression
Discrete Markov network, more
Conference reviewer model - for estimating submission quality in the light of noisy, biased or uncertain reviewers.
Multinomial Logistic Regression

How-to Guides

How to achieve various tasks in Infer.NET.

How to handle missing data - how to cope with situations where some observations of a variable are missing.
How to build scalable applications - how to scale your applications to cope with large data sets and large value ranges.
How to represent large irregular graphs - how to represent large graph structures efficiently.

Last modified at 9/30/2011 2:22 PM by John Guiver