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Infer.NET User Guide
Introduction
What is Infer.NET?
A simple example
How Infer.NET works
Frequently Asked Questions
Resources and References
Building probabilistic models
The Infer.NET modelling API
Creating variables
Variable types and their distributions
Vector and Matrix types
Applying functions and operators to variables
Attaching constraints to variables
Working with arrays and ranges
Arrays and ranges
ForEach blocks
Jagged arrays
Indexing arrays by observed variables
Cloning ranges
Branching on variables to create mixture models
The importance of using SetTo
Computing model evidence for model selection
Factors and constraints
Distribution factors
Boolean and comparison operations
Arithmetic and mathematical operations
Linear algebra factors
Constraints
Undirected factors
Miscellaneous factors
List of factors and constraints
Advanced model building
Adding attributes to your model
The Model Specification Language
Running inference on your model
Running inference
Inference engine settings
Working with different inference algorithms
Expectation Propagation
Variational Message Passing
Gibbs sampling
Monitoring the progress of inference
Quality bands
Controlling how inference is performed
Customising the algorithm initialisation
Using a precompiled inference algorithm
Structure of generated inference code
Debugging inference
Improving the speed and accuracy of inference
Using sparse messages
Sharing variables between models
Shared variable arrays
Defining shared variables within a model
Jagged shared variable arrays
Computing evidence for models with shared variables
Using shared variables to support hybrid algorithms
Extending Infer.NET
Infer.NET component architecture
Adding a new distribution type
Adding a new factor and its message operators
Adding a new constraint
Modifying the operator search path
Calling Infer.NET from other languages
F#
Variable Arrays in F#
Inference in F#
Operator Overloading in F#
Imperative Statement Blocks in F#
C++/CLI
Iron Python
Mono
Silverlight
Tutorials and examples
The Examples Browser
Tutorials:
Two coins
,
Clinical trial
,
Mixture of Gaussians
,
more...
Examples:
Latent Dirichlet Allocation
,
Multi-class classification
,
Bayesian PCA
,
Discrete Bayesian network
,
more...
How-to guides:
How to handle missing data
,
How to build scalable applications
,
more...
Code documentation
Release change history
Resources and References
Last modified at 9/29/2011 11:45 AM by
John Guiver
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