Release change history
- Infer.NET now requires .NET framework version 4.5. If your project uses
an earlier version of .NET then you will get errors of the form "The type or
namespace name 'MicrosoftResearch' could not be found".
- Infer.NET now includes two pre-built learners: a classifier and recommender
system. These can be invoked directly from the command-line or a .NET language,
without having to learn the Infer.NET API.
- Infer.NET Fun is no longer supported.
- Chain and grid models defined using offset indexing are now given efficient
- Added the Chess Analysis example.
- Added support for random Wishart matrices in EP. The Mixture Of Gaussians
tutorial now works with EP.
- The TruncatedGaussian tutorial now works with VMP.
- Factor graphs can now be viewed in Visual Studio using
- Variable.If(value==x) is now allowed and is equivalent to
- Variable.Copy(VariableArray) now returns a VariableArray.
- Added Variable.Double.
- Added Variable.Replicate and Variable.InitialiseBackwardTo for more precise
control over initialisation.
- Improved support for Variable.IList as an alternative to Variable.Array.
- Improved support for product and ratio of Gamma-distributed variables.
- Added ModelCompiler.UseExistingSourceFiles option for debugging.
- Added ModelCompiler.AllowDerivedParents option.
- Added GetMode, FromDerivatives method to various distributions.
- Added Discrete.SetToPadded. Some methods on Discrete now allow mismatched
dimensions (with implied padding).
- Added MMath.BesselI, the incomplete Gamma function, and the incomplete Beta
- Added Vector.FindAll.
- Vector.Outer now returns a Matrix instead of PositiveDefiniteMatrix.
- Improved support for Poisson and Binomial distributions.
- Improved handling of point-mass priors and initialisers in EP operators.
- Improved handling of user-provided initialisers.
- Improved precision of GaussianOp, IsBetween, GammaFromShapeAndRate, and
- Added experimental EP support for string random variables and operations on
strings such as concatenation, formatting, etc.
- Added a new series of tutorials on inference with string random variables.
2.5 Beta 2 (18th April 2013)
- Added engine.Compiler.TreatWarningsAsErrors
- SetToRatio now takes an additional argument
- Variable.Random(PointMass) is equivalent to Variable.Constant, constants can
be inferred, point masses are quoted correctly for various distributions
- Variable.If(x==value) is equivalent to Variable.Case(x,value).
Variable.If(x==i) inside of ForEach(i) is equivalent to Variable.Switch(x).
- Compiler produces better message-passing schedules
- null constants are allowed
- Improved accuracy of GaussianFromMeanAndPrecision with unknown precision for
- InitialiseTo can be applied to array elements, making it simpler to define
initialisers for arrays, such as x[item].InitialiseTo(xInitArray[item]);
- SparseVectors display in a more compact format, and display as dense when
they are actually dense
- Wishart.Uniform bugfix
- Added Wishart.FromMeanAndMeanLogDeterminant, Discrete.GetMedian
- Added Variable.MatrixTimesScalar, Variable.WishartFromShapeAndRate,
- Added Matrix.Parse
- MatlabReader/Writer now support multidimensional arrays
- Added QueryTypes.MarginalDividedByPrior to replace InferOutput.
IGeneratedAlgorithm no longer implements GetOutputMessage (use Marginal with
- Infer(x, QueryType) requires x to have the QueryType as an attribute, for
- WetGrassSprinklerRain now works with VMP
- Multi-class Bayes Point Machine has been re-architected to be more robust
and efficient for large numbers of classes
- Matrix.SetToOuterTranspose bugfix (affected all VectorGaussians with
dimension >= 40)
- Infer.NET Fun inference: new strongly-typed inference function (infer)
directed by user-specified compound distribution type; new Model<A,B> type that
avoids model recompilation for changes to input data
- Infer.NET Fun syntax: additional operations for in-model symmetry breaking
and providing range, dimension and sparsity information to Infer.NET when
2.5 Beta 1 (28th September 2012)
- Infer.NET is now released as a zip folder
- Infer.NET now requires .NET framework version 4.
- Added Variable.Repeat blocks to the modelling API. The LDA example now uses
- Infer.NET Fun, which allows you to write many models directly in F#, is now
included as part of the Infer.NET release.
- New examples: Recommender System, DifficultyAbility, and StudentSkills.
- Some examples that were previously stand-alone solutions have been moved
into the Examples Browser.
- InferenceEngine.ShowFactorGraph shows constant values, among other
- Removed engine.BrowserMode.
- Added engine.Compiler.UseSerialSchedules option.
- Added engine.Compiler.AddComments option.
- Models undergo additional checks for validity. x.SetTo(y) now gives an
error if y has been used in any other expressions.
- The generated class is now marked 'partial'.
- Distributions now implement GetLogAverageOfPower.
- MatlabWriter supports bool and int.
- Distributions can be serialized into XML via DataContractSerializer.
- Matrix.SetToEigenvectorsOfSymmetric can compute eigenvectors and eigenvalues
of a symmetric matrix.
- GammaFromShapeAndRate supports a Gamma-distributed shape with VMP, and a
Gamma-distributed rate with EP.
- Variable.Binomial supports a Poisson-distributed trialCount. '+' supports
- Variable.Constrain methods are more efficient and consistent with each
other. Variable.ConstrainTrue(x) is now equivalent to
Variable.ConstrainEqual(x,true), and similarly for ConstrainFalse,
ConstrainPositive, and ConstrainBetween.
- Variable.Subarray is more efficient in certain cases.
- Removed a bug that sometimes caused the compiler to never complete.
- Removed a bug that prevented use of Variable.GetItem on Vectors.
2.4 Beta 3 (11th October 2011)
- Variable.Softmax() now accepts sparse vectors and is more robust.
- Speed and memory improvements to LDA example.
- Improvements to the generated code, especially the message-update schedules.
- Improved evidence calculations for TruncatedGaussians.
- Variable.Switch is more efficient when dealing with a large number of cases.
- Variable.Logistic is more robust with ExpectationPropagation.
- GetItems and Subarray now support jagged arrays.
- Infer.NET now checks that observed arrays are the correct length, at the
point when the observation is set. This may break some existing user code where
observed arrays are set before the array length is set.
- Variable.ForEach(...) supports any number of ranges.
- Variable.Array() has new overloads for making deep jagged VariableArrays.
- Improved handling of deterministic factors in 'if' blocks (fewer spurious
AllZeroExceptions are thrown).
- Dirichlet.Uniform(dimension, initialCount) is now called
- Infer.NET introduction document.
- Infer.NET 101 document which comes with a series of examples.
- DivideMessages option for Shared variables.
- Bayesian PCA and Discrete Bayesian Network examples.
- Improved the Rand.Poisson sampler.
2.4 Beta 2 (17th December 2010)
- EP now uses message division by default, which speeds up many
models. To get the old behavior for a particular variable, add the new
DivideMessages attribute to the variable.
- Setting variable.ObservedValue to any value will clear cached inference
results, even if the variable was already observed to that value.
- Improved efficiency of Gibbs sampling.
- Gibbs sampling works better with nested if statements.
- Improved numerical accuracy of various factors.
- Improved support for TruncatedGaussian distributions.
- Added Rand.NormalBetween for sampling from truncated Gaussians.
- Added WrappedGaussian distribution and Rotate factor.
- Inference is no longer allowed for local variables in a ForEach block (this
has caused some minor changes to the Multi-class BPM classifiers).
2.4 Beta 1 (30th October 2010)
- Extensive improvements to the documentation + additional examples.
- Introduction of quality bands for inference components (algorithms,
operators, distributions) to make transparent which components are
mature/preview/experimental. Quality auditing functions for giving errors or
warnings if certain quality levels are not met by all used components.
- New method of fine tuning what variables are inferred using the
OptimiseForVariables property on InferenceEngine.
- Preliminary (experimental) support for max product belief propagation in
- Added Silverlight version of the Infer.NET runtime to support using
pre-compiled models in Silverlight 3.0 or above.
- Multicore support now uses .NET framework 4.0 support for parallel tasks (to
use set engine.Compiler.UseParallelForLoops = true)
- Added experimental support for sparse Gaussian Processes - see
Gaussian process classifier for an example.
- Added the DoNotInfer attribute
- Added Compiling and Compiled events to monitor model compilation
- Ability to monitor the progress of inference using the ProgressChanged event
- Added SumWhere factor
- The indexer of a loop can now be accessed using the Index property on a
ForEachBlock. Also, loops can now be cloned.
- Accuracy improvements to several factors, including: BernoulliFromLogOdds,
DiscreteFromLogProbs, Logistic, Softmax, and Exp.
- Improved efficiency of factors that manipulate Vectors (GetItem, Concat,
- Many optimisations of the generated code, reducing computational cost and
memory consumption. These are controlled by the new OptimiseInferenceCode
switch on the model compiler.
- The generated code now follows a different structure, documented in the user
guide. Applications which call directly into generated code will need to be
- Reduced memory consumption in the compiler and the generated code. Added
- Repeated inference on the same model with different observed data is more
efficient. Added Update() method to warm-start the fixed-point iterations.
- Added ability to control the operator search path, which allows selection
between alternate implementations of an operator (e.g. with different
- Deterministic variables (e.g. as a result of observations) can now be
- SparseVector and SparseList classes.
- Support for sparse messages to reduce memory consumption in models with
integers variables over large domains.
- Improvements to Gibbs sampling.
- Several new examples including an LDA wrapper with various scalability
2.3 Beta 4 (12th November 2009)
- Added SharedVariableArray2D.
- Variable.GammaFromShapeAndScale now supports random parameters.
- Added support for multiplication of a Gaussian-distributed variable with a
Gamma-distributed or Beta-distributed variable.
- Added Variable.Vector for converting random arrays to random Vectors.
- MatlabReader added.
- Improved Gibbs sampling - more models and speed improvements.
- Supports VisualStudio 2010 beta 2 and October 2009 CTP releases of F#, and
fixes F# import error for MicrosoftResearch.Compiler.dll
2.3 Beta 3 (4th September 2009)
- Some bug fixes for 2.3 Beta 2.
2.3 Beta 2 (27th August 2009)
- BernoulliFromLogOdds and Logistic now support Expectation Propagation.
- Added plus operator and comparison operators for integer variables.
- Added Concat, Subvector, and GetItem factors for Vector variables.
- DiscreteUniform now allows a random size.
- Observed variables can now be inferred (the result is a point mass
distribution on the observed value).
- Changed the order of arguments to Binomial and Multinomial.
- Beta distributions print out differently.
2.3 Beta 1 (3rd August 2009)
- Added Variable.Logistic, Variable.Softmax,
Variable.Binomial, Variable.Multinomial, Variable.AllTrue factors.
- EP evidence is now computed in a different way, which is more numerically
stable. This is relevant when implementing new factors or calling operator
methods directly. Specifically, the definition of LogEvidenceRatio (the method
for computing EP evidence) has changed. However the overall evidence value is
the same as before.
- Jagged arrays can now be initialized using InitialiseTo.
- Model is now recompiled when trying to infer a variable not included in an
- Improved handling of nested Switch blocks.
- Improved handling of If and Case blocks with non-random conditions.
- Better support for SharedVariableArrays and arrays defined by SetTo.
- Can now index 2D arrays by observed variables (both indices must be
- Added Variable.GammaFromShapeAndRate.
- Reduced memory allocation in the generated code. When possible, messages
are now allocated once in the Reset() method and reused across calls to Infer.
- Added InferenceEngine.ReturnCopies flag.
- Removed a confusing overload of InferenceEngine.Infer<>
- Improved accuracy of PositiveDefiniteMatrix.SetToInverse. Added
- Experimental multicore support using Parallel Extensions library (to use
set engine.Compiler.UseParallelForLoops = true)
- Support for enum types with Variable.EnumDiscrete()
- Some efficiency improvements for If and Switch blocks
- Support for returning arrays of distributions from Infer e.g.
- Gibbs sampling. Some factors not yet supported such as gates and array
- F# wrapper - hides some of API complexity, providing distribution and
domain-specific Variable and Distribution arrays types.
- IronPython wrapper - hides some of API complexity for IronPython users
- Runs on Linux with Mono
2.2 Beta 2 (7th January 2009)
- Fixed bug in multiplication of a Gamma variable
- Fixed problem with locales which use a comma to represent a decimal point
- Tutorial example for mixture of Gaussians now matches documentation
- Reduced memory consumption when transform browser mode is set to 'never'
- Documented examples using F#, C++/CLI, and IronPython
2.2 Beta 1 (5th December 2008)
Version 2.2.31202 (2nd December 2008)
- Given, Constant and RandomVariable classes are deprecated. Use Variable
instead, and set the ObservedValue and IsReadOnly properties as described in
- Support for jagged arrays
- Variables must now be defined in all branches of an If or Case block unless
they are local to that block
- If, Case, and Switch statements can now take non-random conditions
- Generated code is now fully commented
- DLL structure simplified to Infer.Compiler.dll and Infer.Runtime.dll
- Namespace changes:
- Algorithms moved to MicrosoftResearch.Infer
- Many utility classes moved into MicrosoftResearch.Core namespace and
sub-namespaces (e.g. MicrosoftResearch.Core.Math)
- Many classes have been marked internal
- DistributionArray etc are now for internal use only. They implement
IDistribution, and the API provides methods to retrieve .NET arrays of
- Several methods for creating random int/Vector variables now optionally take
a range to indicate the cardinality/dimensionality of the variable
- Easier to use generated code in a standalone fashion
- Distribution classes are now serializable
- Shared variable improvements including support for SharedVariableArray
- Support for extracting multiple elements from an array using indexing
- Snapshot of the online documentation is now included in the installed
- Many bug fixes
Internal version 2.1.30904 (4th September 2008)
- Removed dependence on Reflector.
- Support for 3D random variable arrays.
- InitialiseTo() can take a Given, so that initializers may be changed at
- Added Variable.Max for taking the maximum of two random doubles.
- Added Variable.Copy, Variable.PointMass, Variable.Uniform for various
- Added operator overloads for +,-,*,/ random variables with constants.
- Added ^ operator for VectorGaussian.
- Added option InferenceEngine.Compiler.WriteSourceFiles=false to prevent
writing source files (they are compiled in memory instead).
- Improved accuracy of SetToSum methods.
- Improved accuracy of message-passing across if/case/switch blocks.
- Expectation Propagation now handles Beta/Dirichlet distributions in a more
robust way, reducing the occurrence of ImproperMessageExceptions.
- SetToSum now forces a proper distribution by default for Beta/Dirichlet.
Setting the static field AllowImproperSum=true restores the old behavior.
- The order that loop variables appear in array indices can now be different
from the nesting order of the loops.
- More fixes to SetTo in if/case/switch blocks.
- Fixed handling of AreEqual factor in VMP.
- Fixed evidence computation for various factors.
- Fixed bugs in scheduling.
- Fixed bug in Rand.Perm.
- Fixed handling of nested if/case blocks.
Internal version 2.1.30523 (23rd May 2008)
- New API for SharedVariables.
- Fixed handling of SetTo in if/case/switch blocks.
- Fixed handling of And and Or factors in VMP.
- Fixed handling of array variables defined inside gates (they were sometimes
incorrectly inferred as constant).
- VectorGaussian marginal prototype is now inferred for Factor.VectorGaussian
with constant arguments.
- GivenArray.Value and ConstantArray.Value are now IList<T> instead of T.
- Reduced the occurrence of improper message exceptions during EP.
- InitialiseTo only accepts distribution classes (not arrays as previously).
- Range constructor no longer accepts a name (use the Named method instead).
- Improved handling of boundary cases in Factor.IsBetween.
- Variable.ConstrainBetween is more efficient (has a dedicated operator
- Speed ups to DistributionArray
- Changed default GeneratedSource folder to be below the current folder.
- The name of generated classes can now be specified, using the inference
engine ModelName property.
- Removed debug messages, and instead made InferenceEngine.ShowProgress
default to true (on).
Internal version 2.1.30320 (20th March 2008)
- Proper attribute added to message function parameters which must be
proper. Currently, this has the same behaviour as the "SkipIfUniform"
- Improved handling of corner cases in SetToSum.
- Shared variables work with VMP
- Marginal prototypes are propagated automatically for Factor.GetItem and
- XML code documentation added to install for improved Intellisense
- Some optimisations for message operators
- Improved diagnostic messages during compile
Internal version 2.1.30310 (10th March 2008)
- Increased accuracy in some EP messages
- Improper message exceptions happen less often
- Improved scheduling of VMP
- Fixed indexing of constant/given arrays by random integers
- Fixed click model example
Internal version 2.1.30305 (5th March 2008)
- Expectation propagation (EP)
- Variational Message Passing (VMP)
- Belief propagation (sum-product algorithm) as a special case of EP.
- Exponential family distributions: Gaussian, Gamma, Beta, etc.
- Factors for arithmetic and boolean operations
- Modelling API
- Model compilation for efficient execution
- Consistent operator syntax for message computation
- Evidence computation
- Many application samples and tutorial examples