ExpectationMaximizationTObservation Class 
Namespace: Accord.Statistics.Distributions.Fitting
The ExpectationMaximizationTObservation type exposes the following members.
Name  Description  

ExpectationMaximizationTObservation 
Creates a new ExpectationMaximizationTObservation algorithm.

Name  Description  

Coefficients 
Gets the current coefficient values.
 
Convergence 
Gets or sets convergence properties for
the expectationmaximization algorithm.
 
Distributions 
Gets the current component distribution values.
 
Gamma 
Gets the responsibility of each input vector when estimating
each of the component distributions, in the last iteration.
 
InnerOptions 
Gets or sets the fitting options to be used
when any of the component distributions need
to be estimated from the data.
 
ParallelOptions 
Gets or sets the parallelization options for this algorithm.
(Inherited from ParallelLearningBase.)  
Token 
Gets or sets a cancellation token that can be used
to cancel the algorithm while it is running.
(Inherited from ParallelLearningBase.) 
Name  Description  

Compute(TObservation) 
Estimates a mixture distribution for the given observations
using the ExpectationMaximization algorithm.
 
Compute(TObservation, Double) 
Estimates a mixture distribution for the given observations
using the ExpectationMaximization algorithm, assuming different
weights for each observation.
 
Equals  Determines whether the specified object is equal to the current object. (Inherited from Object.)  
Finalize  Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)  
GetHashCode  Serves as the default hash function. (Inherited from Object.)  
GetType  Gets the Type of the current instance. (Inherited from Object.)  
LogLikelihood(Double, IDistributionTObservation, TObservation) 
Computes the loglikelihood of the distribution
for a given set of observations.
 
LogLikelihood(Double, IDistributionTObservation, TObservation, ParallelOptions) 
Computes the loglikelihood of the distribution
for a given set of observations.
 
LogLikelihood(Double, IDistributionTObservation, TObservation, Double, Double) 
Computes the loglikelihood of the distribution
for a given set of observations.
 
LogLikelihood(Double, IDistributionTObservation, TObservation, Double, Double, ParallelOptions) 
Computes the loglikelihood of the distribution
for a given set of observations.
 
MemberwiseClone  Creates a shallow copy of the current Object. (Inherited from Object.)  
ToString  Returns a string that represents the current object. (Inherited from Object.) 
Name  Description  

HasMethod 
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)  
IsEqual 
Compares two objects for equality, performing an elementwise
comparison if the elements are vectors or matrices.
(Defined by Matrix.)  
To(Type)  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)  
ToT  Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) 
This class implements a generic version of the ExpectationMaximization algorithm which can be used with both univariate or multivariate distribution types.