Extreme Optimization™: Complexity made simple.

Math and Statistics
Libraries for .NET

  • Home
  • Features
    • Math Library
    • Vector and Matrix Library
    • Statistics Library
    • Performance
    • Usability
  • Documentation
    • Introduction
    • Math Library User's Guide
    • Vector and Matrix Library User's Guide
    • Data Analysis Library User's Guide
    • Statistics Library User's Guide
    • Reference
  • Resources
    • Downloads
    • QuickStart Samples
    • Sample Applications
    • Frequently Asked Questions
    • Technical Support
  • Blog
  • Order
  • Company
    • About us
    • Testimonials
    • Customers
    • Press Releases
    • Careers
    • Partners
    • Contact us
Introduction
Deployment Guide
Nuget packages
Configuration
Using Parallelism
Expand Mathematics Library User's GuideMathematics Library User's Guide
Expand Vector and Matrix Library User's GuideVector and Matrix Library User's Guide
Expand Data Analysis Library User's GuideData Analysis Library User's Guide
Expand Statistics Library User's GuideStatistics Library User's Guide
Expand Data Access Library User's GuideData Access Library User's Guide
Expand ReferenceReference
  • Extreme Optimization
    • Features
    • Solutions
    • Documentation
    • QuickStart Samples
    • Sample Applications
    • Downloads
    • Technical Support
    • Download trial
    • How to buy
    • Blog
    • Company
    • Resources
  • Documentation
    • Introduction
    • Deployment Guide
    • Nuget packages
    • Configuration
    • Using Parallelism
    • Mathematics Library User's Guide
    • Vector and Matrix Library User's Guide
    • Data Analysis Library User's Guide
    • Statistics Library User's Guide
    • Data Access Library User's Guide
    • Reference
  • Reference
    • Extreme
    • Extreme.Collections
    • Extreme.Data
    • Extreme.Data.Json
    • Extreme.Data.Matlab
    • Extreme.Data.R
    • Extreme.Data.Stata
    • Extreme.Data.Text
    • Extreme.DataAnalysis
    • Extreme.DataAnalysis.Linq
    • Extreme.DataAnalysis.Models
    • Extreme.Mathematics
    • Extreme.Mathematics.Algorithms
    • Extreme.Mathematics.Calculus
    • Extreme.Mathematics.Calculus.OrdinaryDifferentialEquations
    • Extreme.Mathematics.Curves
    • Extreme.Mathematics.Curves.Nonlinear
    • Extreme.Mathematics.Distributed
    • Extreme.Mathematics.EquationSolvers
    • Extreme.Mathematics.Generic
    • Extreme.Mathematics.LinearAlgebra
    • Extreme.Mathematics.LinearAlgebra.Implementation
    • Extreme.Mathematics.LinearAlgebra.IterativeSolvers
    • Extreme.Mathematics.LinearAlgebra.IterativeSolvers.Preconditioners
    • Extreme.Mathematics.Optimization
    • Extreme.Mathematics.Optimization.LineSearches
    • Extreme.Mathematics.Random
    • Extreme.Mathematics.SignalProcessing
    • Extreme.Providers
    • Extreme.Providers.InteropServices
    • Extreme.Statistics
    • Extreme.Statistics.Distributions
    • Extreme.Statistics.Multivariate
    • Extreme.Statistics.Tests
    • Extreme.Statistics.TimeSeriesAnalysis
  • Extreme.Statistics.Distributions
    • ArcsineDistribution Class
    • BernoulliDistribution Class
    • BetaDistribution Class
    • BinomialDistribution Class
    • CauchyDistribution Class
    • ChiSquareDistribution Class
    • ContinuousDistribution Class
    • ContinuousUniformDistribution Class
    • DirichletDistribution Class
    • DiscreteDistribution Class
    • DiscreteDistribution(T) Class
    • DiscreteUniformDistribution Class
    • Distribution Class
    • ErlangDistribution Class
    • EstimationMethod Enumeration
    • ExponentialDistribution Class
    • FDistribution Class
    • GammaDistribution Class
    • GaussianMixtureDistribution Class
    • GeneralizedParetoDistribution Class
    • GenericDiscreteDistribution Class
    • GeometricDistribution Class
    • GumbelDistribution Class
    • HyperbolicDistribution Class
    • HypergeometricDistribution Class
    • InverseChiSquareDistribution Class
    • InverseGammaDistribution Class
    • InverseGaussianDistribution Class
    • InverseWeibullDistribution Class
    • JohnsonDistribution Class
    • JohnsonDistributionType Enumeration
    • LaplaceDistribution Class
    • LogarithmicSeriesDistribution Class
    • LogisticDistribution Class
    • LogLogisticDistribution Class
    • LognormalDistribution Class
    • MaxwellDistribution Class
    • MultivariateContinuousDistribution Class
    • MultivariateNormalDistribution Class
    • NegativeBinomialDistribution Class
    • NonCentralBetaDistribution Class
    • NonCentralChiSquareDistribution Class
    • NonCentralFDistribution Class
    • NonCentralStudentTDistribution Class
    • NormalDistribution Class
    • NormalInverseGaussianDistribution Class
    • ParetoDistribution Class
    • ParetoDistributionVariant Enumeration
    • PertDistribution Class
    • PiecewiseDistribution Class
    • PoissonDistribution Class
    • RandomExtensions Class
    • RayleighDistribution Class
    • StudentTDistribution Class
    • TransformedBetaDistribution Class
    • TransformedGammaDistribution Class
    • TriangularDistribution Class
    • TruncatedDistribution Class
    • WeibullDistribution Class
    • WishartDistribution Class
  • BernoulliDistribution Class
    • BernoulliDistribution Constructor
    • Properties
    • Methods

BernoulliDistribution Class

Extreme Optimization Numerical Libraries for .NET Professional
Represents the Bernoulli distribution.
Inheritance Hierarchy

SystemObject
  Extreme.Statistics.DistributionsDistribution
    Extreme.Statistics.DistributionsDiscreteDistribution
      Extreme.Statistics.DistributionsBernoulliDistribution

Namespace:  Extreme.Statistics.Distributions
Assembly:  Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.1
Syntax

C#
VB
C++
F#
Copy
[SerializableAttribute]
public class BernoulliDistribution : DiscreteDistribution
<SerializableAttribute>
Public Class BernoulliDistribution
	Inherits DiscreteDistribution
[SerializableAttribute]
public ref class BernoulliDistribution : public DiscreteDistribution
[<SerializableAttribute>]
type BernoulliDistribution =  
    class
        inherit DiscreteDistribution
    end

The BernoulliDistribution type exposes the following members.

Constructors

  NameDescription
Public methodBernoulliDistribution
Constructs a new BernoulliDistribution with the specified probability of success.
Top
Properties

  NameDescription
Public propertyEntropy
Gets the entropy of the distribution.
(Overrides DistributionEntropy.)
Public propertyIsUnimodal
Gets whether the distribution has one or more modes.
(Inherited from DiscreteDistribution.)
Public propertyKurtosis
Gets the kurtosis of the distribution.
(Overrides DistributionKurtosis.)
Public propertyMean
Gets the mean or expectation value of the distribution.
(Overrides DistributionMean.)
Public propertyMode
Gets the mode of the distribution.
(Overrides DiscreteDistributionMode.)
Public propertyNumberOfModes
Gets the number of modes of the distribution.
(Overrides DiscreteDistributionNumberOfModes.)
Public propertyProbabilityOfSuccess
Gets the probability that a trial is successful.
Public propertySkewness
Gets the skewness of the distribution.
(Overrides DistributionSkewness.)
Public propertyStandardDeviation
Gets the standard deviation of the distribution.
(Inherited from Distribution.)
Public propertyStatisticSymbol
Gets the common symbol to describe a statistic from the distribution.
(Inherited from Distribution.)
Public propertyVariance
Gets the variance of the distribution.
(Overrides DistributionVariance.)
Top
Methods

  NameDescription
Public methodDistributionFunction
Evaluates the cumulative distribution function of the distribution.
(Overrides DiscreteDistributionDistributionFunction(Int32).)
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetAllModes
Returns an array that contains all the modes of the distribution.
(Overrides DiscreteDistributionGetAllModes.)
Public methodGetExpectedHistogram(IndexIntervalInt32, Double)
Returns a histogram whose bins contain the expected number of samples from the distribution for a given total number of samples.
(Inherited from DiscreteDistribution.)
Public methodGetExpectedHistogram(IndexInt32, Double)
Returns a histogram whose bins contain the expected number of samples from the distribution for a given total number of samples.
(Inherited from DiscreteDistribution.)
Public methodGetExpectedHistogram(Int32, Int32, Double)
Returns a histogram whose bins contain the expected number of samples from the distribution for a given total number of samples.
(Inherited from DiscreteDistribution.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetRandomSequence
Returns a sequence of random samples from the distribution.
(Inherited from DiscreteDistribution.)
Public methodGetRandomSequence(Random)
Returns a sequence of random samples from the distribution.
(Inherited from DiscreteDistribution.)
Public methodGetRandomSequence(Random, Int32)
Returns a sequence of random samples of the specified length from the distribution.
(Inherited from DiscreteDistribution.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodInverseDistributionFunction
Returns the inverse of the distribution function.
(Overrides DiscreteDistributionInverseDistributionFunction(Double).)
Public methodLeftTailProbability
Gets the probability of obtaining a sample that is less than or less than or equal to the specified upper bound.
(Inherited from DiscreteDistribution.)
Public methodLogProbability
Returns the logarithm of the probability of obtaining a specific integer value in the distribution.
(Inherited from DiscreteDistribution.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodProbability(Int32)
Evaluates the probability function of the distribution.
(Overrides DiscreteDistributionProbability(Int32).)
Public methodProbability(Int32, Int32)
Gets the probability of obtaining a sample that falls within the specified interval from the distribution.
(Inherited from DiscreteDistribution.)
Public methodRightTailProbability
Gets the probability of obtaining a sample that is less than or less than or equal to the specified upper bound.
(Inherited from DiscreteDistribution.)
Public methodSample
Returns a random sample from the distribution.
(Inherited from DiscreteDistribution.)
Public methodSample(Int32)
Returns a vector of random samples from the distribution.
(Inherited from DiscreteDistribution.)
Public methodSample(Random)
Returns a random sample from the distribution.
(Overrides DiscreteDistributionSample(Random).)
Public methodSample(Int32, Random)
Returns a vector of random samples from the distribution.
(Inherited from DiscreteDistribution.)
Public methodStatic memberSample(Random, Double)
Returns a single random sample from a Bernoulli distribution with the specified success rate.
Public methodSample(Random, Int32)
Fills an Int32 array with random numbers.
(Inherited from DiscreteDistribution.)
Public methodSample(Random, Int32, Int32, Int32)
Fills an Int32 array with random numbers from this DiscreteDistribution.
(Inherited from DiscreteDistribution.)
Public methodToString
Returns a string that represents the current object.
(Overrides ObjectToString.)
Public methodTwoTailProbability
Gets the probability of obtaining a sample that is less than or less than or equal to the specified upper bound.
(Inherited from DiscreteDistribution.)
Top
Remarks

The Bernoulli distribution is a discrete probability distribution that has two possible outcomes: 0 (failure) and 1 (success). The distribution has one parameter: the probability probability of success.

The Bernoulli distribution is the simplest discrete probability distribution. It forms the basis for several other distributions, as shown in the following table.

DistributionDefinition
BinomialDistributionThe number of successes in n trials.
GeometricDistributionThe number of failures before the first success.
NegativeBinomialDistributionThe number of failures before the nth success.
Examples

The distribution of heads and tails in a coin toss is a Bernoulli distribution with probability = 0.5. Which of heads or tails corresponds to a successful outcome is arbitrary in this case.
See Also

Reference

Extreme.Statistics.Distributions Namespace
Extreme.Statistics.DistributionsBinomialDistribution
Extreme.Statistics.DistributionsGeometricDistribution
Extreme.Statistics.DistributionsNegativeBinomialDistribution

Copyright (c) 2004-2021 ExoAnalytics Inc.

Send comments on this topic to support@extremeoptimization.com

Copyright © 2004-2021, Extreme Optimization. All rights reserved.
Extreme Optimization, Complexity made simple, M#, and M Sharp are trademarks of ExoAnalytics Inc.
Microsoft, Visual C#, Visual Basic, Visual Studio, Visual Studio.NET, and the Optimized for Visual Studio logo
are registered trademarks of Microsoft Corporation.