bayesopt::StudentTDistribution Class Reference

Inheritance diagram for bayesopt::StudentTDistribution:

Collaboration diagram for bayesopt::StudentTDistribution:

Public Member Functions
	StudentTDistribution (randEngine &eng)
void	setMeanAndStd (double mean, double std)
	Sets the mean and std of the distribution.
void	setDof (size_t dof)
	Sets the degrees of freedom (dof) the distribution.
double	pdf (double x)
	Probability density function. More...
double	negativeExpectedImprovement (double min, size_t g)
	Expected Improvement algorithm for minimization. More...
double	lowerConfidenceBound (double beta)
	Lower confindence bound. More...
double	negativeProbabilityOfImprovement (double min, double epsilon)
	Probability of improvement algorithm for minimization. More...
double	sample_query ()
	Sample outcome acording to the marginal distribution at the query point. More...
double	getMean ()
double	getStd ()
Public Member Functions inherited from bayesopt::ProbabilityDistribution
	ProbabilityDistribution (randEngine &eng)

Private Attributes
boost::math::students_t	d_
double	mean_
double	std_
size_t	dof_

Additional Inherited Members
Protected Attributes inherited from bayesopt::ProbabilityDistribution
randEngine &	mtRandom

Detailed Description

Definition at line 36 of file student_t_distribution.hpp.

Member Function Documentation

lowerConfidenceBound()

double bayesopt::StudentTDistribution::lowerConfidenceBound ( double  beta )

virtual

Lower confindence bound.

Can be seen as the inverse of the Upper confidence bound

Parameters

beta	std coefficient (used for annealing)

Returns

value of the lower confidence bound

Implements bayesopt::ProbabilityDistribution.

Definition at line 28 of file student_t_distribution.cpp.

Referenced by pdf().

negativeExpectedImprovement()

double bayesopt::StudentTDistribution::negativeExpectedImprovement ( double  min, size_t  g  )

virtual

Expected Improvement algorithm for minimization.

Parameters

min	minimum value found
g	exponent (used for annealing)

Returns

negative value of the expected improvement

Implements bayesopt::ProbabilityDistribution.

Definition at line 16 of file student_t_distribution.cpp.

Referenced by pdf().

negativeProbabilityOfImprovement()

double bayesopt::StudentTDistribution::negativeProbabilityOfImprovement ( double  min, double  epsilon  )

virtual

Probability of improvement algorithm for minimization.

Parameters

min	minimum value found
epsilon	minimum improvement margin

Returns

negative value of the probability of improvement

Implements bayesopt::ProbabilityDistribution.

Definition at line 33 of file student_t_distribution.cpp.

Referenced by pdf().

pdf()

double bayesopt::StudentTDistribution::pdf ( double  x )

inlinevirtual

Probability density function.

Parameters

x	query point

Returns

probability

Implements bayesopt::ProbabilityDistribution.

Definition at line 63 of file student_t_distribution.hpp.

References lowerConfidenceBound(), negativeExpectedImprovement(), negativeProbabilityOfImprovement(), and sample_query().

sample_query()

double bayesopt::StudentTDistribution::sample_query ( )

virtual

Sample outcome acording to the marginal distribution at the query point.

Returns

outcome

Implements bayesopt::ProbabilityDistribution.

Definition at line 40 of file student_t_distribution.cpp.

Referenced by pdf().

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The documentation for this class was generated from the following files:

• student_t_distribution.hpp
• student_t_distribution.cpp