Generate a random variate from a beta distribution $\mathrm{Beta}$ . More...

#include <Beta.h>

Inheritance diagram for honey::Beta_< Real >:

Collaboration diagram for honey::Beta_< Real >:

Public Member Functions
	Beta_ (optional< RandomGen & > gen, Real a, Real b)

	Beta_ (Real a, Real b)

virtual Real	next () const
	Get next randomly distributed variate. Requires a random generator (see ctor or setGen()) More...

virtual Real	pdf (Real x) const
	Probability Density Function. More...

virtual Real	cdf (Real x) const
	Cumulative Distribution Function. More...

virtual Real	cdfInv (Real P) const
	Inverse of the CDF. More...

virtual Real	mean () const
	Calc mean. More...

virtual Real	variance () const
	Calc variance. More...

Double	func () const
	Evaluate the beta function. More...

Public Member Functions inherited from honey::RandomDist< Real >
	RandomDist (optional< RandomGen & > gen=optnull)
	Construct with a random generator to use for next() More...

virtual	~RandomDist ()

virtual Real	cdfComp (Real x) const
	Complement of the CDF. More...

Real	stdDev () const
	Calc standard deviation. More...

void	setGen (RandomGen &gen)
	Set random generator to use for next() More...

RandomGen &	getGen () const
	Get random generator. More...

Public Attributes
Real	a

Real	b

Additional Inherited Members
Protected Types inherited from honey::RandomDist< Real >
typedef Numeral< Real >::Real_	Real_

typedef Real_::DoubleType	Double_

typedef Double_::Real	Double

typedef Alge_< Real >	Alge

typedef Alge_< Double >	Alge_d

typedef Uniform_< Double >	Uniform

Protected Member Functions inherited from honey::RandomDist< Real >
Real	cdfInvFind (Real P, Real min, Real max, bool discrete=false) const
	Generic binary search algorithm to find Cdf. More...

Detailed Description

template<class Real>
class honey::Beta_< Real >

Generate a random variate from a beta distribution $\mathrm{Beta}$ .

The beta distribution can be most easily understood as the "conjugate prior" of the binomial distribution. This means that given x number of passes in n number of independent trials, the beta dist will give us the expected p probability.

Example:

Rule of succession (mentioned above) $p(x) = \mathrm{Beta}(x+1, n-x+1)$
Distribution of time required to complete a task (knowing min/max/expected interval)