Public Member Functions
void	add (unsigned long int k, unsigned long int n=1)

const utility::VectorBase &	alpha (void) const

void	clear (void)
	remove all data

void	fit (size_t n)

void	fit (const utility::VectorBase &m, const utility::VectorBase &alpha, const utility::VectorBase &tau)

double	logL (void) const

double	pdf (unsigned long int k) const

double	pdf (unsigned long int k, size_t i) const

const utility::VectorBase &	m (void) const

const utility::VectorBase &	tau (void) const

Detailed Description

Data is modeled as a mixture of negative binomial distributions and the likelihood is calculated as

$L = \sum_i \tau_i L_i = \sum_i \tau_i {k+r_i-1 \choose r_i-1} (1-p_i)^{r_i} p_i^k$ where $\sum_i \tau_i = 1.0$ .

Member Function Documentation

void theplu::yat::statistics::NegativeBinomialMixture::add	(	unsigned long int	k,
		unsigned long int	n = `1`
	)

Add n data points with value k

const utility::VectorBase& theplu::yat::statistics::NegativeBinomialMixture::alpha ( void ) const

The alpha values are defined as $alpha = \frac{1}{1-p}$

void theplu::yat::statistics::NegativeBinomialMixture::fit ( size_t n )

Fit the data to a model with n sub-distributions.

void theplu::yat::statistics::NegativeBinomialMixture::fit	(	const utility::VectorBase &	m,
		const utility::VectorBase &	alpha,
		const utility::VectorBase &	tau
	)

Fit the data initiating the search for the optimal model parameters at the values passed.

double theplu::yat::statistics::NegativeBinomialMixture::logL ( void ) const

const utility::VectorBase& theplu::yat::statistics::NegativeBinomialMixture::m ( void ) const

The mean is calculated as $m = \frac{pr}{1-p}$

double theplu::yat::statistics::NegativeBinomialMixture::pdf ( unsigned long int k ) const

Probabily to get k from the inferred model.

double theplu::yat::statistics::NegativeBinomialMixture::pdf	(	unsigned long int	k,
		size_t	i
	)		const

Probability to get k from the ith model

const utility::VectorBase& theplu::yat::statistics::NegativeBinomialMixture::tau ( void ) const

Returns: tau values in model, describing how much each sub-distribution contributes to the total distribution.

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