Distributions

Distribution functions for greybox.

This module contains implementations of various distributions not available in scipy.stats, plus wrappers around scipy.stats.

greybox.distributions.dalaplace(q, mu=0, scale=1, alpha=0.5, log=False)

Asymmetric Laplace distribution density.

f(x) = alpha * (1-alpha) / scale * exp(-(x-mu)/scale * (alpha - I(x<=mu)))

Parameters:

• q (array_like) – Quantiles.

• mu (float) – Location parameter.

• scale (float) – Scale parameter.

• alpha (float) – Asymmetry parameter (0 < alpha < 1).

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dbcnorm(q, mu=0, sigma=1, lambda_bc=0, log=False)

Box-Cox Normal distribution density.

f(y) = y^(lambda-1) * 1/sqrt(2*pi) * exp(

-((y^lambda-1)/lambda - mu)^2 / (2*sigma^2))

Parameters:

• q (array_like) – Quantiles (must be non-negative).

• mu (float) – Location parameter (on transformed scale).

• sigma (float) – Scale parameter.

• lambda_bc (float) – Box-Cox transformation parameter.

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dbeta(q, a=1, b=1, log=False)

Beta distribution density.

Parameters:

• q (array_like) – Quantiles (must be in [0, 1]).

• a (float) – First shape parameter (alpha).

• b (float) – Second shape parameter (beta).

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dbinom(q, size=1, prob=0.5, log=False)

Binomial distribution probability mass function.

Parameters:

• q (array_like) – Quantiles (non-negative integers, <= size).

• size (int) – Number of trials.

• prob (float) – Probability of success.

• log (bool) – If True, return log-probability.

Returns:

Probability mass values.

Return type:

array

greybox.distributions.dchi2(q, df, log=False)

Chi-squared distribution density.

Parameters:

• q (array_like) – Quantiles (must be non-negative).

• df (float) – Degrees of freedom.

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dexp(q, loc=0, scale=1, log=False)

Exponential distribution density.

Parameters:

• q (array_like) – Quantiles (must be >= loc).

• loc (float) – Location parameter.

• scale (float) – Scale parameter (1/lambda).

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dfnorm(q, mu=0, sigma=1, log=False)

Folded Normal distribution density.

f(x) = 1/sqrt(2*pi*sigma^2) * (

exp(-(x-mu)^2 / (2*sigma^2)) + exp(-(x+mu)^2 / (2*sigma^2)))

Parameters:

• q (array_like) – Quantiles.

• mu (float) – Location parameter.

• sigma (float) – Scale parameter.

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dgamma(q, shape=1, scale=1, log=False)

Gamma distribution density.

Parameters:

• q (array_like) – Quantiles (must be positive).

• shape (float) – Shape parameter (alpha).

• scale (float) – Scale parameter (theta).

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dgeom(q, prob=0.5, log=False)

Geometric distribution probability mass function.

Parameters:

• q (array_like) – Quantiles (non-negative integers, number of failures before first success).

• prob (float) – Probability of success.

• log (bool) – If True, return log-probability.

Returns:

Probability mass values.

Return type:

array

greybox.distributions.dgnorm(q, mu=0, scale=1, shape=1, log=False)

Generalized Normal distribution density.

greybox.distributions.dinvgauss(q, mu=1, scale=1, log=False)

Inverse Gaussian distribution density.

Parameters:

• q (array_like) – Quantiles (must be positive).

• mu (float) – Mean parameter.

• scale (float) – Scale parameter.

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dlaplace(q, loc=0, scale=1, log=False)

Laplace distribution density.

Parameters:

• q (array_like) – Quantiles.

• loc (float) – Location parameter (mu).

• scale (float) – Scale parameter.

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dlgnorm(q, mu=0, scale=1, shape=1, log=False)

Log-Generalised Normal distribution density.

The density is obtained by transforming a Generalised Normal distribution through the exponential function with Jacobian adjustment.

Parameters:

• q (array_like) – Quantiles (must be positive).

• mu (float) – Location parameter.

• scale (float) – Scale parameter.

• shape (float) – Shape parameter.

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dllaplace(q, loc=0, scale=1, log=False)

Log-Laplace distribution density.

The density is obtained by transforming a Laplace distribution through the exponential function with Jacobian adjustment.

f(y) = (1/scale) * exp(-(abs(log(y) - loc) / scale)) / y

Parameters:

• q (array_like) – Quantiles (must be positive).

• loc (float) – Location parameter (of underlying Laplace).

• scale (float) – Scale parameter.

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dlnorm(q, meanlog=0, sdlog=1, log=False)

Log-Normal distribution density.

Parameters:

• q (array_like) – Quantiles (must be positive).

• meanlog (float) – Mean of the underlying normal distribution (on log scale).

• sdlog (float) – Standard deviation of the underlying normal distribution.

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dlogis(q, loc=0, scale=1, log=False)

Logistic distribution density.

Parameters:

• q (array_like) – Quantiles.

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dlogitnorm(q, mu=0, sigma=1, log=False)

Logit-Normal distribution density.

f(y) = 1/(sqrt(2*pi)*sigma*y*(1-y)) * exp(-(logit(y) - mu)^2 / (2*sigma^2))

Parameters:

• q (array_like) – Quantiles (must be in (0, 1)).

• mu (float) – Location parameter (on logit scale).

• sigma (float) – Scale parameter.

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dls(q, loc=0, scale=1, log=False)

Log-S distribution density.

The density is obtained by transforming an S-distribution through the exponential function with Jacobian adjustment.

Parameters:

• q (array_like) – Quantiles (must be positive).

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dnbinom(q, mu=1, size=1, log=False)

Negative Binomial distribution probability mass function.

Parameters:

• q (array_like) – Quantiles (non-negative integers).

• mu (float) – Mean parameter.

• size (float) – Dispersion parameter (number of successes).

• log (bool) – If True, return log-probability.

Returns:

Probability mass values.

Return type:

array

greybox.distributions.dnorm(q, mean=0.0, sd=1.0, log=False)

Normal distribution density.

Parameters:

• q (array_like) – Quantiles.

• mean (float) – Mean.

• sd (float) – Standard deviation.

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dpois(q, mu, log=False)

Poisson distribution probability mass function.

Parameters:

• q (array_like) – Quantiles (non-negative integers).

• mu (float) – Mean parameter (lambda).

• log (bool) – If True, return log-probability.

Returns:

Probability mass values.

Return type:

array

greybox.distributions.drectnorm(q, mu=0, sigma=1, log=False)

Rectified Normal distribution density.

f_y = I(x<=0) * F_x(mu, sigma) + I(x>0) * f_x(x, mu, sigma)

Parameters:

• q (array_like) – Quantiles.

• mu (float) – Location parameter.

• sigma (float) – Scale parameter.

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.ds(q, mu=0, scale=1, log=False)

S-distribution density.

Density function: f(x) = 1/(4*scale^2) * exp(-sqrt(abs(mu - x)) / scale)

Parameters:

• q (array_like) – Quantiles.

• mu (float) – Location parameter.

• scale (float) – Scale parameter.

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.dt(q, df, loc=0, scale=1, log=False)

T-distribution density.

Parameters:

• q (array_like) – Quantiles.

• df (float) – Degrees of freedom.

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

• log (bool) – If True, return log-density.

Returns:

Density values.

Return type:

array

greybox.distributions.palaplace(q, mu=0, scale=1, alpha=0.5)

Asymmetric Laplace distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• mu (float) – Location parameter.

• scale (float) – Scale parameter.

• alpha (float) – Asymmetry parameter (0 < alpha < 1).

Returns:

CDF values.

Return type:

array

greybox.distributions.pbcnorm(q, mu=0, sigma=1, lambda_bc=0)

Box-Cox Normal distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• mu (float) – Location parameter.

• sigma (float) – Scale parameter.

• lambda_bc (float) – Box-Cox transformation parameter.

Returns:

CDF values.

Return type:

array

greybox.distributions.pbeta(q, a=1, b=1)

Beta distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• a (float) – First shape parameter.

• b (float) – Second shape parameter.

Returns:

CDF values.

Return type:

array

greybox.distributions.pbinom(q, size=1, prob=0.5)

Binomial distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• size (int) – Number of trials.

• prob (float) – Probability of success.

Returns:

CDF values.

Return type:

array

greybox.distributions.pchi2(q, df)

Chi-squared distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• df (float) – Degrees of freedom.

Returns:

CDF values.

Return type:

array

greybox.distributions.pexp(q, loc=0, scale=1)

Exponential distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

Returns:

CDF values.

Return type:

array

greybox.distributions.pfnorm(q, mu=0, sigma=1)

Folded Normal distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• mu (float) – Location parameter.

• sigma (float) – Scale parameter.

Returns:

CDF values.

Return type:

array

greybox.distributions.pgamma(q, shape=1, scale=1)

Gamma distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• shape (float) – Shape parameter.

• scale (float) – Scale parameter.

Returns:

CDF values.

Return type:

array

greybox.distributions.pgeom(q, prob=0.5)

Geometric distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• prob (float) – Probability of success.

Returns:

CDF values.

Return type:

array

greybox.distributions.pgnorm(q, mu=0, scale=1, shape=1, lower_tail=True, log_p=False)

Generalized Normal distribution CDF.

greybox.distributions.pinvgauss(q, mu=1, scale=1)

Inverse Gaussian distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• mu (float) – Mean parameter.

• scale (float) – Scale parameter.

Returns:

CDF values.

Return type:

array

greybox.distributions.plaplace(q, loc=0, scale=1)

Laplace distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

Returns:

CDF values.

Return type:

array

greybox.distributions.plnorm(q, meanlog=0, sdlog=1)

Log-Normal distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• meanlog (float) – Mean of the underlying normal distribution.

• sdlog (float) – Standard deviation of the underlying normal distribution.

Returns:

CDF values.

Return type:

array

greybox.distributions.plogis(y, location=0.0, scale=1.0, log_p=False, lower_tail=True)

Logistic distribution CDF.

Parameters:

• y (array_like) – Quantiles.

• location (float) – Location parameter.

• scale (float) – Scale parameter.

• log_p (bool) – If True, return log-CDF.

• lower_tail (bool) – If True, return lower tail probability.

Returns:

CDF values.

Return type:

array

greybox.distributions.plogitnorm(q, mu=0, sigma=1)

Logit-Normal distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• mu (float) – Location parameter.

• sigma (float) – Scale parameter.

Returns:

CDF values.

Return type:

array

greybox.distributions.pnbinom(q, mu=1, size=1)

Negative Binomial distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• mu (float) – Mean parameter.

• size (float) – Dispersion parameter.

Returns:

CDF values.

Return type:

array

greybox.distributions.pnorm(y, mean=0.0, sd=1.0, log_p=False, lower_tail=True)

Normal distribution CDF.

Parameters:

• y (array_like) – Quantiles.

• mean (float) – Mean.

• sd (float) – Standard deviation.

• log_p (bool) – If True, return log-CDF.

• lower_tail (bool) – If True, return lower tail probability.

Returns:

CDF values.

Return type:

array

greybox.distributions.ppois(q, mu)

Poisson distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• mu (float) – Mean parameter.

Returns:

CDF values.

Return type:

array

greybox.distributions.prectnorm(q, mu=0, sigma=1)

Rectified Normal distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• mu (float) – Location parameter.

• sigma (float) – Scale parameter.

Returns:

CDF values.

Return type:

array

greybox.distributions.ps(q, mu=0, scale=1)

S-distribution CDF.

greybox.distributions.pt(q, df, loc=0, scale=1)

T-distribution CDF.

Parameters:

• q (array_like) – Quantiles.

• df (float) – Degrees of freedom.

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

Returns:

CDF values.

Return type:

array

greybox.distributions.qalaplace(p, mu=0, scale=1, alpha=0.5)

Asymmetric Laplace distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• mu (float) – Location parameter.

• scale (float) – Scale parameter.

• alpha (float) – Asymmetry parameter (0 < alpha < 1).

Returns:

Quantile values.

Return type:

array

greybox.distributions.qbcnorm(p, mu=0, sigma=1, lambda_bc=0)

Box-Cox Normal distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• mu (float) – Location parameter.

• sigma (float) – Scale parameter.

• lambda_bc (float) – Box-Cox transformation parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qbeta(p, a=1, b=1)

Beta distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• a (float) – First shape parameter.

• b (float) – Second shape parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qbinom(p, size=1, prob=0.5)

Binomial distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• size (int) – Number of trials.

• prob (float) – Probability of success.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qchi2(p, df)

Chi-squared distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• df (float) – Degrees of freedom.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qexp(p, loc=0, scale=1)

Exponential distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qfnorm(p, mu=0, sigma=1)

Folded Normal distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• mu (float) – Location parameter.

• sigma (float) – Scale parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qgamma(p, shape=1, scale=1)

Gamma distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• shape (float) – Shape parameter.

• scale (float) – Scale parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qgeom(p, prob=0.5)

Geometric distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• prob (float) – Probability of success.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qgnorm(p, mu=0, scale=1, shape=1, lower_tail=True, log_p=False)

Generalized Normal distribution quantile function.

greybox.distributions.qinvgauss(p, mu=1, scale=1)

Inverse Gaussian distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• mu (float) – Mean parameter.

• scale (float) – Scale parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qlaplace(p, loc=0, scale=1)

Laplace distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qlgnorm(p, mu=0, scale=1, shape=1)

Log-Generalised Normal distribution quantile function.

Quantiles are obtained by exponentiating Generalised Normal quantiles.

Parameters:

• p (array_like) – Probabilities.

• mu (float) – Location parameter.

• scale (float) – Scale parameter.

• shape (float) – Shape parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qllaplace(p, loc=0, scale=1)

Log-Laplace distribution quantile function.

Quantiles are obtained by exponentiating Laplace quantiles.

Parameters:

• p (array_like) – Probabilities.

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qlnorm(p, meanlog=0, sdlog=1)

Log-Normal distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• meanlog (float) – Mean of the underlying normal distribution.

• sdlog (float) – Standard deviation of the underlying normal distribution.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qlogis(p, loc=0, scale=1)

Logistic distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qlogitnorm(p, mu=0, sigma=1)

Logit-Normal distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• mu (float) – Location parameter.

• sigma (float) – Scale parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qls(p, loc=0, scale=1)

Log-S distribution quantile function.

Quantiles are obtained by exponentiating S-distribution quantiles.

Parameters:

• p (array_like) – Probabilities.

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qnbinom(p, mu=1, size=1)

Negative Binomial distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• mu (float) – Mean parameter.

• size (float) – Dispersion parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qnorm(p, mean=0.0, sd=1.0)

Normal distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• mean (float) – Mean.

• sd (float) – Standard deviation.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qpois(p, mu)

Poisson distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• mu (float) – Mean parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qrectnorm(p, mu=0, sigma=1)

Rectified Normal distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• mu (float) – Location parameter.

• sigma (float) – Scale parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.qs(p, mu=0, scale=1)

S-distribution quantile function.

greybox.distributions.qt(p, df, loc=0, scale=1)

T-distribution quantile function.

Parameters:

• p (array_like) – Probabilities.

• df (float) – Degrees of freedom.

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

Returns:

Quantile values.

Return type:

array

greybox.distributions.ralaplace(n, mu=0, scale=1, alpha=0.5)

Asymmetric Laplace distribution random number generation.

Parameters:

• n (int) – Number of observations.

• mu (float) – Location parameter.

• scale (float) – Scale parameter.

• alpha (float) – Asymmetry parameter (0 < alpha < 1).

Returns:

Random values.

Return type:

array

greybox.distributions.rbcnorm(n, mu=0, sigma=1, lambda_bc=0)

Box-Cox Normal distribution random number generation.

Parameters:

• n (int) – Number of observations.

• mu (float) – Location parameter.

• sigma (float) – Scale parameter.

• lambda_bc (float) – Box-Cox transformation parameter.

Returns:

Random values.

Return type:

array

greybox.distributions.rbeta(n, a=1, b=1)

Beta distribution random number generation.

Parameters:

• n (int) – Number of observations.

• a (float) – First shape parameter.

• b (float) – Second shape parameter.

Returns:

Random values.

Return type:

array

greybox.distributions.rbinom(n, size=1, prob=0.5)

Binomial distribution random number generation.

Parameters:

• n (int) – Number of observations.

• size (int) – Number of trials.

• prob (float) – Probability of success.

Returns:

Random values.

Return type:

array

greybox.distributions.rchi2(n, df)

Chi-squared distribution random number generation.

Parameters:

• n (int) – Number of observations.

• df (float) – Degrees of freedom.

Returns:

Random values.

Return type:

array

greybox.distributions.rexp(n, loc=0, scale=1)

Exponential distribution random number generation.

Parameters:

• n (int) – Number of observations.

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

Returns:

Random values.

Return type:

array

greybox.distributions.rfnorm(n, mu=0, sigma=1)

Folded Normal distribution random number generation.

Parameters:

• n (int) – Number of observations.

• mu (float) – Location parameter.

• sigma (float) – Scale parameter.

Returns:

Random values.

Return type:

array

greybox.distributions.rgamma(n, shape=1, scale=1)

Gamma distribution random number generation.

Parameters:

• n (int) – Number of observations.

• shape (float) – Shape parameter.

• scale (float) – Scale parameter.

Returns:

Random values.

Return type:

array

greybox.distributions.rgeom(n, prob=0.5)

Geometric distribution random number generation.

Parameters:

• n (int) – Number of observations.

• prob (float) – Probability of success.

Returns:

Random values.

Return type:

array

greybox.distributions.rgnorm(n, mu=0, scale=1, shape=1)

Generalized Normal distribution random number generation.

greybox.distributions.rinvgauss(n, mu=1, scale=1)

Inverse Gaussian distribution random number generation.

Parameters:

• n (int) – Number of observations.

• mu (float) – Mean parameter.

• scale (float) – Scale parameter.

Returns:

Random values.

Return type:

array

greybox.distributions.rlaplace(n, loc=0, scale=1)

Laplace distribution random number generation.

Parameters:

• n (int) – Number of observations.

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

Returns:

Random values.

Return type:

array

greybox.distributions.rlogis(n, loc=0, scale=1)

Logistic distribution random number generation.

Parameters:

• n (int) – Number of observations.

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

Returns:

Random values.

Return type:

array

greybox.distributions.rlogitnorm(n, mu=0, sigma=1)

Logit-Normal distribution random number generation.

Parameters:

• n (int) – Number of observations.

• mu (float) – Location parameter.

• sigma (float) – Scale parameter.

Returns:

Random values.

Return type:

array

greybox.distributions.rnbinom(n, mu=1, size=1)

Negative Binomial distribution random number generation.

Parameters:

• n (int) – Number of observations.

• mu (float) – Mean parameter.

• size (float) – Dispersion parameter.

Returns:

Random values.

Return type:

array

greybox.distributions.rpois(n, mu)

Poisson distribution random number generation.

Parameters:

• n (int) – Number of observations.

• mu (float) – Mean parameter.

Returns:

Random values.

Return type:

array

greybox.distributions.rrectnorm(n, mu=0, sigma=1)

Rectified Normal distribution random number generation.

Parameters:

• n (int) – Number of observations.

• mu (float) – Location parameter.

• sigma (float) – Scale parameter.

Returns:

Random values.

Return type:

array

greybox.distributions.rs(n, mu=0, scale=1)

S-distribution random number generation.

greybox.distributions.rt(n, df, loc=0, scale=1)

T-distribution random number generation.

Parameters:

• n (int) – Number of observations.

• df (float) – Degrees of freedom.

• loc (float) – Location parameter.

• scale (float) – Scale parameter.

Returns:

Random values.

Return type:

array