tfd_generalized_pareto: The Generalized Pareto distribution.

In tfprobability: Interface to 'TensorFlow Probability'

The Generalized Pareto distribution.

Description

The Generalized Pareto distributions are a family of continuous distributions on the reals. Special cases include Exponential (when loc = 0, concentration = 0), Pareto (when concentration > 0, loc = scale / concentration), and Uniform (when concentration = -1).

Usage

tfd_generalized_pareto(
  loc,
  scale,
  concentration,
  validate_args = FALSE,
  allow_nan_stats = TRUE,
  name = NULL
)

Arguments

loc	The location / shift of the distribution. GeneralizedPareto is a location-scale distribution. This parameter lower bounds the distribution's support. Must broadcast with scale, concentration. Floating point Tensor.
scale	The scale of the distribution. GeneralizedPareto is a location-scale distribution, so doubling the scale doubles a sample and halves the density. Strictly positive floating point Tensor. Must broadcast with loc, concentration.
concentration	The shape parameter of the distribution. The larger the magnitude, the more the distribution concentrates near loc (for concentration >= 0) or near loc - (scale/concentration) (for concentration < 0). Floating point Tensor.
validate_args	Logical, default FALSE. When TRUE distribution parameters are checked for validity despite possibly degrading runtime performance. When FALSE invalid inputs may silently render incorrect outputs. Default value: FALSE.
allow_nan_stats	Logical, default TRUE. When TRUE, statistics (e.g., mean, mode, variance) use the value NaN to indicate the result is undefined. When FALSE, an exception is raised if one or more of the statistic's batch members are undefined.
name	name prefixed to Ops created by this class.

Details

This distribution is often used to model the tails of other distributions. As a member of the location-scale family, X ~ GeneralizedPareto(loc=loc, scale=scale, concentration=conc) maps to Y ~ GeneralizedPareto(loc=0, scale=1, concentration=conc) via Y = (X - loc) / scale.

For positive concentrations, the distribution is equivalent to a hierarchical Exponential-Gamma model with X|rate ~ Exponential(rate) and rate ~ Gamma(concentration=1 / concentration, scale=scale / concentration). In the following, samps1 and samps2 are identically distributed:

genp <- tfd_generalized_pareto(loc = 0, scale = scale, concentration = conc)
samps1 <- genp %>% tfd_sample(1000)
jd <- tfd_joint_distribution_named(
  list(
    rate =  tfd_gamma(1 / genp$concentration, genp$scale / genp$concentration),
    x = function(rate) tfd_exponential(rate)))
samps2 <- jd %>% tfd_sample(1000) %>% .$x

The support of the distribution is always lower bounded by loc. When concentration < 0, the support is also upper bounded by loc + scale / abs(concentration).

Mathematical Details

The probability density function (pdf) is,

pdf(x; mu, sigma, shp, x > mu) =   (1 + shp * (x - mu) / sigma)**(-1 / shp - 1) / sigma

where:

• concentration = shp, any real value,

• scale = sigma, sigma > 0,

• loc = mu.

The cumulative density function (cdf) is,

cdf(x; mu, sigma, shp, x > mu) = 1 - (1 + shp * (x - mu) / sigma)**(-1 / shp)

Distribution parameters are automatically broadcast in all functions; see examples for details. Samples of this distribution are reparameterized (pathwise differentiable).

Value

a distribution instance.

See Also

For usage examples see e.g. tfd_sample(), tfd_log_prob(), tfd_mean().

tfprobability documentation built on Sept. 1, 2022, 5:07 p.m.