20.2 Beta proportion distribution
20.2.1 Probability density function
If \(\mu \in (0, 1)\) and \(\kappa \in \mathbb{R}^+\), then for \(\theta \in (0,1)\), \[ \mathrm{Beta\_Proportion}(\theta|\mu,\kappa) = \frac{1}{\mathrm{B}(\mu \kappa, (1 - \mu) \kappa)} \, \theta^{\mu\kappa - 1} \, (1 - \theta)^{(1 - \mu)\kappa- 1} , \] where the beta function \(\mathrm{B}()\) is as defined in section combinatorial functions.
Warning: If \(\theta = 0\) or \(\theta = 1\), then the probability is 0 and the log probability is \(-\infty\). Similarly, the distribution requires \(\mu \in (0, 1)\) and strictly positive parameter, \(\kappa > 0\).
20.2.2 Sampling statement
theta ~ beta_proportion(mu, kappa)
Increment target log probability density with beta_proportion_lupdf(theta | mu, kappa).
Available since 2.19
20.2.3 Stan functions
real beta_proportion_lpdf(reals theta | reals mu, reals kappa)
The log of the beta_proportion density of theta in \((0,1)\) given
mean mu and precision kappa
Available since 2.19
real beta_proportion_lupdf(reals theta | reals mu, reals kappa)
The log of the beta_proportion density of theta in \((0,1)\) given
mean mu and precision kappa dropping constant additive terms
Available since 2.25
real beta_proportion_lcdf(reals theta | reals mu, reals kappa)
The log of the beta_proportion cumulative distribution function of
theta in \((0,1)\) given mean mu and precision kappa
Available since 2.18
real beta_proportion_lccdf(reals theta | reals mu, reals kappa)
The log of the beta_proportion complementary cumulative distribution
function of theta in \((0,1)\) given mean mu and precision kappa
Available since 2.18
R beta_proportion_rng(reals mu, reals kappa)
Generate a beta_proportion variate with mean mu and precision kappa;
may only be used in transformed data and generated quantities blocks.
For a description of argument and return types, see section
vectorized PRNG functions.
Available since 2.18