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19.2 Beta proportion distribution

19.2.1 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, $\kappa > 0$.

19.2.2 Sampling statement

theta ~ beta_proportion(mu, kappa)

Increment target log probability density with beta_proportion_lupdf(theta | mu, kappa).

19.2.3 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

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

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

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

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.