16.3 Exponentially modified normal distribution
16.3.1 Probability density function
If \(\mu \in \mathbb{R}\), \(\sigma \in \mathbb{R}^+\), and \(\lambda \in \mathbb{R}^+\), then for \(y \in \mathbb{R}\), \[ \text{ExpModNormal}(y|\mu,\sigma,\lambda) = \frac{\lambda}{2} \ \exp \left(\frac{\lambda}{2} \left(2\mu + \lambda \sigma^2 - 2y\right)\right) \text{erfc}\left(\frac{\mu + \lambda\sigma^2 - y}{\sqrt{2}\sigma}\right) . \]
16.3.2 Sampling statement
y ~ exp_mod_normal(mu, sigma, lambda)
Increment target log probability density with exp_mod_normal_lupdf(y | mu, sigma, lambda).
16.3.3 Stan functions
real exp_mod_normal_lpdf(reals y | reals mu, reals sigma, reals lambda)
The log of the exponentially modified normal density of y given
location mu, scale sigma, and shape lambda
real exp_mod_normal_lupdf(reals y | reals mu, reals sigma, reals lambda)
The log of the exponentially modified normal density of y given
location mu, scale sigma, and shape lambda dropping constant additive terms
real exp_mod_normal_cdf(reals y, reals mu, reals sigma, reals lambda)
The exponentially modified normal cumulative distribution function of
y given location mu, scale sigma, and shape lambda
real exp_mod_normal_lcdf(reals y | reals mu, reals sigma, reals lambda)
The log of the exponentially modified normal cumulative distribution
function of y given location mu, scale sigma, and shape lambda
real exp_mod_normal_lccdf(reals y | reals mu, reals sigma, reals lambda)
The log of the exponentially modified normal complementary cumulative
distribution function of y given location mu, scale sigma, and shape
lambda
R exp_mod_normal_rng(reals mu, reals sigma, reals lambda)
Generate a exponentially modified normal variate with location mu,
scale sigma, and shape lambda; may only be used in transformed data and generated
quantities blocks. For a description of argument and return types, see
section vectorized PRNG functions.