bernoulli_logit_glm_lpmf() [2/3]

template<bool propto, typename T_y , typename T_x , typename T_alpha , typename T_beta , require_matrix_t< T_x > * = nullptr>

return_type_t< T_x, T_alpha, T_beta > stan::math::bernoulli_logit_glm_lpmf ( const T_y &  y, const T_x &  x, const T_alpha &  alpha, const T_beta &  beta  )

inline

Returns the log PMF of the Generalized Linear Model (GLM) with Bernoulli distribution and logit link function.

The idea is that bernoulli_logit_glm_lpmf(y, x, alpha, beta) should compute a more efficient version of bernoulli_logit_lpmf(y, alpha + x * beta) by using analytically simplified gradients. If containers are supplied, returns the log sum of the probabilities.

Template Parameters

T_y	type of binary vector of dependent variables (labels); this can also be a single binary value;
T_x	type of the matrix of independent variables (features)
T_alpha	type of the intercept(s); this can be a vector (of the same length as y) of intercepts or a single value (for models with constant intercept);
T_beta	type of the weight vector

Parameters

y	binary scalar or vector parameter. If it is a scalar it will be broadcast - used for all instances.
x	design matrix or row vector. If it is a row vector it will be broadcast - used for all instances.
alpha	intercept (in log odds)
beta	weight vector

Returns

log probability or log sum of probabilities

Exceptions

std::domain_error	if x, beta or alpha is infinite.
std::domain_error	if y is not binary.
std::invalid_argument	if container sizes mismatch.

Definition at line 51 of file bernoulli_logit_glm_lpmf.hpp.