bernoulli_logit_glm_lpmf() [1/3]

template<bool propto, typename T_x_cl , typename T_y_cl , typename T_alpha_cl , typename T_beta_cl , require_all_prim_or_rev_kernel_expression_t< T_y_cl, T_x_cl, T_alpha_cl, T_beta_cl > * = nullptr>

return_type_t< T_x_cl, T_alpha_cl, T_beta_cl > stan::math::bernoulli_logit_glm_lpmf	(	const T_y_cl &	y,
		const T_x_cl &	x,
		const T_alpha_cl &	alpha,
		const T_beta_cl &	beta
	)

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

This is an overload of the GLM in prim/prob/bernoulli_logit_glm_lpmf.hpp that is implemented in OpenCL.

Template Parameters

T_y_cl	type of independent variable; this can be a matrix_cl vector of intercepts or a single value (wich will be broadcast - used for all instances);
T_x_cl	type of the design matrix
T_alpha_cl	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_cl	type of the weight vector; this can also be a single value;

Parameters

y	binary scalar or vector parameter on OpenCL device. If it is a scalar it will be broadcast - used for all instances.
x	design matrix on OpenCL device. This overload does not support broadcasting of a row vector x!
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 56 of file bernoulli_logit_glm_lpmf.hpp.