categorical_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, require_col_vector_t< T_alpha > * = nullptr, require_matrix_t< T_beta > * = nullptr>

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

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

Template Parameters

T_y	type of classes. It can be either std::vector<int> or int.
T_x_scalar	type of the matrix of independent variables (features)
T_alpha	type of the intercept vector
T_beta	type of the matrix of weights

Parameters

y	a scalar or vector of classes. If it is a scalar it will be broadcast - used for all instances. Values should be between 1 and number of classes, including endpoints.
x	design matrix or row vector. If it is a row vector it will be broadcast - used for all instances.
alpha	intercept vector (in log odds)
beta	weight matrix

Returns

log probability or log sum of probabilities

Exceptions

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

Definition at line 47 of file categorical_logit_glm_lpmf.hpp.