For compiling models. More...

Classes
class	prob_grad
	The `prob_grad` class represents the basic parameter holders for a model. More...

struct	model_functional

Functions
template<bool jacobian_adjust_transform, class M >
double	log_prob_propto (const M &model, std::vector< double > &params_r, std::vector< int > &params_i, std::ostream *msgs=0)
	Helper function to calculate log probability for `double` scalars up to a proportion. More...

template<bool propto, bool jacobian_adjust_transform, class M >
double	log_prob_grad (const M &model, std::vector< double > &params_r, std::vector< int > &params_i, std::vector< double > &gradient, std::ostream *msgs=0)
	Compute the gradient using reverse-mode automatic differentiation, writing the result into the specified gradient, using the specified perturbation. More...

template<bool jacobian_adjust_transform, class M >
double	log_prob_propto (const M &model, Eigen::VectorXd &params_r, std::ostream *msgs=0)
	Helper function to calculate log probability for `double` scalars up to a proportion. More...

template<bool propto, bool jacobian_adjust_transform, class M >
double	log_prob_grad (const M &model, Eigen::VectorXd &params_r, Eigen::VectorXd &gradient, std::ostream *msgs=0)
	Compute the gradient using reverse-mode automatic differentiation, writing the result into the specified gradient, using the specified perturbation. More...

template<bool propto, bool jacobian_adjust_transform, class M >
void	finite_diff_grad (const M &model, std::vector< double > &params_r, std::vector< int > &params_i, std::vector< double > &grad, double epsilon=1e-6, std::ostream *msgs=0)
	Compute the gradient using finite differences for the specified parameters, writing the result into the specified gradient, using the specified perturbation. More...

template<bool propto, bool jacobian_adjust_transform, class M >
int	test_gradients (const M &model, std::vector< double > &params_r, std::vector< int > &params_i, double epsilon=1e-6, double error=1e-6, std::ostream &o=std::cout, std::ostream *msgs=0)
	Test the log_prob_grad() function's ability to produce accurate gradients using finite differences. More...

template<bool propto, bool jacobian_adjust_transform, class M >
double	grad_hess_log_prob (const M &model, std::vector< double > &params_r, std::vector< int > &params_i, std::vector< double > &gradient, std::vector< double > &hessian, std::ostream *msgs=0)
	Evaluate the log-probability, its gradient, and its Hessian at params_r. More...

template<class M >
void	gradient (const M &model, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &x, double &f, Eigen::Matrix< double, Eigen::Dynamic, 1 > &grad_f, std::ostream *msgs=0)

template<class M >
void	hessian (const M &model, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &x, double &f, Eigen::Matrix< double, Eigen::Dynamic, 1 > &grad_f, Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic > &hess_f, std::ostream *msgs=0)

template<class M >
void	gradient_dot_vector (const M &model, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &x, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &v, double &f, double &grad_f_dot_v, std::ostream *msgs=0)

template<class M >
void	hessian_times_vector (const M &model, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &x, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &v, double &f, Eigen::Matrix< double, Eigen::Dynamic, 1 > &hess_f_dot_v, std::ostream *msgs=0)

template<class M >
void	grad_tr_mat_times_hessian (const M &model, const Eigen::Matrix< double, Eigen::Dynamic, 1 > &x, const Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic > &X, Eigen::Matrix< double, Eigen::Dynamic, 1 > &grad_tr_X_hess_f, std::ostream *msgs=0)

Detailed Description

For compiling models.

Function Documentation

template<bool propto, bool jacobian_adjust_transform, class M >

void stan::model::finite_diff_grad	(	const M &	model,
		std::vector< double > &	params_r,
		std::vector< int > &	params_i,
		std::vector< double > &	grad,
		double	epsilon = `1e-6`,
		std::ostream *	msgs = `0`
	)

Compute the gradient using finite differences for the specified parameters, writing the result into the specified gradient, using the specified perturbation.

Template Parameters

propto	True if calculation is up to proportion (double-only terms dropped).
jacobian_adjust_transform	True if the log absolute Jacobian determinant of inverse parameter transforms is added to the log probability.
M	Class of model.

Parameters

	model	Model.
	params_r	Real-valued parameters.
	params_i	Integer-valued parameters.
[out]	grad	Vector into which gradient is written.
	epsilon
[in,out]	msgs

Definition at line 217 of file util.hpp.

template<bool propto, bool jacobian_adjust_transform, class M >

double stan::model::grad_hess_log_prob	(	const M &	model,
		std::vector< double > &	params_r,
		std::vector< int > &	params_i,
		std::vector< double > &	gradient,
		std::vector< double > &	hessian,
		std::ostream *	msgs = `0`
	)

Evaluate the log-probability, its gradient, and its Hessian at params_r.

This default version computes the Hessian numerically by finite-differencing the gradient, at a cost of O(params_r.size()^2).

Template Parameters

propto	True if calculation is up to proportion (double-only terms dropped).
jacobian_adjust_transform	True if the log absolute Jacobian determinant of inverse parameter transforms is added to the log probability.
M	Class of model.

Parameters

model	Model.
params_r	Real-valued parameter vector.
params_i	Integer-valued parameter vector.
gradient	Vector to write gradient to.
hessian	Vector to write gradient to. hessian[i*D + j] gives the element at the ith row and jth column of the Hessian (where D=params_r.size()).
msgs	Stream to which print statements in Stan programs are written, default is 0

Definition at line 341 of file util.hpp.

template<class M >

void stan::model::grad_tr_mat_times_hessian	(	const M &	model,
		const Eigen::Matrix< double, Eigen::Dynamic, 1 > &	x,
		const Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic > &	X,
		Eigen::Matrix< double, Eigen::Dynamic, 1 > &	grad_tr_X_hess_f,
		std::ostream *	msgs = `0`
	)

Definition at line 452 of file util.hpp.

template<class M >

void stan::model::gradient	(	const M &	model,
		const Eigen::Matrix< double, Eigen::Dynamic, 1 > &	x,
		double &	f,
		Eigen::Matrix< double, Eigen::Dynamic, 1 > &	grad_f,
		std::ostream *	msgs = `0`
	)

Definition at line 405 of file util.hpp.

template<class M >

void stan::model::gradient_dot_vector	(	const M &	model,
		const Eigen::Matrix< double, Eigen::Dynamic, 1 > &	x,
		const Eigen::Matrix< double, Eigen::Dynamic, 1 > &	v,
		double &	f,
		double &	grad_f_dot_v,
		std::ostream *	msgs = `0`
	)

Definition at line 428 of file util.hpp.

template<class M >

void stan::model::hessian	(	const M &	model,
		const Eigen::Matrix< double, Eigen::Dynamic, 1 > &	x,
		double &	f,
		Eigen::Matrix< double, Eigen::Dynamic, 1 > &	grad_f,
		Eigen::Matrix< double, Eigen::Dynamic, Eigen::Dynamic > &	hess_f,
		std::ostream *	msgs = `0`
	)

Definition at line 416 of file util.hpp.

template<class M >

void stan::model::hessian_times_vector	(	const M &	model,
		const Eigen::Matrix< double, Eigen::Dynamic, 1 > &	x,
		const Eigen::Matrix< double, Eigen::Dynamic, 1 > &	v,
		double &	f,
		Eigen::Matrix< double, Eigen::Dynamic, 1 > &	hess_f_dot_v,
		std::ostream *	msgs = `0`
	)

Definition at line 440 of file util.hpp.

template<bool propto, bool jacobian_adjust_transform, class M >

double stan::model::log_prob_grad	(	const M &	model,
		std::vector< double > &	params_r,
		std::vector< int > &	params_i,
		std::vector< double > &	gradient,
		std::ostream *	msgs = `0`
	)

Compute the gradient using reverse-mode automatic differentiation, writing the result into the specified gradient, using the specified perturbation.

Template Parameters

propto	True if calculation is up to proportion (double-only terms dropped).
jacobian_adjust_transform	True if the log absolute Jacobian determinant of inverse parameter transforms is added to the log probability.
M	Class of model.

Parameters

[in]	model	Model.
[in]	params_r	Real-valued parameters.
[in]	params_i	Integer-valued parameters.
[out]	gradient	Vector into which gradient is written.
[in,out]	msgs

Definition at line 83 of file util.hpp.

template<bool propto, bool jacobian_adjust_transform, class M >

double stan::model::log_prob_grad	(	const M &	model,
		Eigen::VectorXd &	params_r,
		Eigen::VectorXd &	gradient,
		std::ostream *	msgs = `0`
	)

Compute the gradient using reverse-mode automatic differentiation, writing the result into the specified gradient, using the specified perturbation.

Template Parameters

propto	True if calculation is up to proportion (double-only terms dropped).
jacobian_adjust_transform	True if the log absolute Jacobian determinant of inverse parameter transforms is added to the log probability.
M	Class of model.

Parameters

[in]	model	Model.
[in]	params_r	Real-valued parameters.
[out]	gradient	Vector into which gradient is written.
[in,out]	msgs

Definition at line 173 of file util.hpp.

template<bool jacobian_adjust_transform, class M >

double stan::model::log_prob_propto	(	const M &	model,
		std::vector< double > &	params_r,
		std::vector< int > &	params_i,
		std::ostream *	msgs = `0`
	)

Helper function to calculate log probability for double scalars up to a proportion.

This implementation wraps the double values in stan::agrad::var and calls the model's log_prob() function with propto=true and the specified parameter for applying the Jacobian adjustment for transformed parameters.

Template Parameters

propto	True if calculation is up to proportion (double-only terms dropped).
jacobian_adjust_transform	True if the log absolute Jacobian determinant of inverse parameter transforms is added to the log probability.
M	Class of model.

Parameters

[in]	model	Model.
[in]	params_r	Real-valued parameters.
[in]	params_i	Integer-valued parameters.
[in,out]	msgs

Definition at line 41 of file util.hpp.

template<bool jacobian_adjust_transform, class M >

double stan::model::log_prob_propto	(	const M &	model,
		Eigen::VectorXd &	params_r,
		std::ostream *	msgs = `0`
	)

Helper function to calculate log probability for double scalars up to a proportion.

This implementation wraps the double values in stan::agrad::var and calls the model's log_prob() function with propto=true and the specified parameter for applying the Jacobian adjustment for transformed parameters.

Template Parameters

propto	True if calculation is up to proportion (double-only terms dropped).
jacobian_adjust_transform	True if the log absolute Jacobian determinant of inverse parameter transforms is added to the log probability.
M	Class of model.

Parameters

[in]	model	Model.
[in]	params_r	Real-valued parameters.
[in,out]	msgs

Definition at line 133 of file util.hpp.

template<bool propto, bool jacobian_adjust_transform, class M >

int stan::model::test_gradients	(	const M &	model,
		std::vector< double > &	params_r,
		std::vector< int > &	params_i,
		double	epsilon = `1e-6`,
		double	error = `1e-6`,
		std::ostream &	o = `std::cout`,
		std::ostream *	msgs = `0`
	)

Test the log_prob_grad() function's ability to produce accurate gradients using finite differences.

This shouldn't be necessary when using autodiff, but is useful for finding bugs in hand-written code (or agrad).

Template Parameters

propto	True if calculation is up to proportion (double-only terms dropped).
jacobian_adjust_transform	True if the log absolute Jacobian determinant of inverse parameter transforms is added to the log probability.
M	Class of model.

Parameters

model	Model.
params_r	Real-valued parameter vector.
params_i	Integer-valued parameter vector.
epsilon	Real-valued scalar saying how much to perturb. Defaults to 1e-6.
error	Real-valued scalar saying how much error to allow. Defaults to 1e-6.
o	Output stream for messages. Defaults to std::cout.
msgs	Stream to which Stan programs write. Defaults to 0.

Returns: number of failed gradient comparisons versus allowed error, so 0 if all gradients pass

Definition at line 268 of file util.hpp.

Classes

Functions

Detailed Description

Function Documentation