◆ laplace_latent_tol_poisson_2_log_rng()

template<typename YeVec , typename ThetaVec , typename CovarFun , typename CovarArgs , typename RNG , require_eigen_t< ThetaVec > * = nullptr>

auto stan::math::laplace_latent_tol_poisson_2_log_rng	(	const std::vector< int > &	y,
		const std::vector< int > &	y_index,
		const YeVec &	ye,
		ThetaVec &&	theta_0,
		CovarFun &&	covariance_function,
		CovarArgs &&	covar_args,
		const double	tolerance,
		const int	max_num_steps,
		const int	hessian_block_size,
		const int	solver,
		const int	max_steps_line_search,
		RNG &	rng,
		std::ostream *	msgs
	)

inline

Wrapper function around the laplace_marginal function for a log poisson likelihood with exposure.

Returns the marginal density p(y | phi) by marginalizing out the latent gaussian variable, with a Laplace approximation. See the laplace_marginal function for more details.

Template Parameters

YeVec	A type inheriting from `Eigen::EigenBase` with dynamic sized rows and 1 column.
ThetaVec	A type inheriting from `Eigen::EigenBase` with dynamic sized rows and 1 column.
CovarFun	A functor with an `operator()(CovarArgsElements..., {TrainTupleElements...\| PredTupleElements...})` method. The `operator()` method should accept as arguments the inner elements of `CovarArgs`. The return type of the `operator()` method should be a type inheriting from `Eigen::EigenBase` with dynamic sized rows and columns.
CovarArgs	A tuple of types to passed as the first arguments of `CovarFun::operator()`
RNG	A valid boost rng type

Parameters

[in]	y	total counts per group. Second sufficient statistics.
[in]	y_index	group to which each observation belongs.
[in]	ye	the exposure for each group.
[in]	theta_0	the initial guess for the Laplace approximation.
[in]	covariance_function	a function which returns the prior covariance.
[in]	covar_args	arguments for the covariance function.
[in]	tolerance	controls the convergence criterion when finding the mode in the Laplace approximation.
[in]	max_num_steps	maximum number of steps before the Newton solver breaks and returns an error.
[in]	hessian_block_size	Block size of Hessian of log likelihood w.r.t latent Gaussian variable theta.
[in]	solver	Type of Newton solver. Each corresponds to a distinct choice of B matrix (i.e. application SWM formula): 1. computes square-root of negative Hessian. 2. computes square-root of covariance matrix. 3. computes no square-root and uses LU decomposition.
[in]	max_steps_line_search	Number of steps after which the algorithm gives up on doing a line search. If 0, no linesearch.
[in,out]	rng	Random number generator
[in,out]	msgs	stream for messages from likelihood and covariance

Definition at line 33 of file laplace_latent_poisson_log_2_rng.hpp.