laplace_marginal_tol()

template<bool propto = false, typename LFun , typename LArgs , typename CovarFun , typename ThetaVec , typename CovarArgs , require_all_eigen_vector_t< ThetaVec > * = nullptr>

auto stan::math::laplace_marginal_tol ( LFun &&  L_f, LArgs &&  l_args, CovarFun &&  covariance_function, CovarArgs &&  covar_args, const ThetaVec &  theta_0, double  tolerance, int  max_num_steps, const int  hessian_block_size, const int  solver, const int  max_steps_line_search, std::ostream *  msgs  )

inline

Wrapper function around the laplace_marginal_density function.

Returns the marginal density p(y|phi) by marginalizing out the latent gaussian variable theta, with a Laplace approximation. See the laplace_marginal function for more details. The data y is assumed to be real. The function is "overloaded" below for the int y and lpmf case.

Template Parameters

propto	If FALSE, log density is computed up to an additive const.
LFun	The function which returns the log likelihood.
LArgs	A tuple of arguments to the log likelihood.
EtaVec	The type of the parameter arguments for the likelihood fn.
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()

Parameters

[in]	L_f	a function which returns the log likelihood.
[in]	l_args	A tuple of arguments to pass to the log likelihood.
[in]	covariance_function	a function which returns the prior covariance.
[in]	covar_args	arguments for the covariance function.
[in]	theta_0	the initial guess for the Laplace approximation.
[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]	msgs	stream for messages from likelihood and covariance

Definition at line 31 of file laplace_marginal.hpp.