laplace_latent_solve_tol()

template<typename LLFunc , typename LLArgs , typename CovarFun , typename CovarArgs , typename OpsTuple >

auto stan::math::laplace_latent_solve_tol ( LLFunc &&  ll_fun, LLArgs &&  ll_args, int  hessian_block_size, CovarFun &&  covariance_function, CovarArgs &&  covar_args, OpsTuple &&  ops, std::ostream *  msgs  )

inline

In a latent gaussian model,.

theta ~ Normal(0, Sigma(phi)) y ~ p(y|theta,phi)

returns the posterior mean and Cholesky factor from the Laplace approximation to p(theta|y,phi), where the log likelihood is given by L_f.

Template Parameters

LLFunc	Type of likelihood function.
LLArgs	Tuple of arguments types of likelihood function.
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()
OpsTuple	A tuple of laplace_options types

Parameters

	ll_fun	Likelihood function.
	ll_args	Arguments for likelihood function.
[in]	covariance_function	a function which returns the prior covariance.
[in]	covar_args	arguments for the covariance function.
[in]	hessian_block_size	Block size for the Hessian approximation with respect to the latent gaussian variable theta.
[in]	ops	Options for controlling Laplace approximation. The following options are available: - theta_0 the initial guess for the Laplace approximation. - tolerance controls the convergence criterion when finding the mode in the Laplace approximation. - max_num_steps maximum number of steps before the Newton solver breaks and returns an error. - 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. - max_steps_line_search Number of steps after which the algorithm gives up on doing a line search. If 0, no linesearch. - allow_fallthrough If true, if solver 1 fails then solver 2 is tried, and if that fails solver 3 is tried.
[in,out]	msgs	stream for messages from likelihood and covariance

Definition at line 38 of file laplace_latent_solve.hpp.