laplace_marginal_tol_neg_binomial_2_log_lpmf()

template<bool propto = false, typename Eta , typename ThetaVec , typename Mean , typename CovarFun , typename CovarArgs , require_eigen_vector_t< ThetaVec > * = nullptr>

auto stan::math::laplace_marginal_tol_neg_binomial_2_log_lpmf ( const std::vector< int > &  y, const std::vector< int > &  y_index, const Eta &  eta, Mean &&  mean, 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 function for a negative binomial likelihood.

Uses the 2nd parameterization. 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

Eta	The type of parameter arguments for the likelihood function.
ThetaVec	A type inheriting from Eigen::EigenBase with dynamic sized rows and 1 column.
Mean	type of the mean of the latent normal distribution
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]	y	observed counts.
[in]	y_index	group to which each observation belongs. Each group is parameterized by one element of theta.
[in]	eta	non-marginalized model parameters for the likelihood.
[in]	mean	the mean of the latent normal variable
[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 77 of file laplace_marginal_neg_binomial_2_log_lpmf.hpp.