laplace_latent_tol_bernoulli_logit_rng()

template<typename Mean , typename CovarFun , typename CovarArgs , typename OpsTuple , typename RNG >

Eigen::VectorXd stan::math::laplace_latent_tol_bernoulli_logit_rng ( const std::vector< int > &  y, const std::vector< int > &  n_samples, Mean &&  mean, CovarFun &&  covariance_function, CovarArgs &&  covar_args, OpsTuple &&  ops, RNG &  rng, std::ostream *  msgs  )

inline

In a latent gaussian model,.

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

return a multivariate normal random variate sampled from the gaussian approximation of p(theta | y, phi), where the likelihood is a Bernoulli with logit link.

Template Parameters

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()
OpsTuple	A tuple of laplace_options types
RNG	A valid boost rng type

Parameters

[in]	y	Vector Vector of total number of trials with a positive outcome.
[in]	n_samples	Vector of number of trials.
[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]	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. - hessian_block_size Block size of Hessian of log likelihood w.r.t latent Gaussian variable theta. - 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]	rng	Random number generator
[in,out]	msgs	stream for messages from likelihood and covariance

Definition at line 33 of file laplace_latent_bernoulli_logit_rng.hpp.