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    Stan Math Library
    5.1.0
    
   Automatic Differentiation 
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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.
| 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()  | 
| RNG | A valid boost rng type | 
| [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,out] | rng | Random number generator | 
| [in,out] | msgs | stream for messages from likelihood and covariance | 
Definition at line 72 of file laplace_latent_bernoulli_logit_rng.hpp.