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    Stan Math Library
    5.1.0
    
   Automatic Differentiation 
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In a latent gaussian model,.
theta ~ Normal(0, Sigma(phi)) y ~ p(y|theta,phi)
return a sample from the Laplace approximation to p(theta|y,phi). The Laplace approximation is computed using a Newton solver. In this specialized function, the likelihood p(y|theta) is a Negative Binomial with a log link. This function uses the second parameterization of the Negative Binomial.
| Eta | A type for the overdispersion parameter. | 
| 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 | Observed counts. | 
| [in] | y_index | Index indicating which group each observation belongs to. | 
| [in] | eta | Overdisperison parameter. | 
| [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 84 of file laplace_latent_neg_binomial_2_log_rng.hpp.