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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 Poisson with a log 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 | Observed counts. |
[in] | y_index | Index indicating which group each observation belongs to. |
[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 73 of file laplace_latent_poisson_log_rng.hpp.