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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
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() |
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] | 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] | rng | Random number generator |
[in,out] | msgs | stream for messages from likelihood and covariance |
Definition at line 36 of file laplace_latent_poisson_log_rng.hpp.