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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.