diff_eta_implicit()

template<typename F , typename V_t , typename Theta , typename Stream , typename... Args, require_eigen_vector_t< Theta > * = nullptr>

auto stan::math::laplace_likelihood::internal::diff_eta_implicit ( F &&  f, V_t &&  v, Theta &&  theta, Stream *  msgs, Args &&...  args  )

inline

Compute second order gradient of f wrt theta and args...

Note

See proposition 2 in https://arxiv.org/pdf/2306.14976 See lines 31-37 in Algorithm 4 If Args contains var types then their adjoints will be calculated as a side effect.

Template Parameters

F	A functor with opertor()(Args&&...) returning a scalar
V_t	A type assignable to an Eigen vector type
Theta	A type assignable to an Eigen vector type
Stream	Type of stream for messages.
Args	Parameter pack of arguments to F's operator()

Parameters

f	Log likelihood function.
v	Initial tangent.
theta	Latent Gaussian variable.
msgs	Stream for messages.
args	Variadic arguments for likelhood function.

Returns

args which are var types will have their adjoints set as a side effect of this function.

Definition at line 276 of file laplace_likelihood.hpp.