Calculate the log-probability , the gradient w.r.t. each input, and the hessian for a given vector of unconstrained parameters — fit-method-hessian • cmdstanr

The $hessian() method provides access to the Stan model's log_prob, its derivative, and its hessian.

hessian(unconstrained_variables, jacobian = TRUE, jacobian_adjustment = NULL)

Arguments

unconstrained_variables: (numeric) A vector of unconstrained parameters.
jacobian: (logical) Whether to include the log-density adjustments from un/constraining variables.
jacobian_adjustment: Deprecated. Please use jacobian instead.

See also

log_prob(), grad_log_prob(), constrain_variables(), unconstrain_variables(), unconstrain_draws(), variable_skeleton(), hessian()

Examples

# \dontrun{
fit_mcmc <- cmdstanr_example("logistic", method = "sample", force_recompile = TRUE)
# fit_mcmc$init_model_methods(hessian = TRUE)
# fit_mcmc$hessian(unconstrained_variables = c(0.5, 1.2, 1.1, 2.2))
# }