Calculate the log-probability and the gradient w.r.t. each input for a given vector of unconstrained parameters

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

Usage

grad_log_prob(unconstrained_variables, jacobian = TRUE)

Arguments

unconstrained_variables

(numeric) A vector of unconstrained parameters.

jacobian

(logical) Whether to include the log-density adjustments from constraining or unconstraining variables.

Value

A numeric vector containing the gradient, with the log probability stored in the "log_prob" attribute.

See also

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

Examples

# \dontrun{
fit_mcmc <- cmdstanr_example("logistic", method = "sample", force_recompile = TRUE)
fit_mcmc$grad_log_prob(unconstrained_variables = c(0.5, 1.2, 1.1, 2.2))
#> [1]   1.462151 -26.619534 -25.528776 -14.286822
#> attr(,"log_prob")
#> [1] -130.2141
# }