make_log_hessian.Rd- m_expected_value is the expectated value matrix - m_variance is the matrix of variances - A, m_expected_value, m_variance all have shape c(size, size) - The variables _v are copies of the originals to shape c(npar,size,size), paralleling the gradient of g. - The variables _m are copies of the originals to shape c(npar,npar,size,size), paralleling the hessian of g hessian-of-objective-function
make_log_hessian(a, A, dnom, g_obj, g_grad, g_hess)
| a | do not know |
|---|---|
| A | do not know |
| dnom | numeric vector representing the exposures (claims) used in the denominator |
| g_obj | objective function |
| g_grad | gradient function |
| g_hess | hessian function |