TransportMaps.Routines
¶
Module Contents¶
Functions¶
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Compute the maximum likelihood of the log-likelihood \(\log\pi({\bf y}\vert{\bf x})\). |
- TransportMaps.Routines.maximum_likelihood(logL, params=None, x0=None, tol=1e-05, ders=2, fungrad=False)[source]¶
Compute the maximum likelihood of the log-likelihood \(\log\pi({\bf y}\vert{\bf x})\).
- Parameters:
logL (
LogLikelihood
) – log-likelihood \(\log\pi({\bf y}\vert{\bf x})\)params (dict) – parameters for the log-likelihood \(\log\pi({\bf y}\vert{\bf x})\)
tol (float) – tolerance to be used to solve the maximization problem
ders (int) – order of derivatives available for the solution of the optimization problem. 0 -> derivative free, 1 -> gradient, 2 -> hessian
fungrad (bool) – whether the distributions \(\pi_1,\pi_2\) provide the method
Distribution.tuple_grad_x_log_pdf()
computing the evaluation and the gradient in one step. This is used only forders==1
- Returns:
(
ndarray
) – Maximum likelihood estimator