TransportMaps.Routines

Module Contents

Functions

maximum_likelihood(logL[, params, x0, tol, ders, fungrad])

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 for ders==1

Returns:

(ndarray) – Maximum likelihood estimator