mlestimate {NeuralEstimators} | R Documentation |
Maximum likelihood estimation
Description
Given data Z
and a neural likelihood-to-evidence-ratio estimator
, computes the implied approximate maximum-likelihood estimate
If a vector theta0
of initial parameter estimates is given, the approximate likelihood is maximised by gradient descent. Otherwise, if a matrix of parameters theta_grid
is given, the approximate likelihood is maximised by grid search.
Usage
mlestimate(estimator, Z, theta_grid = NULL, theta0 = NULL, use_gpu = TRUE)
Arguments
estimator |
a neural likelihood-to-evidence-ratio estimator |
Z |
data; it's format should be amenable to the architecture of |
theta_grid |
a (fine) gridding of the parameter space, given as a matrix with p rows, where p is the number of parameters in the model |
theta0 |
a vector of initial parameter estimates |
use_gpu |
a boolean indicating whether to use the GPU if it is available (default true) |
Value
a p × K matrix of maximum-likelihood estimates, where p is the number of parameters in the statistical model and K is the number of data sets provided in Z
See Also
sampleposterior()
, mapestimate()