networkqit.algorithms.optimize.MLEOptimizer

class MLEOptimizer(G: autograd.numpy.numpy_wrapper.array, x0: autograd.numpy.numpy_wrapper.array, model: networkqit.graphtheory.models.GraphModel.GraphModel, **kwargs)

This class, inheriting from the model optimizer class solves the problem of maximum likelihood parameters estimation in the classical settings.

__init__(G: autograd.numpy.numpy_wrapper.array, x0: autograd.numpy.numpy_wrapper.array, model: networkqit.graphtheory.models.GraphModel.GraphModel, **kwargs)

Initialize the optimizer with the observed graph, an initial guess and the model to optimize.

Args:

G (numpy.array) :is the empirical network to study. A N x N adjacency matrix as numpy.array. x0 (numpy.array): is the k-element array of initial parameters estimates. Typically set as random. model (nq.GraphModel): the graph model to optimize the likelihood of.

Methods

__init__(G, x0, model, **kwargs)	Initialize the optimizer with the observed graph, an initial guess and the model to optimize.
run(method[, ftol, gtol, xtol, maxiter])	Maximimize the likelihood of the model given the observed network G.