pygsti.tools.likelihoodfns

Functions related to computation of the log-likelihood.

Functions

logl(model, dataset[, circuits, ...])	The log-likelihood function.
logl_approximate_hessian(model, dataset[, ...])	An approximate Hessian of the log-likelihood function.
logl_hessian(model, dataset[, circuits, ...])	The hessian of the log-likelihood function.
logl_jacobian(model, dataset[, circuits, ...])	The jacobian of the log-likelihood function.
logl_max(model, dataset[, circuits, ...])	The maximum log-likelihood possible for a DataSet.
logl_max_per_circuit(model, dataset[, ...])	The vector of maximum log-likelihood contributions for each circuit, aggregated over outcomes.
logl_per_circuit(model, dataset[, circuits, ...])	Computes the per-circuit log-likelihood contribution for a set of circuits.
two_delta_logl(model, dataset[, circuits, ...])	Twice the difference between the maximum and actual log-likelihood.
two_delta_logl_nsigma(model, dataset[, ...])	See docstring for pygsti.tools.two_delta_logl()
two_delta_logl_per_circuit(model, dataset[, ...])	Twice the per-circuit difference between the maximum and actual log-likelihood.
two_delta_logl_term(n, p, f[, ...])	Term of the 2*[log(L)-upper-bound - log(L)] sum corresponding to a single circuit and spam label.