  Fisher matrices play an important role in experimental design and in data
analysis. Their primary role is to make predictions for the inference of model
parameters - both their errors and covariances. In this short review, I outline
a number of extensions to the simple Fisher matrix formalism, covering a number
of recent developments in the field. These are: (a) situations where the data
(in the form of (x,y) pairs) have errors in both x and y; (b) modifications to
parameter inference in the presence of systematic errors, or through fixing the
values of some model parameters; (c) Derivative Approximation for LIkelihoods
(DALI) - higher-order expansions of the likelihood surface, going beyond the
Gaussian shape approximation; (d) extensions of the Fisher-like formalism, to
treat model selection problems with Bayesian evidence.
