http://arxiv.org/abs/1606.06455
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.
A. Heavens
Wed, 22 Jun 16
29/50
Comments: Invited review article for Entropy special issue on ‘Applications of Fisher Information in Sciences’. Accepted version