A general, recursive algorithm is presented for computing the expected Fisher information matrix for state-space model parameters. Simulation results are featured where known Fisher information matrices corresponding to simple state-space models are estimated using both observed and expected information matrices. The accuracy of the two approaches is compared.

Fisher’s linear discriminant analysis (FLDA) has been attracting many researchers and practitioners for several decades thanks to its ease of use and low computational cost. However, FLDA implicitly assumes that all the classes share the same covariance: which implies that FLDA might fail when thhis assumption is not necessarily satisfied. To overcome this problem, we propose a simple extension...

By using the q -Gaussian distribution derived by the maximum entropy method for spatially correlated N -unit nonextensive systems, we have calculated the generalized Fisher information matrix of gthetanthetam for (theta1,theta2,theta3)=(muq,sigmaq2,s) , where muq, sigmaq2, and s denote the mean, variance, and degree of spatial correlation, respectively, for a given entropic index q . It has bee...

The paper presents closed form expressions and an algorithm for obtaining the Fisher information matrix of Gaussian single input single output (SISO) time series models. It enables the computation of the asymptotic covariance matrix of maximum likelihood estimators of the parameters. The procedure makes use of the autocovariance function of one or more autoregressive processes. Under certain co...

Data sets that are acquired in many practical systems can be described as the output of a multidimensional linear separable-denominator system with Gaussian measurement noise. An important example is nuclear magnetic resonance (NMR) spectroscopy. In NMR spectroscopy, high-accuracy parameter estimation is of central importance. A classical result on the Cramér–Rao lower bound states that the inv...

The Fisher-matrix formalism is used routinely in the literature on gravitational-wave detection to characterize the parameter-estimation performance of gravitational-wave measurements, given parametrized models of the waveforms, and assuming detector noise of known colored Gaussian distribution. Unfortunately, the Fisher matrix can be a poor predictor of the amount of information obtained from ...

A family of probability distributions parametrized by an open domain Λ in Rn defines the Fisher information matrix on this domain which is positive semi-definite. In information geometry the standard assumption has been that the Fisher information matrix is positive definite defining in this way a Riemannian metric on Λ. If we replace the "positive definite" assumption by "0-deformable" conditi...

Abstract We introduce GWFAST ( https://github.com/CosmoStatGW/gwfast ), a Fisher information matrix Python code that allows for easy and efficient estimation of signal-to-noise ratios parameter measurement errors large catalogs resolved sources observed by networks gravitational-wave (GW) detectors. In particular, includes the effects Earth’s motion during evolution signal, supports parallel co...