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The Fisher information matrix (FIM) has long been of interest in statistics and other areas. It is widely used to measure the amount of information and calculate the lower bound for the variance for maximum likelihood estimation (MLE). In…

Information Theory · Computer Science 2015-06-19 Shenghan Guo

The subject of this paper is a mathematical transition from the Fisher information of classical statistics to the matrix formalism of quantum theory. If the monotonicity is the main requirement, then there are several quantum versions…

Quantum Physics · Physics 2017-08-23 Denes Petz , Catalin Ghinea

We introduce a new kind of quantum measurement that is defined to be symmetric in the sense of uniform Fisher information across a set of parameters that injectively represent pure quantum states in the neighborhood of a fiducial pure…

Quantum Physics · Physics 2016-05-11 Nan Li , Christopher Ferrie , Jonathan A. Gross , Amir Kalev , Carlton M. Caves

We show that the mathematical form of the information measure of Fisher's I for a Gibbs' canonical probability distribution (the most important one in statistical mechanics) incorporates important features of the intrinsic structure of…

Statistical Mechanics · Physics 2007-05-23 F. Pennini , A. Plastino

In this paper, we consider a parametric hidden Markov model where the hidden state space is non necessarily finite. We provide a necessary and sufficient condition for the invertibility of the limiting Fisher information matrix.

Statistics Theory · Mathematics 2007-06-13 Randal Douc

We derive a new variational principle for the quantum Fisher information leading to a simple iterative alternating algorithm, the convergence of which is proved. The case of a fixed measurement, i.e. the classical Fisher information, is…

Quantum Physics · Physics 2013-12-06 Katarzyna Macieszczak

Quantum Fisher Information (QFI) is a ubiquitous quantity with applications ranging from quantum metrology and resource theories to condensed matter physics. In equilibrium local quantum many-body systems, the QFI of a subsystem with…

Quantum Physics · Physics 2025-04-30 Florent Ferro , Maurizio Fagotti

For normalized sums $Z_n$ of i.i.d. random variables, we explore necessary and sufficient conditions which guarantee the normal approximation with respect to the R\'enyi divergence of infinite order. In terms of densities $p_n$ of $Z_n$,…

Probability · Mathematics 2024-06-21 Sergey G. Bobkov , Friedrich Götze

Results by van der Vaart (1991) from semi-parametric statistics about the existence of a non-zero Fisher information are reviewed in an infinite-dimensional non-linear Gaussian regression setting. Information-theoretically optimal inference…

Statistics Theory · Mathematics 2021-07-21 Richard Nickl , Gabriel Paternain

Relative Fisher information (IR), which is a measure of correlative fluctuation between two probability densities, has been pursued for a number of quantum systems, such as, 1D quantum harmonic oscillator (QHO) and a few central potentials…

Quantum Physics · Physics 2019-04-05 Neetik Mukherjee , Amlan K. Roy

Local information objectivity, that local, independent observers can infer the same information about a model upon exchange of independently acquired experimental data, is fundamental to science. It is mathematically encoded via Cencov's…

General Relativity and Quantum Cosmology · Physics 2026-05-19 Per Berglund , Andrew Geraci , Tristan Hubsch , David Mattingly , Djordje Minic

Expected Fisher information can be found a priori and as a result its inverse is the primary variance approximation used in the design of experiments. This is in contrast to the common claim that the inverse of observed Fisher information…

Methodology · Statistics 2022-08-04 Adam Lane

We discuss the possibility of extending the RG flows to complex coupling spaces. We argue that the Fisher's zeros are located at the boundary of the complex basin of attraction of IR fixed points. We support this picture with numerical…

High Energy Physics - Lattice · Physics 2011-12-14 A. Bazavov , A. Denbleyker , Daping Du , Yuzhi Liu , Y. Meurice , Haiyuan Zou

The quantum Fisher information constrains the achievable precision in parameter estimation via the quantum Cram\'er-Rao bound, which has attracted much attention in Hermitian systems since the 60s of the last century. However, less…

Quantum Physics · Physics 2021-03-15 Jianning Li , Haodi Liu , Zhihai Wang , Xuexi Yi

New inequalities are proved for the variance of the Pitman estimators (minimum variance equivariant estimators) of \theta constructed from samples of fixed size from populations F(x-\theta). The inequalities are closely related to the…

Statistics Theory · Mathematics 2013-07-11 Abram M. Kagan , Tinghui Yu , Andrew Barron , Mokshay Madiman

In this paper, we study an asymptotic approximation of the Fisher information for the estimation of a scalar parameter using quantized measurements. We show that, as the number of quantization intervals tends to infinity, the loss of Fisher…

Information Theory · Computer Science 2013-10-28 Rodrigo Cabral Farias , Jean-Marc Brossier

We analyze the Shannon and Fisher information measures for systems subjected to quartic and symmetric potential wells. The wave functions are obtained by solving the time-independent Schr\"{o}dinger equation, using aspects of perturbation…

Quantum Physics · Physics 2025-01-07 Vikash Kumar Ojha , Ramkumar Radhakrishnan , Mariyah Ughradar

We study the evolution of the nonlinear version of the Fisher information along the quasilinear heat equation. We also provide a nonlinear version of a recent functional inequality (Cie\'slak--Fuest--Hajduk--Sier\.z\k{e}ga, 2024),…

Analysis of PDEs · Mathematics 2025-09-03 Tomasz Cieślak , Kentaro Fujie , Tatsuya Hosono

Classically, Fisher information is the relevant object in defining optimal experimental designs. However, for models that lack certain regularity, the Fisher information does not exist and, hence, there is no notion of design optimality…

Statistics Theory · Mathematics 2019-12-04 Yi Lin , Ryan Martin , Min Yang

In this paper, we examine the fundamental performance limits of prediction, with or without side information. More specifically, we derive generic lower bounds on the $\mathcal{L}_p$ norms of the prediction errors that are valid for any…

Machine Learning · Computer Science 2021-06-07 Song Fang , Quanyan Zhu