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Post-data statistical inference concerns making probability statements about model parameters conditional on observed data. When a priori knowledge about parameters is available, post-data inference can be conveniently made from Bayesian…

Statistics Theory · Mathematics 2025-06-05 Yang Liu , Jan Hannig , Alexander C Murph

Gene expression is a stochastic process in which cells produce biomolecules essential to the function of life. Modern experimental methods allow for the measurement of biomolecules at single-cell and single-molecule resolution over time.…

Quantitative Methods · Quantitative Biology 2022-07-01 Zachary R Fox

In quantum metrology, the parameter estimation accuracy is bounded by quantum Fisher information. In this paper, we present coherence measures in terms of (quantum) Fisher information by directly considering the post-selective non-unitary…

Quantum Physics · Physics 2023-07-06 Deng-hui Yu , Chang-shui Yu

The net Fisher information measure, defined as the product of position and momentum Fisher information measures and derived from the non-relativistic Hartree-Fock wave functions for atoms with Z=1-102, is found to correlate well with the…

Quantum Physics · Physics 2015-06-26 K. D. Sen , C. P. Panos , K. Ch. Chatzisavvas , Ch. C. Moustakidis

The quantum Fisher information (QFI) represents a fundamental concept in quantum physics. On the one hand, it quantifies the metrological potential of quantum states in quantum-parameter-estimation measurements. On the other hand, it is…

Expert specialization is fundamental to Mixture-of-Experts (MoE) model success, yet existing metrics (cosine similarity, routing entropy) lack theoretical grounding and yield inconsistent conclusions under reparameterization. We present an…

Artificial Intelligence · Computer Science 2026-04-17 Dongxin Guo , Jikun Wu , Siu Ming Yiu

Point estimators may not exist, need not be unique, and their distributions are not parameter invariant. Generalized estimators provide distributions that are parameter invariant, unique, and exist when point estimates do not. Comparing…

Statistics Theory · Mathematics 2022-11-04 Paul W. Vos

We consider torsion in parameter manifolds that arises via conformal transformations of the Fisher information metric, and define it for information geometry of a wide class of physical systems. The torsion can be used to differentiate…

Classical Physics · Physics 2023-08-09 Kunal Pal , Kuntal Pal , Tapobrata Sarkar

In the present paper, we would like to draw attention to a possible generalized Fisher information that fits well in the formalism of nonextensive thermostatistics. This generalized Fisher information is defined for densities on…

Mathematical Physics · Physics 2013-01-18 J. -F. Bercher

We introduce a concept of distance for a space-time where the notion of point is replaced by the notion of physical states e.g. probability distributions. We apply ideas of information theory and compute the Fisher information matrix on…

Mathematical Physics · Physics 2007-05-23 Jacques Calmet , Xavier Calmet

In biology, information flows from the environment to the genome by the process of natural selection. But it has not been clear precisely what sort of information metric properly describes natural selection. Here, I show that Fisher…

Populations and Evolution · Quantitative Biology 2009-06-18 Steven A. Frank

Quantum metrology is a science about quantum measurements and it plays a key role in precision of quantum parameter estimation. Meanwhile, quantum coherence is an important quantum feature and quantum Fisher information (QFI) is an…

Quantum Physics · Physics 2025-10-30 Jun-Long Zhao , Li Yu , Ming Yang , Chui-Ping Yang

Statistical divergences are important tools in data analysis, information theory, and statistical physics, and there exist well known inequalities on their bounds. However, in many circumstances involving temporal evolution, one needs…

Data Analysis, Statistics and Probability · Physics 2025-03-25 Jan Karbowski

Gravitational-wave detectors operate in inherently non-stationary environments, requiring robust detector characterization (DetChar) to distinguish instrumental transients from astrophysical signals. Traditional DetChar frameworks typically…

Instrumentation and Methods for Astrophysics · Physics 2026-05-22 James Kennington , Zach Yarbrough

The Fisher Information Matrix formalism is extended to cases where the data is divided into two parts (X,Y), where the expectation value of Y depends on X according to some theoretical model, and X and Y both have errors with arbitrary…

Cosmology and Nongalactic Astrophysics · Physics 2015-02-20 A. F. Heavens , M. Seikel , B. D. Nord , M. Aich , Y. Bouffanais , B. A. Bassett , M. P. Hobson

This paper considers the problem of estimation of the Fisher information for location from a random sample of size $n$. First, an estimator proposed by Bhattacharya is revisited and improved convergence rates are derived. Second, a new…

Information Theory · Computer Science 2020-05-08 Wei Cao , Alex Dytso , Michael Fauß , H. Vincent Poor , Gang Feng

A deep neural network is a hierarchical nonlinear model transforming input signals to output signals. Its input-output relation is considered to be stochastic, being described for a given input by a parameterized conditional probability…

Machine Learning · Computer Science 2018-08-23 Shun-ichi Amari , Ryo Karakida , Masafumi Oizumi

The paper deals with the reformulation of quantum uncertainty relation involving position and momentum of a particle on the basis of the Kerridge measure of inaccuracy and the Fisher information.

Quantum Physics · Physics 2007-05-23 Indranil Chakrabarty

A new information-theoretic approach to the central limit theorem for stable laws is presented. The main novelty is the concept of relative fractional Fisher information, which shares most of the properties of the classical one, included…

Information Theory · Computer Science 2015-04-28 Giuseppe Toscani

We introduce two new classes of measures of information for statistical experiments which generalise and subsume $\phi$-divergences, integral probability metrics, $\mathfrak{N}$-distances (MMD), and $(f,\Gamma)$ divergences between two or…

Machine Learning · Computer Science 2023-09-11 Robert C. Williamson , Zac Cranko
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