Results 11 to 20 of about 636,793 (279)

Simple Exponential Family PCA [PDF]

IEEE Transactions on Neural Networks and Learning Systems, 2013
Principal component analysis (PCA) is a widely used model for dimensionality reduction. In this paper, we address the problem of determining the intrinsic dimensionality of a general type data population by selecting the number of principal components for a generalized PCA model.
Jun, Li, Dacheng, Tao
openaire   +4 more sources

Geometry of q-Exponential Family of Probability Distributions

Entropy, 2011
The Gibbs distribution of statistical physics is an exponential family of probability distributions, which has a mathematical basis of duality in the form of the Legendre transformation.
Shun-ichi Amari, Atsumi Ohara
doaj   +3 more sources

Tangent exponential-G family of distributions with applications in medical and engineering

Alexandria Engineering Journal
This study introduces a novel family of probability distributions called the ”tangent exponential-G family,” which is derived using trigonometric transformations of the exponential distribution.
Eslam Hussam, Laxmi Prasad Sapkota, Ahmed M. Gemeay   +2 more
doaj   +3 more sources

Deterministic Approximate EM Algorithm; Application to the Riemann Approximation EM and the Tempered EM

Algorithms, 2022
The Expectation Maximisation (EM) algorithm is widely used to optimise non-convex likelihood functions with latent variables. Many authors modified its simple design to fit more specific situations.
Thomas Lartigue, Stanley Durrleman, Stéphanie Allassonnière   +2 more
doaj   +1 more source

A Simple Approximation Method for the Fisher–Rao Distance between Multivariate Normal Distributions

Entropy, 2023
We present a simple method to approximate the Fisher–Rao distance between multivariate normal distributions based on discretizing curves joining normal distributions and approximating the Fisher–Rao distances between successive nearby normal ...
Frank Nielsen
doaj   +1 more source

$q$-Exponential Families [PDF]

The Electronic Journal of Combinatorics, 2004
We develop an analog of the exponential families of Wilf in which the label sets are finite dimensional vector spaces over a finite field rather than finite sets of positive integers. The essential features of exponential families are preserved, including the exponential formula relating the deck enumerator and the hand enumerator.
openaire   +3 more sources

Statistical Properties and Applications of the Exponentiated Chen-G Family of Distributions: Exponential Distribution as a Baseline Distribution

Austrian Journal of Statistics, 2022
In this work, the Exponentiated Chen-G family of distributions is studied by generalizing the Chen-G family of distributions through the introduction of an additional shape parameter. The mixture properties of the derived family are studied.
Phillip Awodutire
doaj   +1 more source

Intrinsic Losses Based on Information Geometry and Their Applications

Entropy, 2017
One main interest of information geometry is to study the properties of statistical models that do not depend on the coordinate systems or model parametrization; thus, it may serve as an analytic tool for intrinsic inference in statistics. In this paper,
Yao Rong, Mengjiao Tang, Jie Zhou
doaj   +1 more source

Using Geometry to Select One Dimensional Exponential Families That Are Monotone Likelihood Ratio in the Sample Space, Are Weakly Unimodal and Can Be Parametrized by a Measure of Central Tendency

Entropy, 2014
One dimensional exponential families on finite sample spaces are studied using the geometry of the simplex Δn°-1  and that of a transformation Vn-1 of its interior.
Paul Vos, Karim Anaya-Izquierdo
doaj   +1 more source

Reproductive Exponential Families

The Annals of Statistics, 1983
Consider a full and steep exponential model $\mathscr{M}$ with model function $a(\theta)b(x)\exp\{\theta \cdot t(x)\}$ and a sample $x_1, \cdots, x_n$ from $\mathscr{M}$. Let $\bar{t} = \{t(x_1) + \cdots + t(x_n)\}/n$ and let $\bar{t} = (\bar{t}_1, \bar{t}_2)$ be a partition of the canonical statistic $\bar{t}$.
Barndorff-Nielsen, O., Blæsild, P.
openaire   +2 more sources