Results 11 to 20 of about 634,911 (278)
PLoS ONE, 2021 In this work, a new family of distributions, which extends the Beta transmuted family, was obtained, called the Modified Beta Transmuted Family of distribution.
Phillip Oluwatobi Awodutire +3 more
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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 +2 more
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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
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Information Geometry Approach to Parameter Estimation in Markov Chains [PDF]
, 2015 We consider the parameter estimation of Markov chain when the unknown transition matrix belongs to an exponential family of transition matrices. Then, we show that the sample mean of the generator of the exponential family is an asymptotically efficient ...
Hayashi, Masahito, Watanabe, Shun
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$q$-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.
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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
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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
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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
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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.
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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
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