Results 21 to 30 of about 4,943,403 (353)
Mini-batch learning of exponential family finite mixture models [PDF]
Statistics and computing, 2019 Mini-batch algorithms have become increasingly popular due to the requirement for solving optimization problems, based on large-scale data sets. Using an existing online expectation–maximization (EM) algorithm framework, we demonstrate how mini-batch (MB)
H. Nguyen, F. Forbes, G. McLachlan
semanticscholar +1 more source
Extended Divergence on a Foliation by Deformed Probability Simplexes
Entropy, 2022 This study considers a new decomposition of an extended divergence on a foliation by deformed probability simplexes from the information geometry perspective.
Keiko Uohashi
doaj +1 more source
Exponential Family Graph Embeddings [PDF]
AAAI Conference on Artificial Intelligence, 2019 Representing networks in a low dimensional latent space is a crucial task with many interesting applications in graph learning problems, such as link prediction and node classification.
Abdulkadir Çelikkanat +1 more
semanticscholar +1 more source
Entropy, 2021 We generalize the Jensen-Shannon divergence and the Jensen-Shannon diversity index by considering a variational definition with respect to a generic mean, thereby extending the notion of Sibson’s information radius.
Frank Nielsen
doaj +1 more source
Exponential-Family Models of Random Graphs: Inference in Finite, Super and Infinite Population Scenarios [PDF]
Statistical Science, 2017 Exponential-family Random Graph Models (ERGMs) constitute a large statistical framework for modeling sparse and dense random graphs, short- and long-tailed degree distributions, covariates, and a wide range of complex dependencies. Special cases of ERGMs
M. Schweinberger +3 more
semanticscholar +1 more source
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
doaj +2 more sources
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
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
doaj +1 more source
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
core +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