Results 31 to 40 of about 3,859,005 (290)
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
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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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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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Exponential-family random graph models for valued networks. [PDF]
Electron J Stat, 2012 Exponential-family random graph models (ERGMs) provide a principled and flexible way to model and simulate features common in social networks, such as propensities for homophily, mutuality, and friend-of-a- friend triad closure, through choice of model ...
Krivitsky PN.
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CoRR, 2016 Word embeddings are a powerful approach for capturing semantic similarity among terms in a vocabulary. In this paper, we develop exponential family embeddings, a class of methods that extends the idea of word embeddings to other types of high-dimensional data.
Maja Rudolph +3 more
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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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Leaf count overdispersion in coffee seedlings
Ciência Rural, 2019 : Coffee crops play an important role in Brazilian agriculture, with a high level of social and economic participation resulting from the jobs created in the supply chain and from the income obtained by producers and the revenue generated for the ...
Edilson Marcelino Silva +4 more
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Exponential families of mixed Poisson distributions [PDF]
, 2006 If I=(I1,…,Id) is a random variable on [0,∞)d with distribution μ(dλ1,…,dλd), the mixed Poisson distribution MP(μ) on View the MathML source is the distribution of (N1(I1),…,Nd(Id)) where N1,…,Nd are ordinary independent Poisson processes which are also ...
Letac, G. +9 more
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On the Jensen–Shannon Symmetrization of Distances Relying on Abstract Means
Entropy, 2019 The Jensen–Shannon divergence is a renowned bounded symmetrization of the unbounded Kullback–Leibler divergence which measures the total Kullback–Leibler divergence to the average mixture distribution.
Frank Nielsen
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