Results 161 to 170 of about 24,126 (306)

Restricted Tweedie stochastic block models

Canadian Journal of Statistics, EarlyView.
Abstract The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of nonnegative zero‐inflated continuous edge weights.
Jie Jian, Mu Zhu, Peijun Sang
wiley   +1 more source

Biology-Informed Matrix Factorization: An AI-Driven Framework for Enhanced Drug Repositioning

Biology
Advances in artificial intelligence (AI) and intelligent computing have significantly accelerated drug discovery by enabling accurate modeling of complex biomedical relationships. Among these efforts, drug repositioning—identifying novel therapeutic uses
Yangyang Wang, Yaping Wang, Ya Hu, Jihan Wang   +3 more
doaj   +1 more source

Hidden Markov graphical models with state‐dependent generalized hyperbolic distributions

Canadian Journal of Statistics, EarlyView.
Abstract In this article, we develop a novel hidden Markov graphical model to investigate time‐varying interconnectedness between different financial markets. To identify conditional correlation structures under varying market conditions and accommodate shape features embedded in financial time series, we rely upon the generalized hyperbolic family of ...
Beatrice Foroni, Luca Merlo, Lea Petrella   +2 more
wiley   +1 more source

Discrete Regularization

, 2006
This chapter presents a systemic framework for learning from a finite set represented as a graph. Discrete analogues are developed here of a number of differential operators, and then a discrete analogue of classical regularization theory is constructed ...
Schölkopf, B., Zhou, D., Zhou Dengyong, Schülkopf Bernhard   +3 more
core   +1 more source

Nonlinear permuted Granger causality

Canadian Journal of Statistics, EarlyView.
Abstract Granger causality is an established, contentious method that seeks causal temporal connections via association and precedence. While not true causal inference, it assists in mapping networks of information flow that may warrant further study.
Noah D. Gade, Jordan Rodu
wiley   +1 more source

On distance-regularity in graphs

Journal of Combinatorial Theory, Series B, 1982
AbstractIf A is the adjacency matrix of a graph G, then Ai is the adjacency matrix of the graph on the same vertex set in which a pair of vertices is adjacent if and only if their distance apart is i in G. If G is distance-regular, then Ai is a polynomial of degree i in A. It is shown that the converse is also true.
openaire   +1 more source

The Benjamin–Ono Equation in the Zero‐Dispersion Limit for Rational Initial Data: Generation of Dispersive Shock Waves

Communications on Pure and Applied Mathematics, EarlyView.
ABSTRACT The leading‐order asymptotic behavior of the solution of the Cauchy initial‐value problem for the Benjamin–Ono equation in L2(R)$L^2(\mathbb {R})$ is obtained explicitly for generic rational initial data u0$u_0$. An explicit asymptotic wave profile uZD(t,x;ε)$u^\mathrm{ZD}(t,x;\epsilon)$ is given, in terms of the branches of the multivalued ...
Elliot Blackstone, Louise Gassot, Patrick Gérard, Peter D. Miller   +3 more
wiley   +1 more source

Sparse Graph Regularization Non-Negative Matrix Factorization Based on Huber Loss Model for Cancer Data Analysis. [PDF]

Front Genet, 2019
Wang CY, Liu JX, Yu N, Zheng CH.
europepmc   +1 more source

The Role of Business R&D in Environmental Sustainability: Evidence From the Nordic Countries

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT As environmental sustainability becomes an increasingly important concern for advanced economies, the role of business innovation in relation to environmental outcomes has attracted increasing attention. This study examines the relationship between business enterprise research and development (BERD) and environmental sustainability in the ...
Abdullah Emre Caglar, Mehmet Ulug, Ismail Demirdag   +2 more
wiley   +1 more source

A Data-Dependent Regularization Method Based on the Graph Laplacian

We investigate a variational method for ill-posed problems, named graphLa+Psi, which embeds a graph Laplacian operator in the regularization term. The novelty of this method lies in constructing the graph Laplacian based on a preliminary approximation of
Aleotti, Stefano, Donatelli, Marco, Evangelista, Davide, Bianchi, Davide, Li, Wenbin, Piccolomini, Elena Loli   +5 more
core   +2 more sources