Results 261 to 270 of about 32,855 (298)

Variance Matrix Priors for Dirichlet Process Mixture Models With Gaussian Kernels

International Statistical Review, EarlyView.
Summary Bayesian mixture modelling is widely used for density estimation and clustering. The Dirichlet process mixture model (DPMM) is the most popular Bayesian non‐parametric mixture modelling approach. In this manuscript, we study the choice of prior for the variance or precision matrix when Gaussian kernels are adopted.
Wei Jing, Michail Papathomas, Silvia Liverani   +2 more
wiley   +1 more source

Analyzing Semantic Properties of Word Embeddings Using Eigenvectors

Bassma Abdlrazg, Hanan Atetalla
openalex   +2 more sources

Unveiling hidden connections: How social networks impact diversion in hospital emergency departments: An exploratory social network analysis. [PDF]

PLoS One
Francis T, de Vries M, Fan M, Yousefi-Nooraie R, Ouimet M, Rac VE, Trbovich P.   +6 more
europepmc   +1 more source

Network analysis using Krylov subspace trajectories. [PDF]

Complex Netw Appl XIII (2024)
Robert Frost H.
europepmc   +1 more source

Discrete mathematical network analysis bridging clinical vocabulary and patient discourse in interstitial cystitis/bladder pain syndrome online communications. [PDF]

Sci Rep
Okui N, Ichino K, Sakuma Y, Ikehata Y, Okui M, Horie S.   +5 more
europepmc   +1 more source

Dangling centrality highlights critical nodes by evaluating network stability through link removal. [PDF]

Sci Rep
Fatima U, Hina S, Wasif M.
europepmc   +1 more source

CENTRA: knowledge-based gene contextuality graphs reveal functional master regulators by centrality and fractality. [PDF]

NAR Genom Bioinform
Hause F, Wedler A, Keil R, Schian L, Glaß M, Grimm WF, Sorokin O, Hoehenwarter W, Sinz A, Hüttelmaier S.   +9 more
europepmc   +1 more source

Correlation Analysis Study Between the Intensity of Counseling Friendship Network and Exercise Time Among University Taekwondo Athletics. [PDF]

Brain Behav
Son SM, Ahn BO.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

On Eigenvector Bounds

BIT Numerical Mathematics, 2003
The authors investigate the conditions under which it is possible to estimate and compute error bounds on a computed eigenvector of a finite matrix. It is shown that nontrivial error bounds on an eigenvector are computable if and only if its geometric multiplicity is one. They also provide an algorithm for the computation of these error bounds and show
Rump, Siegfried M., Zemke, Jens-Peter M.
openaire   +1 more source

Extended eigenvalue–eigenvector method

Statistics & Probability Letters, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kataria, K. K., Khandakar, M.
openaire   +2 more sources