Results 231 to 240 of about 11,193 (260)

Dynamics-informed priors (DIP) for neural mass modelling. [PDF]

Imaging Neurosci (Camb)
Caccamo A, Dunstan DM, Richardson MP, Shaw AD, Goodfellow M.   +4 more
europepmc   +1 more source

Accurate remaining useful life prediction and uncertainty quantification for wind turbines using temporal convolutional variational deep Gaussian processes. [PDF]

Sci Rep
Cui S, Liu X, Wu G, Qiao W, Liu J, Chen X.   +5 more
europepmc   +1 more source

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Split variational inference

Proceedings of the 26th Annual International Conference on Machine Learning, 2009
We propose a deterministic method to evaluate the integral of a positive function based on soft-binning functions that smoothly cut the integral into smaller integrals that are easier to approximate. In combination with mean-field approximations for each individual sub-part this leads to a tractable algorithm that alternates between the optimization of
BOUCHARD GUILLAUME M, ZOETER ONNO
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Fast copula variational inference

Journal of Experimental & Theoretical Artificial Intelligence, 2021
Mean-field variational inference, built on fully factorisations, can be efficiently solved; however, it ignores the dependencies between latent variables, resulting in lower performance.
Jinjin Chi, Jihong Ouyang, Ang Zhang, Xinhua Wang, Ximing Li 0002   +4 more
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Metric Gaussian variational inference

2020
One main result of this dissertation is the development of Metric Gaussian Variational Inference (MGVI), a method to perform approximate inference in extremely high dimensions and for complex probabilistic models. The problem with high-dimensional and complex models is twofold. Fist, to capture the true posterior distribution accurately, a sufficiently
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The variational approximation for Bayesian inference

IEEE Signal Processing Magazine, 2008
The influence of this Thomas Bayes' work was immense. It was from here that "Bayesian" ideas first spread through the mathematical world, as Bayes's own article was ignored until 1780 and played no important role in scientific debate until the 20th century.
Dimitris G. Tzikas, Aristidis C. Likas, Nikolas P. Galatsanos   +2 more
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Inferences on the common coefficient of variation

Statistics in Medicine, 2005
The coefficient of variation is often used as a measure of precision and reproducibility of data in medical and biological science. This paper considers the problem of making inference about the common population coefficient of variation when it is a priori suspected that several independent samples are from populations with a common coefficient of ...
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Seismic Tomography Using Variational Inference Methods

Journal of Geophysical Research: Solid Earth, 2020
Xin Zhang, Andrew Curtis
exaly  

Gibbs sampler and coordinate ascent variational inference: A set-theoretical review

Communications in Statistics - Theory and Methods, 2022
Se Yoon Lee
exaly