Results 11 to 20 of about 43,983 (273)
Variational Inference for Bayesian Bridge Regression
Statistics and Computing, 2022 Abstract The bridge approach for regularization of coefficients in regression models uses ℓα norm, with α ∈ (0, +∞), to define a penalization on large values of the regression coefficients. Particular cases include the lasso and ridge penalizations.
Carlos Tadeu Pagani Zanini +2 more
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Variational Bayesian Inference for Crowdsourcing Predictions [PDF]
2020 59th IEEE Conference on Decision and Control (CDC), 2020 Crowdsourcing has emerged as an effective means for performing a number of machine learning tasks such as annotation and labelling of images and other data sets. In most early settings of crowdsourcing, the task involved classification, that is assigning one of a discrete set of labels to each task.
Desmond Cai +3 more
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Variational Bayesian Inference for Pairwise Markov Models [PDF]
2021 IEEE Statistical Signal Processing Workshop (SSP), 2021 Generative models based on latent random variables are a popular tool for time series forecasting. Generative models include the Hidden Markov Model, the Recurrent Neural Network and the Stochastic Recurrent Neural Network. In this paper, we exploit the Pairwise Markov Models, a generalization of Hidden Markov models, as generative models.
Katherine Morales, Yohan Petetin
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Variational Bayesian Inference Techniques [PDF]
IEEE Signal Processing Magazine, 2010 Milestones in sparse signal reconstruction and compressive sensing can be understood in a probabilistic Bayesian context, fusing underdetermined measurements with knowledge about low-level signal properties in the posterior distribution, which is maximized for point estimation. We review recent progress to advance beyond this setting.
Matthias W. Seeger, David P. Wipf
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A Geometric Variational Approach to Bayesian Inference [PDF]
Journal of the American Statistical Association, 2019 We propose a novel Riemannian geometric framework for variational inference in Bayesian models based on the nonparametric Fisher–Rao metric on the manifold of probability density functions. Under the square-root density representation, the manifold can be identified with the positive orthant of the unit hypersphereS∞inL2, and the Fisher–Rao metric ...
Saha, Abhijoy +2 more
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A Variational Approach to Bayesian Phylogenetic Inference
CoRR, 2022 Bayesian phylogenetic inference is currently done via Markov chain Monte Carlo (MCMC) with simple proposal mechanisms. This hinders exploration efficiency and often requires long runs to deliver accurate posterior estimates. In this paper, we present an alternative approach: a variational framework for Bayesian phylogenetic analysis.
Cheng Zhang, Frederick A. Matsen IV
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Gradient Regularization as Approximate Variational Inference
Entropy, 2021 We developed Variational Laplace for Bayesian neural networks (BNNs), which exploits a local approximation of the curvature of the likelihood to estimate the ELBO without the need for stochastic sampling of the neural-network weights.
Ali Unlu, Laurence Aitchison
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Variational Bayesian inference for network autoregression models [PDF]
Computational Statistics & Data Analysis, 2022 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wei-Ting Lai +3 more
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Adaptive Non-Rigid Point Set Registration Based on Variational Bayesian [PDF]
Xibei Gongye Daxue Xuebao, 2018 For the existence of outliers in non-rigid point set registration, a method based on Bayesian student's t mixture model(SMM) is proposed. Under the framework of variational Bayesian, the point set registration problem is converted to maximize the ...
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Variational Bayesian Inference of Line Spectra [PDF]
IEEE Transactions on Signal Processing, 2017 15 pages, 8 figures, accepted for publication in IEEE Transactions on Signal ...
Mihai-Alin Badiu +2 more
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