Results 41 to 50 of about 63,287 (274)
AxiomsOur study presents an innovative variational Bayesian parameter estimation method for the Quantile Nonlinear Dynamic Latent Variable Model (QNDLVM), particularly when dealing with missing data and nonparametric priors.
Mulati Tuerde, Ahmadjan Muhammadhaji
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IEEE Access, 2021 This work discusses a variational Bayesian learning approach towards decentralized blind deconvolution of seismic signals within a sensor network. Blind seismic deconvolution is cast into a probabilistic framework based on Sparse Bayesian learning ...
Dmitriy Shutin, Ban-Sok Shin
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Variational Bayesian Supertrees
, 2021 Given overlapping subsets of a set of taxa (e.g. species), and posterior distributions on phylogenetic tree topologies for each of these taxon sets, how can we infer a posterior distribution on phylogenetic tree topologies for the entire taxon set? Although the equivalent problem for in the non-Bayesian case has attracted substantial research, the ...
Karcher, Michael +2 more
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Bayesian Nonlinear Support Vector Machines for Big Data
, 2017 We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales ...
Deutsch, Matthaeus +3 more
core +1 more source
Automatic Variational Inference in Stan [PDF]
, 2015 Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes it difficult to automate.
Blei, David M. +3 more
core
Toward Variational Structural Learning of Bayesian Networks
IEEE AccessThis study presents a novel variational framework for structural learning in Bayesian networks (BNs), addressing the key limitation of existing Bayesian methods: their lack of scalability to large graphs with many variables.
Andres R. Masegosa, Manuel Gomez-Olmedo
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Robust Student’s T Distribution Based PHD/CPHD Filter for Multiple Targets Tracking Using Variational Bayesian Approach [PDF]
Radioengineering, 2020 Measurement-outliers caused by non-linear observation model or random disturbance will lead to the accuracy decline of a target tracking filter. This paper proposes a robust probability hypothesis density (PHD) filter to handle the measurement-outlier ...
P. Li, C. Xu, W. Wang, S. Su
doaj
FEBS Open Bio, EarlyView.This protocol paper outlines methods to establish the success of a time‐resolved serial crystallographic experiment, by means of statistical analysis of timepoint data in reciprocal space and models in real space. We show how to amplify the signal from excited states to visualise structural changes in successful experiments.
Jake Hill +4 more
wiley +1 more source
PAC-Bayes Bounds on Variational Tempered Posteriors for Markov Models
Entropy, 2021 Datasets displaying temporal dependencies abound in science and engineering applications, with Markov models representing a simplified and popular view of the temporal dependence structure.
Imon Banerjee +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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