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   Articles with "variational bayesian" as a keyword



Variational Bayesian inference for the multiple-choice DINA model

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recommendations! Published in 2020 at "Behaviormetrika"

DOI: 10.1007/s41237-020-00104-w

Abstract: In this study, a fully Bayesian formulation has been proposed for the multiple-choice item version of the deterministic input noisy “AND” gate (MC-DINA) model, which represents a cognitive diagnostic model for extracting information from multiple-choice… read more here.

Keywords: variational bayesian; dina model; choice; multiple choice ... See more keywords

A Variational Bayesian Based Robust Cubature Kalman Filter under dynamic model mismatch and outliers interference

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recommendations! Published in 2021 at "Measurement"

DOI: 10.1016/j.measurement.2021.110063

Abstract: Abstract This paper proposes a variational Bayesian-based cubature Kalman filter to solve the state estimation problem of nonlinear discrete-time systems under dynamic model mismatch and outliers interference. In the proposed filter, the measurement noise is… read more here.

Keywords: variational bayesian; measurement noise; filter; dynamic model ... See more keywords

Nonlinear point-process estimation of neural spiking activity based on variational Bayesian inference

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recommendations! Published in 2022 at "Journal of Neural Engineering"

DOI: 10.1088/1741-2552/ac88a0

Abstract: Objective. Understanding neural encoding and decoding processes are crucial to the development of brain-machine interfaces (BMI). Higher decoding speed of neural signals is required for the large-scale neural data and the extremely low detection delay… read more here.

Keywords: based variational; nonlinear point; point process; bayesian inference ... See more keywords

An R package VIGoR for joint estimation of multiple linear learners with variational Bayesian inference

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recommendations! Published in 2022 at "Bioinformatics"

DOI: 10.1093/bioinformatics/btac328

Abstract: Abstract Summary An R package that can implement multiple linear learners, including penalized regression and regression with spike and slab priors, in a single model has been developed. Solutions are obtained with fast minorize-maximization algorithms… read more here.

Keywords: linear learners; bayesian inference; multiple linear; package ... See more keywords

Variational Bayesian Inference-Based Multiple Target Localization in WSNs With Quantized Received Signal Strength

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recommendations! Published in 2019 at "IEEE Access"

DOI: 10.1109/access.2019.2915657

Abstract: The received signal strength (RSS)-based target localization is an important field of research with numerous applications in wireless sensor networks. By exploiting the sparsity of localization, the compressive sensing (CS) can be applied to develop… read more here.

Keywords: target localization; signal strength; target; received signal ... See more keywords

Variational Bayesian Group-Level Sparsification for Knowledge Distillation

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recommendations! Published in 2020 at "IEEE Access"

DOI: 10.1109/access.2020.3008854

Abstract: Deep neural networks are capable of learning powerful representation, but often limited by heavy network architectures and high computational cost. Knowledge distillation (KD) is one of the effective ways to perform model compression and inference… read more here.

Keywords: variational bayesian; knowledge distillation; group level; bayesian group ... See more keywords

An Over-Sampling Amplitude-Limited Variational Bayesian Method for the Identification of Hammerstein Model

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recommendations! Published in 2020 at "IEEE Access"

DOI: 10.1109/access.2020.3044272

Abstract: Chemical industrial processes involve numerous multivariable nonlinear systems. Nonlinear Muli-Input Muli-Output (MIMO) models seem more suitable to represent most systems and control problems in industrial processes. Furthermore, the outputs of the real systems might be… read more here.

Keywords: hammerstein model; model; variational bayesian; bayesian method ... See more keywords

Robust Sequential Variational Bayesian Filter for Tightly Coupled Navigation System Within GNSS Challenged Environment

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recommendations! Published in 2025 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2025.3607480

Abstract: Navigation systems are commonly used in Internet of Things (IoT) devices to provide navigation information. For achieving accurate information on devices, this article describes a robust variational Bayesian filter with sequential processing to estimate the… read more here.

Keywords: filter; system; navigation; gnss challenged ... See more keywords

Laser FM Noise Compensation in Fiber-THz Convergence Systems Employing Robust Variational Bayesian Unscented Kalman Filter

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recommendations! Published in 2025 at "Journal of Lightwave Technology"

DOI: 10.1109/jlt.2025.3600402

Abstract: The convergence of fiber and terahertz (THz) wireless communication has emerged as a promising solution for ultra-high-speed data transmission in future 6G networks. However, phase noise (PN) arising from the intrinsic linewidth and phase fluctuations… read more here.

Keywords: variational bayesian; kalman filter; unscented kalman; laser noise ... See more keywords

A Stochastic Particle Variational Bayesian Inference Inspired Deep-Unfolding Network for Sensing Over Wireless Networks

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recommendations! Published in 2024 at "IEEE Journal on Selected Areas in Communications"

DOI: 10.1109/jsac.2024.3414626

Abstract: Future wireless networks are envisioned to provide ubiquitous sensing services, driving a substantial demand for multi-dimensional non-convex parameter estimation. This entails dealing with non-convex likelihood functions containing numerous local optima. Variational Bayesian inference (VBI) provides… read more here.

Keywords: bayesian inference; deep unfolding; wireless networks; particle ... See more keywords

Covariance-Free Variational Bayesian Learning for Correlated Block Sparse Signals

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recommendations! Published in 2023 at "IEEE Communications Letters"

DOI: 10.1109/lcomm.2023.3241316

Abstract: We consider the problem of estimating channel in massive machine type communication (mMTC) systems. The sparse device activity in a mMTC system makes the channel block-sparse, with intra-block correlation. Block-sparse Bayesian learning (B-SBL) is a… read more here.

Keywords: block sparse; variational bayesian; covariance free; block ... See more keywords