Variational Bayesian inference for the multiple-choice DINA model
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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
Sign Up to like & getrecommendations! 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
Covariance-Free Variational Bayesian Learning for Correlated Block Sparse Signals
Sign Up to like & getrecommendations! 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
Variational Bayesian Gaussian Mixture Regression for Soft Sensing Key Variables in Non-Gaussian Industrial Processes
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Control Systems Technology"
DOI: 10.1109/tcst.2016.2576999
Abstract: In this brief, a variational Bayesian Gaussian mixture regression (VBGMR) method is developed for soft sensing key quality-related variables in a non-Gaussian industrial process. Traditional Gaussian mixture regression (GMR) is based on Gaussian mixture model… read more here.
Keywords: variational bayesian; mixture regression; gaussian mixture; mixture ... See more keywords
Variational Bayesian Orthogonal Nonnegative Matrix Factorization Over the Stiefel Manifold
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2022.3194701
Abstract: Nonnegative matrix factorization (NMF) is one of the best-known multivariate data analysis techniques. The NMF uniqueness and its rank selection are two major open problems in this field. The solutions uniqueness issue can be addressed… read more here.
Keywords: stiefel manifold; matrix factorization; nonnegative matrix; model ... See more keywords
Reconstruction-Aware Imaging System Ranking by Use of a Sparsity-Driven Numerical Observer Enabled by Variational Bayesian Inference
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Medical Imaging"
DOI: 10.1109/tmi.2018.2880870
Abstract: It is widely accepted that optimization of imaging system performance should be guided by task-based measures of image quality. It has been advocated that imaging hardware or data-acquisition designs should be optimized by use of… read more here.
Keywords: reconstruction; imaging system; sparsity driven; variational bayesian ... See more keywords