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Journal articles on the topic 'Variational bayes methods'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

Lee, Jangwon, Dongu Han, Jichan Park, and Taeryon Choi. "Variational Bayes methods for Bayesian quantile stochastic frontier models." Journal of the Korean Data And Information Science Society 35, no. 2 (2024): 239–57. http://dx.doi.org/10.7465/jkdi.2024.35.2.239.

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Hussein, Noor Hassan, and Gorgees Shaheed Mohammad. "Variational Bayes analysis of the normal-gamma-exponential prior." Journal of Interdisciplinary Mathematics 28, no. 1 (2025): 1–8. https://doi.org/10.47974/jim-1766.

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The Variational Bayes (VB) method for the Normal-Gamma-Exponential (NGE) prior is derived. The relation between the VB method and the regular Gibbs sampler is shown. The factorized approximation tool is used to expand the VB method to the Mean Field Variational Bayes (MFVB) method. The predication ability and theoretical properties of this method with the NGE prior are demonstrated using simulated data and its result is compared with other methods showing that this method preforms reasonably well.

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Park, Mijung, James Foulds, Kamalika Chaudhuri, and Max Welling. "Variational Bayes In Private Settings (VIPS)." Journal of Artificial Intelligence Research 68 (May 5, 2020): 109–57. http://dx.doi.org/10.1613/jair.1.11763.

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Many applications of Bayesian data analysis involve sensitive information such as personal documents or medical records, motivating methods which ensure that privacy is protected. We introduce a general privacy-preserving framework for Variational Bayes (VB), a widely used optimization-based Bayesian inference method. Our framework respects differential privacy, the gold-standard privacy criterion, and encompasses a large class of probabilistic models, called the Conjugate Exponential (CE) family. We observe that we can straightforwardly privatise VB’s approximate posterior distributions for m

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Kang, Hyohyeong, and Seungjin Choi. "Probabilistic Models for Common Spatial Patterns: Parameter-Expanded EM and Variational Bayes." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 970–76. http://dx.doi.org/10.1609/aaai.v26i1.8277.

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Common spatial patterns (CSP) is a popular feature extraction method for discriminating between positive andnegative classes in electroencephalography (EEG) data.Two probabilistic models for CSP were recently developed: probabilistic CSP (PCSP), which is trained by expectation maximization (EM), and variational BayesianCSP (VBCSP) which is learned by variational approx-imation. Parameter expansion methods use auxiliaryparameters to speed up the convergence of EM or thedeterministic approximation of the target distributionin variational inference. In this paper, we describethe development of pa

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Li, Kun, Jinyang Luo, Peng Li, Guisheng Liao, Zhixiang Huang, and Lixia Yang. "Improved Variational Bayes for Space-Time Adaptive Processing." Entropy 27, no. 3 (2025): 242. https://doi.org/10.3390/e27030242.

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To tackle the challenge of enhancing moving target detection performance in environments characterized by small sample sizes and non-uniformity, methods rooted in sparse signal reconstruction have been incorporated into Space-Time Adaptive Processing (STAP) algorithms. Given the prominent sparse nature of clutter spectra in the angle-Doppler domain, adopting sparse recovery algorithms has proven to be a feasible approach for accurately estimating high-resolution spatio-temporal two-dimensional clutter spectra. Sparse Bayesian Learning (SBL) is a pivotal tool in sparse signal reconstruction and

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Kaji, Daisuke, and Sumio Watanabe. "Two design methods of hyperparameters in variational Bayes learning for Bernoulli mixtures." Neurocomputing 74, no. 11 (2011): 2002–7. http://dx.doi.org/10.1016/j.neucom.2010.06.027.

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Nakajima, Shinichi, and Sumio Watanabe. "Variational Bayes Solution of Linear Neural Networks and Its Generalization Performance." Neural Computation 19, no. 4 (2007): 1112–53. http://dx.doi.org/10.1162/neco.2007.19.4.1112.

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It is well known that in unidentifiable models, the Bayes estimation provides much better generalization performance than the maximum likelihood (ML) estimation. However, its accurate approximation by Markov chain Monte Carlo methods requires huge computational costs. As an alternative, a tractable approximation method, called the variational Bayes (VB) approach, has recently been proposed and has been attracting attention. Its advantage over the expectation maximization (EM) algorithm, often used for realizing the ML estimation, has been experimentally shown in many applications; nevertheless

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MA, ZHANYU, and ANDREW E. TESCHENDORFF. "A VARIATIONAL BAYES BETA MIXTURE MODEL FOR FEATURE SELECTION IN DNA METHYLATION STUDIES." Journal of Bioinformatics and Computational Biology 11, no. 04 (2013): 1350005. http://dx.doi.org/10.1142/s0219720013500054.

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An increasing number of studies are using beadarrays to measure DNA methylation on a genome-wide basis. The purpose is to identify novel biomarkers in a wide range of complex genetic diseases including cancer. A common difficulty encountered in these studies is distinguishing true biomarkers from false positives. While statistical methods aimed at improving the feature selection step have been developed for gene expression, relatively few methods have been adapted to DNA methylation data, which is naturally beta-distributed. Here we explore and propose an innovative application of a recently d

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Yuan, Ke, Mark Girolami, and Mahesan Niranjan. "Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations." Neural Computation 24, no. 6 (2012): 1462–86. http://dx.doi.org/10.1162/neco_a_00281.

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This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations. We quantified the efficiencies of these MCMC methods on synthetic data, and our results suggest that the Reimannian manifold Hamiltonian Monte Carlo method offers the best performance. We further compared such a method with a previously tested variational Bayes method on two experimental data sets. Results indicate similar performance on the large data sets and superior performance on small ones. The wo

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Ulitzsch, Esther, and Steffen Nestler. "Evaluating Stan’s Variational Bayes Algorithm for Estimating Multidimensional IRT Models." Psych 4, no. 1 (2022): 73–88. http://dx.doi.org/10.3390/psych4010007.

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Bayesian estimation of multidimensional item response theory (IRT) models in large data sets may come with impractical computational burdens when general-purpose Markov chain Monte Carlo (MCMC) samplers are employed. Variational Bayes (VB)—a method for approximating the posterior distribution—poses a potential remedy. Stan’s general-purpose VB algorithms have drastically improved the accessibility of VB methods for a wide psychometric audience. Using marginal maximum likelihood (MML) and MCMC as benchmarks, the present simulation study investigates the utility of Stan’s built-in VB function fo

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Du, Libin, Huming Li, Lei Wang, Xu Lin, and Zhichao Lv. "Research on High Robustness Underwater Target Estimation Method Based on Variational Sparse Bayesian Inference." Remote Sensing 15, no. 13 (2023): 3222. http://dx.doi.org/10.3390/rs15133222.

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Pulse noise (such as glacier fracturing and offshore pile driving), commonly seen in the marine environment, seriously affects the performance of Direction-of-Arrival (DOA) estimation methods in sonar systems. To address this issue, this paper proposes a high robustness underwater target estimation method based on variational sparse Bayesian inference by studying and analyzing the sparse prior assumption characteristics of signals. This method models pulse noise to build an observation signal, completes the derivation of the conditional distribution of the observed variables and the prior dist

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Svensson, Valentine, Adam Gayoso, Nir Yosef, and Lior Pachter. "Interpretable factor models of single-cell RNA-seq via variational autoencoders." Bioinformatics 36, no. 11 (2020): 3418–21. http://dx.doi.org/10.1093/bioinformatics/btaa169.

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Abstract Motivation Single-cell RNA-seq makes possible the investigation of variability in gene expression among cells, and dependence of variation on cell type. Statistical inference methods for such analyses must be scalable, and ideally interpretable. Results We present an approach based on a modification of a recently published highly scalable variational autoencoder framework that provides interpretability without sacrificing much accuracy. We demonstrate that our approach enables identification of gene programs in massive datasets. Our strategy, namely the learning of factor models with

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Li, Xinhai, Chenxu Meng, Heng Zhou, et al. "Generative Learning from Semantically Confused Label Distribution via Auto-Encoding Variational Bayes." Electronics 14, no. 13 (2025): 2736. https://doi.org/10.3390/electronics14132736.

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Label Distribution Learning (LDL) has emerged as a powerful paradigm for addressing label ambiguity, offering a more nuanced quantification of the instance–label relationship compared to traditional single-label and multi-label learning approaches. This paper focuses on the challenge of noisy label distributions, which is ubiquitous in real-world applications due to the annotator subjectivity, algorithmic biases, and experimental errors. Existing related LDL algorithms often assume a linear combination of true and random label distributions when modeling the noisy label distributions, an overs

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Shapovalova, Yuliya. "“Exact” and Approximate Methods for Bayesian Inference: Stochastic Volatility Case Study." Entropy 23, no. 4 (2021): 466. http://dx.doi.org/10.3390/e23040466.

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We conduct a case study in which we empirically illustrate the performance of different classes of Bayesian inference methods to estimate stochastic volatility models. In particular, we consider how different particle filtering methods affect the variance of the estimated likelihood. We review and compare particle Markov Chain Monte Carlo (MCMC), RMHMC, fixed-form variational Bayes, and integrated nested Laplace approximation to estimate the posterior distribution of the parameters. Additionally, we conduct the review from the point of view of whether these methods are (1) easily adaptable to

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Sridhar, Dhanya, Hal Daumé, and David Blei. "Heterogeneous Supervised Topic Models." Transactions of the Association for Computational Linguistics 10 (2022): 732–45. http://dx.doi.org/10.1162/tacl_a_00487.

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Abstract Researchers in the social sciences are often interested in the relationship between text and an outcome of interest, where the goal is to both uncover latent patterns in the text and predict outcomes for unseen texts. To this end, this paper develops the heterogeneous supervised topic model (HSTM), a probabilistic approach to text analysis and prediction. HSTMs posit a joint model of text and outcomes to find heterogeneous patterns that help with both text analysis and prediction. The main benefit of HSTMs is that they capture heterogeneity in the relationship between text and the out

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Lee, Juhyun, and Sangsung Park. "A Study on the Calibrated Confidence of Text Classification Using a Variational Bayes." Applied Sciences 12, no. 18 (2022): 9007. http://dx.doi.org/10.3390/app12189007.

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Recently, predictions based on big data have become more successful. In fact, research using images or text can make a long-imagined future come true. However, the data often contain a lot of noise, or the model does not account for the data, which increases uncertainty. Moreover, the gap between accuracy and likelihood is widening in modern predictive models. This gap may increase the uncertainty of predictions. In particular, applications such as self-driving cars and healthcare have problems that can be directly threatened by these uncertainties. Previous studies have proposed methods for r

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Qian, Ke. "Innovative Applications of Machine Learning in Image Recognition." Journal of Computer Technology and Applied Mathematics 2, no. 1 (2025): 15–20. https://doi.org/10.70393/6a6374616d.323533.

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Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference

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Yao, Yusi. "Applications of Bayesian Inference in Financial Econometrics: A Review." Economics and Management Innovation 2, no. 2 (2025): 45–52. https://doi.org/10.71222/mz71ts21.

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This review provides a comprehensive review of the applications of Bayesian inference in financial econometrics. It explores fundamental Bayesian methods, such as Bayes' Theorem, Markov Chain Monte Carlo (MCMC), and Variational Inference, and discusses their use in financial modeling, including asset pricing, risk management, and portfolio optimization. The paper also highlights recent advancements such as Hamiltonian Monte Carlo and Bayesian Neural Networks, which have enhanced the computational efficiency of Bayesian techniques. Despite these advancements, challenges related to computational

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Yao, Yusi. "Applications of Bayesian Inference in Financial Econometrics: A Review." Economics and Management Innovation 2, no. 2 (2025): 29–36. https://doi.org/10.71222/pf0sg388.

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This review provides a comprehensive review of the applications of Bayesian inference in financial econometrics. It explores fundamental Bayesian methods, such as Bayes' Theorem, Markov Chain Monte Carlo (MCMC), and Variational Inference, and discusses their use in financial modeling, including asset pricing, risk management, and portfolio optimization. The paper also highlights recent advancements such as Hamiltonian Monte Carlo and Bayesian Neural Networks, which have enhanced the computational efficiency of Bayesian techniques. Despite these advancements, challenges related to computational

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Bates, Oscar, Lluis Guasch, George Strong, et al. "A probabilistic approach to tomography and adjoint state methods, with an application to full waveform inversion in medical ultrasound." Inverse Problems 38, no. 4 (2022): 045008. http://dx.doi.org/10.1088/1361-6420/ac55ee.

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Abstract Bayesian methods are a popular research direction for inverse problems. There are a variety of techniques available to solve Bayes’ equation, each with their own strengths and limitations. Here, we discuss stochastic variational inference (SVI), which solves Bayes’ equation using gradient-based methods. This is important for applications which are time-limited (e.g. medical tomography) or where solving the forward problem is expensive (e.g. adjoint methods). To evaluate the use of SVI in both these contexts, we apply it to ultrasound tomography of the brain using full-waveform inversi

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Goldsmith, Jeff, and Joseph E. Schwartz. "Variable selection in the functional linear concurrent model." Statistics in Medicine 36, no. 14 (2017): 2237–50. http://dx.doi.org/10.1002/sim.7254.

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We propose methods for variable selection in the context of modeling the association between a functional response and concurrently observed functional predictors. This data structure, and the need for such methods, is exemplified by our motivating example: a study in which blood pressure values are observed throughout the day, together with measurements of physical activity, location, posture, affect or mood, and other quantities that may influence blood pressure. We estimate the coefficients of the concurrent functional linear model using variational Bayes and jointly model residual correlat

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Bresson, Georges, Anoop Chaturvedi, Mohammad Arshad Rahman, and Shalabh. "Seemingly unrelated regression with measurement error: estimation via Markov Chain Monte Carlo and mean field variational Bayes approximation." International Journal of Biostatistics 17, no. 1 (2020): 75–97. http://dx.doi.org/10.1515/ijb-2019-0120.

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Abstract Linear regression with measurement error in the covariates is a heavily studied topic, however, the statistics/econometrics literature is almost silent to estimating a multi-equation model with measurement error. This paper considers a seemingly unrelated regression model with measurement error in the covariates and introduces two novel estimation methods: a pure Bayesian algorithm (based on Markov chain Monte Carlo techniques) and its mean field variational Bayes (MFVB) approximation. The MFVB method has the added advantage of being computationally fast and can handle big data. An is

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Zhao, Yuexuan, and Jing Huang. "Dirichlet Process Prior for Student’s t Graph Variational Autoencoders." Future Internet 13, no. 3 (2021): 75. http://dx.doi.org/10.3390/fi13030075.

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Graph variational auto-encoder (GVAE) is a model that combines neural networks and Bayes methods, capable of deeper exploring the influential latent features of graph reconstruction. However, several pieces of research based on GVAE employ a plain prior distribution for latent variables, for instance, standard normal distribution (N(0,1)). Although this kind of simple distribution has the advantage of convenient calculation, it will also make latent variables contain relatively little helpful information. The lack of adequate expression of nodes will inevitably affect the process of generating

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Akkari, Nissrine, Fabien Casenave, Thomas Daniel, and David Ryckelynck. "Data-Targeted Prior Distribution for Variational AutoEncoder." Fluids 6, no. 10 (2021): 343. http://dx.doi.org/10.3390/fluids6100343.

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Bayesian methods were studied in this paper using deep neural networks. We are interested in variational autoencoders, where an encoder approaches the true posterior and the decoder approaches the direct probability. Specifically, we applied these autoencoders for unsteady and compressible fluid flows in aircraft engines. We used inferential methods to compute a sharp approximation of the posterior probability of these parameters with the transient dynamics of the training velocity fields and to generate plausible velocity fields. An important application is the initialization of transient num

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Takiyama, Ken, and Masato Okada. "Detection of Hidden Structures in Nonstationary Spike Trains." Neural Computation 23, no. 5 (2011): 1205–33. http://dx.doi.org/10.1162/neco_a_00109.

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We propose an algorithm for simultaneously estimating state transitions among neural states and nonstationary firing rates using a switching state-space model (SSSM). This algorithm enables us to detect state transitions on the basis of not only discontinuous changes in mean firing rates but also discontinuous changes in the temporal profiles of firing rates (e.g., temporal correlation). We construct estimation and learning algorithms for a nongaussian SSSM, whose nongaussian property is caused by binary spike events. Local variational methods can transform the binary observation process into

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Tichý, Ondřej, and Václav Smídl. "Estimation of input function from dynamic PET brain data using Bayesian blind source separation." Computer Science and Information Systems 12, no. 4 (2015): 1273–87. http://dx.doi.org/10.2298/csis141201051t.

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Selection of regions of interest in an image sequence is a typical prerequisite step for estimation of time-activity curves in dynamic positron emission tomography (PET). This procedure is done manually by a human operator and therefore suffers from subjective errors. Another such problem is to estimate the input function. It can be measured from arterial blood or it can be searched for a vascular structure on the images which is hard to be done, unreliable, and often impossible. In this study, we focus on blind source separation methods with no needs of manual interaction. Recently, we develo

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Song, Ruibo. "Research on Bayesian Method and Its Application." Theoretical and Natural Science 92, no. 1 (2025): 54–59. https://doi.org/10.54254/2753-8818/2025.21402.

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Bayesian methods, rooted in Bayes' theorem, offer a robust framework for statistical inference and decision-making by integrating prior knowledge with new evidence. This paper examines the theoretical foundations, learning paradigms, and applications of Bayesian methods in fields such as data mining, credit risk assessment, and actuarial science. Parametric and theoretical learning are highlighted as essential methodologies that enhance Bayesian inferences adaptability and reliability in complex environments. The study highlights recent progress, particularly enhancements in Bayesian network a

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Wang, Yu, Ke Fu, Hao Chen, Quan Liu, Jian Huang, and Zhongjie Zhang. "Efficiently Detecting Non-Stationary Opponents: A Bayesian Policy Reuse Approach under Partial Observability." Applied Sciences 12, no. 14 (2022): 6953. http://dx.doi.org/10.3390/app12146953.

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In multi-agent domains, dealing with non-stationary opponents that change behaviors (policies) consistently over time is still a challenging problem, where an agent usually requires the ability to detect the opponent’s policy accurately and adopt the optimal response policy accordingly. Previous works commonly assume that the opponent’s observations and actions during online interactions are known, which can significantly limit their applications, especially in partially observable environments. This paper focuses on efficient policy detecting and reusing techniques against non-stationary oppo

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Milosevic, Sara, Philipp Frank, Reimar H. Leike, Ancla Müller, and Torsten A. Enßlin. "Bayesian decomposition of the Galactic multi-frequency sky using probabilistic autoencoders." Astronomy & Astrophysics 650 (June 2021): A100. http://dx.doi.org/10.1051/0004-6361/202039435.

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Context. All-sky observations show both Galactic and non-Galactic diffuse emission, for example from interstellar matter or the cosmic microwave background (CMB). The decomposition of the emission into different underlying radiative components is an important signal reconstruction problem. Aims. We aim to reconstruct radiative all-sky components using spectral data, without incorporating knowledge about physical or spatial correlations. Methods. We built a self-instructing algorithm based on variational autoencoders following three steps: (1)We stated a forward model describing how the data se

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Huang, Jiaxin, Qi Wu, Yazhou Ren, et al. "Sparse Bayesian Deep Learning for Cross Domain Medical Image Reconstruction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (2024): 2339–47. http://dx.doi.org/10.1609/aaai.v38i3.28008.

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Cross domain medical image reconstruction aims to address the issue that deep learning models trained solely on one source dataset might not generalize effectively to unseen target datasets from different hospitals. Some recent methods achieve satisfactory reconstruction performance, but often at the expense of extensive parameters and time consumption. To strike a balance between cross domain image reconstruction quality and model computational efficiency, we propose a lightweight sparse Bayesian deep learning method. Notably, we apply a fixed-form variational Bayes (FFVB) approach to quantif

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Deleforge, Antoine, Florence Forbes, and Radu Horaud. "Acoustic Space Learning for Sound-Source Separation and Localization on Binaural Manifolds." International Journal of Neural Systems 25, no. 01 (2015): 1440003. http://dx.doi.org/10.1142/s0129065714400036.

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In this paper, we address the problems of modeling the acoustic space generated by a full-spectrum sound source and using the learned model for the localization and separation of multiple sources that simultaneously emit sparse-spectrum sounds. We lay theoretical and methodological grounds in order to introduce the binaural manifold paradigm. We perform an in-depth study of the latent low-dimensional structure of the high-dimensional interaural spectral data, based on a corpus recorded with a human-like audiomotor robot head. A nonlinear dimensionality reduction technique is used to show that

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Yu, Dacheng, Mingjun Zhang, Feng Yao, and Jitao Li. "Weak Fault Feature Extraction and Enhancement of Autonomous Underwater Vehicle Thrusters Based on Artificial Rabbits Optimization and Variational Mode Decomposition." Journal of Marine Science and Engineering 12, no. 3 (2024): 455. http://dx.doi.org/10.3390/jmse12030455.

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Variational Mode Decomposition (VMD) has typically been used in weak fault feature extraction in recent years. The problem analyzed in this study is weak fault feature extraction and the enhancement of AUV thrusters based on Artificial Rabbits Optimization (ARO) and VMD. First, we introduce ARO to solve the problem of long-running times when using VMD for weak fault feature extraction. Then, we propose a VMD denoising method based on an improved ARO algorithm to address the issue of deteriorations in the fault feature extraction effect after introducing ARO. In this method, chaotic mapping and

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Kapoor, Gaurav, Nuttanan Wichitaksorn, Mengheng Li, and Wenjun Zhang. "Forecasting Half-Hourly Electricity Prices Using a Mixed-Frequency Structural VAR Framework." Econometrics 13, no. 1 (2025): 2. https://doi.org/10.3390/econometrics13010002.

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Electricity price forecasting has been a topic of significant interest since the deregulation of electricity markets worldwide. The New Zealand electricity market is run primarily on renewable fuels, and so weather metrics have a significant impact on electricity price and volatility. In this paper, we employ a mixed-frequency vector autoregression (MF-VAR) framework where we propose a VAR specification to the reverse unrestricted mixed-data sampling (RU-MIDAS) model, called RU-MIDAS-VAR, to provide point forecasts of half-hourly electricity prices using several weather variables and electrici

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Zhang, Luyao, Mengtao Zhu, Ziwei Zhang, and Yunjie Li. "Disentangled Representation Learning for Robust Radar Inter-Pulse Modulation Feature Extraction and Recognition." Remote Sensing 16, no. 19 (2024): 3585. http://dx.doi.org/10.3390/rs16193585.

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Modern Multi-Function Radars (MFRs) are sophisticated sensors that are capable of flexibly adapting their control parameters in transmitted pulse sequences. In complex electromagnetic environments, efficiently and accurately recognizing the inter-pulse modulations of non-cooperative radar pulse sequences is a key step for modern Electronic Support (ES) systems. Existing recognition methods focus more on algorithmic designs, such as neural network structure designs, to improve recognition performance. However, in open electromagnetic environments with increased flexibility in radar transmission

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Sun, Yang, Jungang Yang, Miao Li, and Wei An. "Infrared Small-Faint Target Detection Using Non-i.i.d. Mixture of Gaussians and Flux Density." Remote Sensing 11, no. 23 (2019): 2831. http://dx.doi.org/10.3390/rs11232831.

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The robustness of infrared small-faint target detection methods to noisy situations has been a challenging and meaningful research spot. The targets are usually spatially small due to the far observation distance. Considering the underlying assumption of noise distribution in the existing methods is impractical; a state-of-the-art method has been developed to dig out valuable information in the temporal domain and separate small-faint targets from background noise. However, there are still two drawbacks: (1) The mixture of Gaussians (MoG) model assumes that noise of different frames satisfies

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Tichý, Ondřej, Václav Šmídl, Radek Hofman, and Andreas Stohl. "LS-APC v1.0: a tuning-free method for the linear inverse problem and its application to source-term determination." Geoscientific Model Development 9, no. 11 (2016): 4297–311. http://dx.doi.org/10.5194/gmd-9-4297-2016.

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Abstract. Estimation of pollutant releases into the atmosphere is an important problem in the environmental sciences. It is typically formalized as an inverse problem using a linear model that can explain observable quantities (e.g., concentrations or deposition values) as a product of the source-receptor sensitivity (SRS) matrix obtained from an atmospheric transport model multiplied by the unknown source-term vector. Since this problem is typically ill-posed, current state-of-the-art methods are based on regularization of the problem and solution of a formulated optimization problem. This pr

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Yue, Zongsheng, Deyu Meng, Yongqing Sun, and Qian Zhao. "Hyperspectral Image Restoration under Complex Multi-Band Noises." Remote Sensing 10, no. 10 (2018): 1631. http://dx.doi.org/10.3390/rs10101631.

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Hyperspectral images (HSIs) are always corrupted by complicated forms of noise during the acquisition process, such as Gaussian noise, impulse noise, stripes, deadlines and so on. Specifically, different bands of the practical HSIs generally contain different noises of evidently distinct type and extent. While current HSI restoration methods give less consideration to such band-noise-distinctness issues, this study elaborately constructs a new HSI restoration technique, aimed at more faithfully and comprehensively taking such noise characteristics into account. Particularly, through a two-leve

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Jing, Tao, and Cheng Li. "Regression for Astronomical Data with Realistic Distributions, Errors, and Nonlinearity." Astronomical Journal 170, no. 1 (2025): 45. https://doi.org/10.3847/1538-3881/add891.

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Abstract We have developed a new regression technique, the maximum likelihood (ML)–based method and its variant, the Kolmogorov–Smirnov (KS) test–based method, designed to obtain unbiased regression results from typical astronomical data. A normalizing flow model is employed to automatically estimate the unobservable intrinsic distribution of the independent variable and the unobservable correlation between uncertainty level and intrinsic value of both independent and dependent variables from the observed data points in a variational-inference-based empirical Bayes approach. By incorporating t

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Soleimani, Hossein, and David J. Miller. "Semisupervised, Multilabel, Multi-Instance Learning for Structured Data." Neural Computation 29, no. 4 (2017): 1053–102. http://dx.doi.org/10.1162/neco_a_00939.

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Many classification tasks require both labeling objects and determining label associations for parts of each object. Example applications include labeling segments of images or determining relevant parts of a text document when the training labels are available only at the image or document level. This task is usually referred to as multi-instance (MI) learning, where the learner typically receives a collection of labeled (or sometimes unlabeled) bags, each containing several segments (instances). We propose a semisupervised MI learning method for multilabel classification. Most MI learning me

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Alnemari, Mohammed, and Nader Bagherzadeh. "Ultimate Compression: Joint Method of Quantization and Tensor Decomposition for Compact Models on the Edge." Applied Sciences 14, no. 20 (2024): 9354. http://dx.doi.org/10.3390/app14209354.

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This paper proposes the “ultimate compression” method as a solution to the expansive computation and high storage costs required by state-of-the-art neural network models in inference. Our approach uniquely combines tensor decomposition techniques with binary neural networks to create efficient deep neural network models optimized for edge inference. The process includes training floating-point models, applying tensor decomposition algorithms, binarizing the decomposed layers, and fine tuning the resulting models. We evaluated our approach in various state-of-the-art deep neural network archit

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Alhalaseh, Rania, and Suzan Alasasfeh. "Machine-Learning-Based Emotion Recognition System Using EEG Signals." Computers 9, no. 4 (2020): 95. http://dx.doi.org/10.3390/computers9040095.

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Many scientific studies have been concerned with building an automatic system to recognize emotions, and building such systems usually relies on brain signals. These studies have shown that brain signals can be used to classify many emotional states. This process is considered difficult, especially since the brain’s signals are not stable. Human emotions are generated as a result of reactions to different emotional states, which affect brain signals. Thus, the performance of emotion recognition systems by brain signals depends on the efficiency of the algorithms used to extract features, the f

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Holota, P. "Variational methods in geoid determination and function bases." Physics and Chemistry of the Earth, Part A: Solid Earth and Geodesy 24, no. 1 (1999): 3–14. http://dx.doi.org/10.1016/s1464-1895(98)00003-9.

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Cao, Xiao-Qun, Ya-Nan Guo, Shi-Cheng Hou, Cheng-Zhuo Zhang, and Ke-Cheng Peng. "Variational Principles for Two Kinds of Coupled Nonlinear Equations in Shallow Water." Symmetry 12, no. 5 (2020): 850. http://dx.doi.org/10.3390/sym12050850.

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It is a very important but difficult task to seek explicit variational formulations for nonlinear and complex models because variational principles are theoretical bases for many methods to solve or analyze the nonlinear problem. By designing skillfully the trial-Lagrange functional, different groups of variational principles are successfully constructed for two kinds of coupled nonlinear equations in shallow water, i.e., the Broer-Kaup equations and the (2+1)-dimensional dispersive long-wave equations, respectively. Both of them contain many kinds of soliton solutions, which are always symmet

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Pös, Ondrej, Jan Radvanszky, Jakub Styk, et al. "Copy Number Variation: Methods and Clinical Applications." Applied Sciences 11, no. 2 (2021): 819. http://dx.doi.org/10.3390/app11020819.

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Gains and losses of large segments of genomic DNA, known as copy number variants (CNVs) gained considerable interest in clinical diagnostics lately, as particular forms may lead to inherited genetic diseases. In recent decades, researchers developed a wide variety of cytogenetic and molecular methods with different detection capabilities to detect clinically relevant CNVs. In this review, we summarize methodological progress from conventional approaches to current state of the art techniques capable of detecting CNVs from a few bases up to several megabases. Although the recent rapid progress

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Umar, Najirah, and M. Adnan Nur. "Application of Naïve Bayes Algorithm Variations On Indonesian General Analysis Dataset for Sentiment Analysis." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 4 (2022): 585–90. http://dx.doi.org/10.29207/resti.v6i4.4179.

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Indonesian General Analysis Dataset is a dataset sourced from social media twitter by using keywords in the form of conjunctions to get a dataset that does not only focus on a particular topic. The use of Indonesian language datasets with general topics can be used to test the accuracy of the classification model so as to provide additional reference in choosing the right methods and parameters for sentiment analysis. One of the algorithms which in several studies produces the highest level of accuracy is naive Bayes which has several variations. This study aims to obtain the method with the b

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Levsen, N. D., D. J. Crawford, J. K. Archibald, A. Santos-Geurra, and M. E. Mort. "Nei's to Bayes': comparing computational methods and genetic markers to estimate patterns of genetic variation in Tolpis (Asteraceae)." American Journal of Botany 95, no. 11 (2008): 1466–74. http://dx.doi.org/10.3732/ajb.0800091.

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Simanjuntak, Taufik Ismail, Muhathir Muhathir, Fadlisyah Fadlisyah, and Ira Safira. "Performance Analysis of Naive Bayes Variation Method in Spice Image Classification Using Histogram of Gradient Oriented (HOG) Feature Extraction." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 7, no. 1 (2023): 282–91. http://dx.doi.org/10.31289/jite.v7i1.7957.

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Indonesia has a lot of natural wealth of spices. The diversity of spices is an inseparable aspect of Indonesian history. Spices and seasonings are biological resources that have long played an important role in human life. Indonesian spices have almost the same color and shape. The purpose of this study was to analyze the performance of the Naïve Bayes variation method in classifying spices using a Histogram Of Oriented Gradient (HOG) feature extraction. Based on 3 tests, the performance of the four Naïve Bayes variation methods carried out in this study, it can be seen that testing 5 types of

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K.S. Krishnamohan. "Splines and Special Functions to Solve Boundary Value Problems in Differential Equations." Journal of Information Systems Engineering and Management 10, no. 3 (2025): 1912–26. https://doi.org/10.52783/jisem.v10i3.8859.

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Professional applications in engineering and physics and applied sciences require Boundary value problems (BVPs) for their mathematical modeling. The traditional solution methods struggle to handle nonlinear BVPs because stability issues and accuracy limits prevent them from obtaining satisfactory results. The research explores spline-based numerical methods that use special function approximations to achieve efficient solutions of nonlinear BVPs. The combination of B-splines and high-degree splines with spectral special functions allows for building accurate smooth approximations that preserv

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Saleh, Talal Hussein. "Genetic variation of sequencing 18srDNA gene of Trichophyton mentagrophytes isolates." Romanian Journal of Infectious Diseases 26, no. 3 (2023): 105–10. http://dx.doi.org/10.37897/rjid.2023.3.4.

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Background. Trichophyton species are considered the most frequent causative and considerable agents in infection concerns. Objective. The existing training designed to conduct the sequencing analysis of nucleotides of ITS1-5.8S-ITS2 for Trichophyton mentagrophytes. Methods. The isolation and identification of pathogenic fungi, Trichophyton mentagrophytes from clinical specimens was done based on the standard morphological and molecular methods. The genomic DNA of fungal isolates were extracted and purified to amplify with primers of 18S rRNA gene for detection and sequencing the nitrogenous ba

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Rajshree Singh. "BALANCING SARCASTIC HINGLISH SHORT TEXT DATA USING AUGMENTATION TECHNIQUES WITH HANDLING SPELLING VARIATIONS." Journal of Electrical Systems 20, no. 7s (2024): 1085–101. http://dx.doi.org/10.52783/jes.3602.

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In the real world, there is a significant presence of imbalanced data due to the fact that the classes that make up the datasets are not evenly distributed. Even when using methods that are traditionally used to achieve class balance, such as re-sampling & re-weighting, current deep learning still faces a significant obstacle because of the class imbalance. This study’s major objective is proposing a data augmentation technique to balance the data to improve the sample sizes for the minority classes. Python, a well-known programming language, & multiple methods of machine learning are

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