Relevant bibliographies by topics / Variational bayes methods

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Variational bayes methods'

Author: Grafiati
Published: 28 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Variational bayes methods"

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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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Dissertations / Theses on the topic "Variational bayes methods"

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Marnissi, Yosra. "Bayesian methods for inverse problems in signal and image processing." Thesis, Paris Est, 2017. http://www.theses.fr/2017PESC1142/document.

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Les approches bayésiennes sont largement utilisées dans le domaine du traitement du signal. Elles utilisent des informations a priori sur les paramètres inconnus à estimer ainsi que des informations sur les observations, pour construire des estimateurs. L'estimateur optimal au sens du coût quadratique est l'un des estimateurs les plus couramment employés. Toutefois, comme la loi a posteriori exacte a très souvent une forme complexe, il faut généralement recourir à des outils d'approximation bayésiens pour l'approcher. Dans ce travail, nous nous intéressons particulièrement à deux types de méth
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Simpson, Edwin Daniel. "Combined decision making with multiple agents." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:f5c9770b-a1c9-4872-b0dc-1bfa28c11a7f.

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In a wide range of applications, decisions must be made by combining information from multiple agents with varying levels of trust and expertise. For example, citizen science involves large numbers of human volunteers with differing skills, while disaster management requires aggregating information from multiple people and devices to make timely decisions. This thesis introduces efficient and scalable Bayesian inference for decision combination, allowing us to fuse the responses of multiple agents in large, real-world problems and account for the agents’ unreliability in a principled manner. A
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Tomešová, Tereza. "Autonomní jednokanálový deinterleaving." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445470.

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This thesis deals with an autonomous single-channel deinterleaving. An autonomous single-channel deinterleaving is a separation of the received sequence of impulses from more than one emitter to sequences of impulses from one emitter without a human assistance. Methods used for deinterleaving could be divided into single-parameter and multiple-parameter methods according to the number of parameters used for separation. This thesis primarily deals with multi-parameter methods. As appropriate methods for an autonomous single-channel deinterleaving DBSCAN and variational bayes methods were chosen
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Yu, Xue Qin. "Comparing survival from cancer using population-based cancer registry data - methods and applications." Thesis, The University of Sydney, 2007. http://hdl.handle.net/2123/1774.

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Over the past decade, population-based cancer registry data have been used increasingly worldwide to evaluate and improve the quality of cancer care. The utility of the conclusions from such studies relies heavily on the data quality and the methods used to analyse the data. Interpretation of comparative survival from such data, examining either temporal trends or geographical differences, is generally not easy. The observed differences could be due to methodological and statistical approaches or to real effects. For example, geographical differences in cancer survival could be due to a number
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Yu, Xue Qin. "Comparing survival from cancer using population-based cancer registry data - methods and applications." University of Sydney, 2007. http://hdl.handle.net/2123/1774.

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Doctor of Philosophy<br>Over the past decade, population-based cancer registry data have been used increasingly worldwide to evaluate and improve the quality of cancer care. The utility of the conclusions from such studies relies heavily on the data quality and the methods used to analyse the data. Interpretation of comparative survival from such data, examining either temporal trends or geographical differences, is generally not easy. The observed differences could be due to methodological and statistical approaches or to real effects. For example, geographical differences in cancer survival
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Falk, Matthew Gregory. "Incorporating uncertainty in environmental models informed by imagery." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/33235/1/Matthew_Falk_Thesis.pdf.

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In this thesis, the issue of incorporating uncertainty for environmental modelling informed by imagery is explored by considering uncertainty in deterministic modelling, measurement uncertainty and uncertainty in image composition. Incorporating uncertainty in deterministic modelling is extended for use with imagery using the Bayesian melding approach. In the application presented, slope steepness is shown to be the main contributor to total uncertainty in the Revised Universal Soil Loss Equation. A spatial sampling procedure is also proposed to assist in implementing Bayesian melding given th
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Prevost, Raphaël. "Méthodes variationnelles pour la segmentation d'images à partir de modèles : applications en imagerie médicale." Phd thesis, Université Paris Dauphine - Paris IX, 2013. http://tel.archives-ouvertes.fr/tel-00932995.

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La segmentation d'images médicales est depuis longtemps un sujet de recherche actif. Cette thèse traite des méthodes de segmentation basées modèles, qui sont un bon compromis entre généricité et capacité d'utilisation d'informations a priori sur l'organe cible. Notre but est de construire un algorithme de segmentation pouvant tirer profit d'une grande variété d'informations extérieures telles que des bases de données annotées (via l'apprentissage statistique), d'autres images du même patient (via la co-segmentation) et des interactions de l'utilisateur. Ce travail est basé sur la déformation d
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Books on the topic "Variational bayes methods"

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Šmídl, Václav. The variational Bayes method in signal processing. Springer, 2006.

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Smídl, Václav, and Anthony Quinn. The Variational Bayes Method in Signal Processing (Signals and Communication Technology). Springer, 2005.

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Quinn, Anthony, and Václav Šmídl. The Variational Bayes Method in Signal Processing. Springer, 2010.

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The Variational Bayes Method in Signal Processing. Springer-Verlag, 2006. http://dx.doi.org/10.1007/3-540-28820-1.

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Quinn, Anthony, and Václav Šmídl. The Variational Bayes Method in Signal Processing (Signals and Communication Technology). Springer, 2006.

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Canli, Turhan, ed. The Oxford Handbook of Molecular Psychology. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199753888.001.0001.

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Determining the biological bases for behavior—and the extent to which we can observe and explain their neural underpinnings—requires a bold, broadly defined research methodology. The interdisciplinary entries in this handbook are organized around the principle of “molecular psychology,” which unites cutting-edge research from such wide-ranging disciplines as clinical neuroscience and genetics, psychology, behavioral neuroscience, and neuroethology. For the first time in a single volume, leaders from diverse research areas present their work in which they use molecular approaches to investigate
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Bayley, Robert, Richard Cameron, and Ceil Lucas, eds. The Oxford Handbook of Sociolinguistics. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199744084.001.0001.

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The Oxford Handbook of Sociolinguistics contains forty chapters dealing with a great variety of topics in the study of language and society. It presents the major theoretical approaches in particular bilingual and multilingual contexts, and both spoken and signed languages. The volume not only offers an up-to-date guide to the diverse areas of the study of language in society, but also numerous guideposts to where the field is headed. The first section examines the contributions of the various disciplines that have contributed to the sociolinguistic enterprise. The second section deals with me
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Book chapters on the topic "Variational bayes methods"

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Prathap, G. "Variational bases: A philosophical summing-up." In The Finite Element Method in Structural Mechanics. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-017-3319-9_12.

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Watanabe, Chihiro, Kaoru Hiramatsu, and Kunio Kashino. "Recursive Extraction of Modular Structure from Layered Neural Networks Using Variational Bayes Method." In Discovery Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67786-6_15.

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Yoshimoto, Junichiro, Shin Ishii, and Masa-aki Sato. "System Identification Based on Online Variational Bayes Method and Its Application to Reinforcement Learning." In Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_16.

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Mielniczuk, Jan, and Adam Wawrzeńczyk. "Augmented Prediction of a True Class for Positive Unlabeled Data Under Selection Bias." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240806.

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We introduce a new observational setting for Positive Unlabeled (PU) data where the observations at prediction time are also labeled. This occurs commonly in practice – we argue that the additional information is important for prediction, and call this task “augmented PU prediction”. We allow for labeling to be feature dependent. In such scenario, Bayes classifier and its risk is established and compared with a risk of a classifier which for unlabeled data is based only on predictors. We introduce several variants of the empirical Bayes rule in such scenario and investigate their performance.
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Zaid, Mohamed, and RajendranT. "Higher Classification Accuracy of Income Class Using Decision Tree Algorithm over Naive Bayes Algorithm." In Advances in Parallel Computing Algorithms, Tools and Paradigms. IOS Press, 2022. http://dx.doi.org/10.3233/apc220079.

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Developing two machine learning classifiers with higher accuracy for classifying income classes for people earning less and a higher salary scale between 50,000. Decision Tree Algorithm (DTA) and Naive Bayes Algorithm (NBA) are the two classifier mechanisms employed. On a dataset of 32516 records, the methods were implemented and tested. Implemented each algorithm through programs and performed ten rounds on both methods to determine distinct scales of income class for who earns lesser and higher salary scale between 50,000. The G-power test is around 80% accurate. The findings of the programm
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Purcell, Shaun M. "Genetic Methodologies and Applications." In Neurobiology of Mental Illness, edited by Karl Deisseroth. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199934959.003.0012.

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There have been tremendous advances in the molecular technologies and data-analytic methods at our disposal for studying the genetic bases of complex d diseases and traits. These advances have enabled the creation of comprehensive catalogs of different forms of human genetic variation, as well as large-scale studies focused on specific diseases or traits. This chapter outlines the general principles behind some of these advances and discusses their application to studying complex genetic traits, with a focus on neuropsychiatric disease in particular. Different genetic strategies that are under
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Williams, David M., and Vicki H. Harris. "Phosphorus methods in nucleotide chemistry." In Organophosphorus Reagents. Oxford University PressOxford, 2004. http://dx.doi.org/10.1093/oso/9780198502623.003.0009.

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Abstract There are currently many reagents available for the chemical synthesis of nucleotides via phosphorylation of nucleosides. Although much of the early chemistry in this area was based on the use of relatively unstable phosphorus(V) intermediates, there are now reliable methods in common usage, which employ both phosphorus(V) and the more reactive phosphorus(III) intermediates. The main impetus for the development of nucleotide chemistry in recent years has been the desire for the preparation of the 5’-triphosphates of nucleoside analogues containing both modified bases and modified suga
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Sander, Chris, Michael Scharf,, and Reinhard Schneider. "Design of Protein Structures." In Protein Engineering. Oxford University PressOxford, 1992. http://dx.doi.org/10.1093/oso/9780199631391.003.0004.

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Abstract The design of protein structures, whether by variation of natural proteins or from scratch, is an essential first step in the development of newly engineered proteins. Designs based on theoretical and computer methods enter the protein design cycle of repeated steps of experimental testing and improvement, much as in other engineering disciplines. Success in protein design depends on how well we make use of this cycle and on how well we understand the principles of protein folding, both in terms of the underlying molecular physics and in terms of knowledge extracted from the data-base
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Utecht Joseph, Brochhausen Mathias, Judkins John, Schneider Jodi, and Boyce Richard D. "Formalizing Evidence Type Definitions for Drug-Drug Interaction Studies to Improve Evidence Base Curation." In Studies in Health Technology and Informatics. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-830-3-960.

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In this research we aim to demonstrate that an ontology-based system can categorize potential drug-drug interaction (PDDI) evidence items into complex types based on a small set of simple questions. Such a method could increase the transparency and reliability of PDDI evidence evaluation, while also reducing the variations in content and seriousness ratings present in PDDI knowledge bases. We extended the DIDEO ontology with 44 formal evidence type definitions. We then manually annotated the evidence types of 30 evidence items. We tested an RDF/OWL representation of answers to a small number o
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Chianese, Claudia, Maria Acanfora, Tommaso Coppola, and Marco Altosole. "On the Application of the Isogeometric Approach to a Ship’s Structural Member." In Progress in Marine Science and Technology. IOS Press, 2024. http://dx.doi.org/10.3233/pmst240015.

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Isogeometric Analysis (IgA) is an emerging numerical procedure conceived as a useful advancement of the traditional Finite Element Method (FEM). The essence of IgA is to bridge the current communication gap between Computer-Aided Design (CAD) and Computer-Aided Engineering (CAE) modules by employing high-order smooth spline basis functions both for geometry modelling and the construction of the solution space of analysis. As such, the method enables major benefits, namely a simplified pre-processing workflow and enhanced accuracy per degree of freedom associated with the capability of by-passi
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Conference papers on the topic "Variational bayes methods"

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Nakahara, Yuta. "Variational Bayesian Methods for a Tree-Structured Stick-Breaking Process Mixture of Gaussians by Application of the Bayes Codes for Context Tree Models." In 2024 International Symposium on Information Theory and Its Applications (ISITA). IEEE, 2024. https://doi.org/10.23919/isita60732.2024.10858231.

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Wang, Hao, Yi Deng, Ru Xin Hou, and Qie Liu. "A Data-Driven Prediction Method for Lithium-ion Batteries based on Variational Bayes and Particle Filtering." In 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2024. http://dx.doi.org/10.1109/ddcls61622.2024.10606755.

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Motahari, Nicholas, Nandeesh Hiremath, and Narayanan Komerath. "Towards Generalized Certification of Slung Load Flight Envelopes." In Vertical Flight Society 72nd Annual Forum & Technology Display. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11501.

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An alternative is sought to individual full-scale flight testing in order to certify the safe flight envelope for every combination of helicopter and slung load. The major uncertainty has been the difficulty in obtaining reliable aerodynamic load maps for use in dynamic simulation. The possibility of reliable certification based on an iterative process is discussed. Knowledge bases with parametric variation of canonical shapes are now feasible using the Continuous Rotation method. The test case of an empty engine canister is considered. An initial estimate of dynamics based on interpolation fr
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Ayasso, Hacheme, Sofia Fekih-Salem, Ali Mohammad-Djafari, Marcelo de Souza Lauretto, Carlos Alberto de Bragança Pereira, and Julio Michael Stern. "Variational Bayes Approach For Tomographic Reconstruction." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: Proceedings of the 28th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2008. http://dx.doi.org/10.1063/1.3039006.

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Villalba, Jesus, and Eduardo Lleida. "Unsupervised adaptation of PLDA by using variational Bayes methods." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6853695.

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Yang, Yaodong, Rui Luo, and Yuanyuan Liu. "Adversarial Variational Bayes Methods for Tweedie Compound Poisson Mixed Models." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682184.

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Foulds, James R., Mijung Park, Kamalika Chaudhuri, and Max Welling. "Variational Bayes in Private Settings (VIPS) (Extended Abstract)." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/705.

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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. The iterative nature of variational Bayes presents a challenge since iterations increase the amount of noise needed to ensure privacy. We overcome this by combining: (1) an improved composition meth
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Saito, Hidetoshi, Masayuki Hayashi, and Ryuji Kohno. "The Maximum A Posteriori Decoding Using Variational Bayes Methods for Digital Magnetic Recording Channels." In 2007 IEEE Information Theory Workshop. IEEE, 2007. http://dx.doi.org/10.1109/itw.2007.4313041.

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Jiang, Zhuxi, Yin Zheng, Huachun Tan, Bangsheng Tang, and Hanning Zhou. "Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/273.

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Clustering is among the most fundamental tasks in machine learning and artificial intelligence. In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE). Specifically, VaDE models the data generative procedure with a Gaussian Mixture Model (GMM) and a deep neural network (DNN): 1) the GMM picks a cluster; 2) from which a latent embedding is generated; 3) then the DNN decodes the latent embedding into an observable. Inference in VaDE is done in a variational way: a different DNN is use
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Yap, Sin-Yee, Junn Yong Loo, Chee-Ming Ting, et al. "A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/592.

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Recent applications of pattern recognition techniques on brain connectome classification using functional connectivity (FC) are shifting towards acknowledging the non-Euclidean topology and dynamic aspects of brain connectivity across time. In this paper, a deep spatiotemporal variational Bayes (DSVB) framework is proposed to learn time-varying topological structures in dynamic FC networks for identifying autism spectrum disorder (ASD) in human participants. The framework incorporates a spatial-aware recurrent neural network with an attention-based message passing scheme to capture rich spatio
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Reports on the topic "Variational bayes methods"

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Seroussi, Eyal, and George Liu. Genome-Wide Association Study of Copy Number Variation and QTL for Economic Traits in Holstein Cattle. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7593397.bard.

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Copy number variation (CNV) has been recently identified in human and other mammalian genomes and increasing awareness that CNV might be a major source for heritable variation in complex traits has emerged. Despite this, little has been published on CNVs in Holsteins. In order to fill this knowledge-gap, we proposed a genome-wide association study between quantitative trait loci (QTL) for economic traits and CNV in the Holstein cattle. The approved feasibility study was aimed at the genome-wide characterization of CNVs in Holstein cattle and at the demonstrating of their possible association w
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Ullman, Diane, James Moyer, Benjamin Raccah, Abed Gera, Meir Klein, and Jacob Cohen. Tospoviruses Infecting Bulb Crops: Evolution, Diversity, Vector Specificity and Control. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7695847.bard.

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Objectives. The overall goal of the proposed research was to develop a mechanistic understanding of tospovirus evolution, diversity and vector specificity that could be applied to development of novel methods for limiting virus establishment and spread. Our specific objectives were: 1) To characterize newly intercepted tospoviruses in onion, Hippeastrum and other bulb crops and compare them with the known tomato spotted wilt virus (TSWV) and its isolates; 2) To characterize intra- and interspecific variation in the virus transmission by thrips of the new and distinct tospoviruses. and, 3) To d
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