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

Academic literature on the topic 'Variational bayes methods'

Author: Grafiati
Published: 28 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Variational bayes methods"

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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 models in the CE family, by perturbing the expected sufficient statistics of the complete-data likelihood. For a broadly-used class of non-CE models, those with binomial likelihoods, we show how to bring such models into the CE family, such that inferences in the modified model resemble the private variational Bayes algorithm as closely as possible, using the Pólya-Gamma data augmentation scheme. The iterative nature of variational Bayes presents a further challenge since iterations increase the amount of noise needed. We overcome this by combining: (1) an improved composition method for differential privacy, called the moments accountant, which provides a tight bound on the privacy cost of multiple VB iterations and thus significantly decreases the amount of additive noise; and (2) the privacy amplification effect of subsampling mini-batches from large-scale data in stochastic learning. We empirically demonstrate the effectiveness of our method in CE and non-CE models including latent Dirichlet allocation, Bayesian logistic regression, and sigmoid belief networks, evaluated on real-world datasets.
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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 (May 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 (April 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, it has not yet been theoretically shown. In this letter, through analysis of the simplest unidentifiable models, we theoretically show some properties of the VB approach. We first prove that in three-layer linear neural networks, the VB approach is asymptotically equivalent to a positive-part James-Stein type shrinkage estimation. Then we theoretically clarify its free energy, generalization error, and training error. Comparing them with those of the ML estimation and the Bayes estimation, we discuss the advantage of the VB approach. We also show that unlike in the Bayes estimation, the free energy and the generalization error are less simply related with each other and that in typical cases, the VB free energy well approximates the Bayes one, while the VB generalization error significantly differs from the Bayes one.
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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 (July 16, 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 developed variational Bayesian beta-mixture model (VBBMM) to the feature selection problem in the context of DNA methylation data generated from a highly popular beadarray technology. We demonstrate that VBBMM offers significant improvements in inference and feature selection in this type of data compared to an Expectation-Maximization (EM) algorithm, at a significantly reduced computational cost. We further demonstrate the added value of VBBMM as a feature selection and prioritization step in the context of identifying prognostic markers in breast cancer. A variational Bayesian approach to feature selection of DNA methylation profiles should thus be of value to any study undergoing large-scale DNA methylation profiling in search of novel biomarkers.
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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 (March 16, 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 the auto-encoding variational Bayes framework, is not domain specific and may be useful for other applications. Availability and implementation The factor model is available in the scVI package hosted at https://github.com/YosefLab/scVI/. Contact v@nxn.se Supplementary information Supplementary data are available at Bioinformatics online.
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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 (June 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 work offers an extensive suite of MCMC algorithms evaluated on an important class of models for physiological signal analysis.
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Zhao, Yuexuan, and Jing Huang. "Dirichlet Process Prior for Student’s t Graph Variational Autoencoders." Future Internet 13, no. 3 (March 16, 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 graphs, which will eventually lead to the discovery of only external relations and the neglect of some complex internal correlations. In this paper, we present a novel prior distribution for GVAE, called Dirichlet process (DP) construction for Student’s t (St) distribution. The DP allows the latent variables to adapt their complexity during learning and then cooperates with heavy-tailed St distribution to approach sufficient node representation. Experimental results show that this method can achieve a relatively better performance against the baselines.
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Shapovalova, Yuliya. "“Exact” and Approximate Methods for Bayesian Inference: Stochastic Volatility Case Study." Entropy 23, no. 4 (April 15, 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 different model specifications; (2) adaptable to higher dimensions of the model in a straightforward way; (3) feasible in the multivariate case. We show that when using the stochastic volatility model for methods comparison, various data-generating processes have to be considered to make a fair assessment of the methods. Finally, we present a challenging specification of the multivariate stochastic volatility model, which is rarely used to illustrate the methods but constitutes an important practical application.
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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 (September 21, 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 issue pertinent to measurement error models is parameter identification, and this is resolved by employing a prior distribution on the measurement error variance. The methods are shown to perform well in multiple simulation studies, where we analyze the impact on posterior estimates for different values of reliability ratio or variance of the true unobserved quantity used in the data generating process. The paper further implements the proposed algorithms in an application drawn from the health literature and shows that modeling measurement error in the data can improve model fitting.
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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 developed sparse blind source separation and deconvolution (S-BSS-vecDC) method for separation of original sources from dynamic medical data based on probability modeling and Variational Bayes approximation methodology. In this paper, we extend this method and we apply the methods on dynamic brain PET data and application and comparison of derived algorithms with those of similar assumptions are given. The S-BSS-vecDC algorithm is publicly available for download.
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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 méthodes: les algorithmes d'échantillonnage Monte Carlo par chaînes de Markov (MCMC) et les approches basées sur des approximations bayésiennes variationnelles (VBA).La thèse est composée de deux parties. La première partie concerne les algorithmes d'échantillonnage. Dans un premier temps, une attention particulière est consacrée à l'amélioration des méthodes MCMC basées sur la discrétisation de la diffusion de Langevin. Nous proposons une nouvelle méthode pour régler la composante directionnelle de tels algorithmes en utilisant une stratégie de Majoration-Minimisation ayant des propriétés de convergence garanties. Les résultats expérimentaux obtenus lors de la restauration d'un signal parcimonieux confirment la rapidité de cette nouvelle approche par rapport à l'échantillonneur usuel de Langevin. Dans un second temps, une nouvelle méthode d'échantillonnage basée sur une stratégie d'augmentation des données est proposée pour améliorer la vitesse de convergence et les propriétés de mélange des algorithmes d'échantillonnage standards. L'application de notre méthode à différents exemples en traitement d'images montre sa capacité à surmonter les difficultés liées à la présence de corrélations hétérogènes entre les coefficients du signal.Dans la seconde partie de la thèse, nous proposons de recourir aux techniques VBA pour la restauration de signaux dégradés par un bruit non-gaussien. Afin de contourner les difficultés liées à la forme compliquée de la loi a posteriori, une stratégie de majoration est employée pour approximer la vraisemblance des données ainsi que la densité de la loi a priori. Grâce à sa flexibilité, notre méthode peut être appliquée à une large classe de modèles et permet d'estimer le signal d'intérêt conjointement au paramètre de régularisation associé à la loi a priori. L'application de cette approche sur des exemples de déconvolution d'images en présence d'un bruit mixte Poisson-gaussien, confirme ses bonnes performances par rapport à des méthodes supervisées de l'état de l'art
Bayesian approaches are widely used in signal processing applications. In order to derive plausible estimates of original parameters from their distorted observations, they rely on the posterior distribution that incorporates prior knowledge about the unknown parameters as well as informations about the observations. The posterior mean estimator is one of the most commonly used inference rule. However, as the exact posterior distribution is very often intractable, one has to resort to some Bayesian approximation tools to approximate it. In this work, we are mainly interested in two particular Bayesian methods, namely Markov Chain Monte Carlo (MCMC) sampling algorithms and Variational Bayes approximations (VBA).This thesis is made of two parts. The first one is dedicated to sampling algorithms. First, a special attention is devoted to the improvement of MCMC methods based on the discretization of the Langevin diffusion. We propose a novel method for tuning the directional component of such algorithms using a Majorization-Minimization strategy with guaranteed convergence properties.Experimental results on the restoration of a sparse signal confirm the performance of this new approach compared with the standard Langevin sampler. Second, a new sampling algorithm based on a Data Augmentation strategy, is proposed to improve the convergence speed and the mixing properties of standard MCMC sampling algorithms. Our methodological contributions are validated on various applications in image processing showing the great potentiality of the proposed method to manage problems with heterogeneous correlations between the signal coefficients.In the second part, we propose to resort to VBA techniques to build a fast estimation algorithm for restoring signals corrupted with non-Gaussian noise. In order to circumvent the difficulties raised by the intricate form of the true posterior distribution, a majorization technique is employed to approximate either the data fidelity term or the prior density. Thanks to its flexibility, the proposed approach can be applied to a broad range of data fidelity terms allowing us to estimate the target signal jointly with the associated regularization parameter. Illustration of this approach through examples of image deconvolution in the presence of mixed Poisson-Gaussian noise, show the good performance of the proposed algorithm compared with state of the art supervised methods
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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. As the behaviour of individual agents can change significantly, for example if agents move in a physical space or learn to perform an analysis task, this work proposes a novel combination method that accounts for these time variations in a fully Bayesian manner using a dynamic generalised linear model. This approach can also be used to augment agents’ responses with continuous feature data, thus permitting decision-making when agents’ responses are in limited supply. Working with information inferred using the proposed Bayesian techniques, an information-theoretic approach is developed for choosing optimal pairs of tasks and agents. This approach is demonstrated by an algorithm that maintains a trustworthy pool of workers and enables efficient learning by selecting informative tasks. The novel methods developed here are compared theoretically and empirically to a range of existing decision combination methods, using both simulated and real data. The results show that the methodology proposed in this thesis improves accuracy and computational efficiency over alternative approaches, and allows for insights to be determined into the behavioural groupings of agents.
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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. Selected methods were adjusted for deinterleaving and implemented in programming language Python. Their efficiency is examined on simulated and real data.
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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
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 of real factors, including access to primary health care, the availability of diagnostic and treatment facilities and the treatment actually given, or to artefact, such as lead-time bias, stage migration, sampling error or measurement error. Likewise, a temporal increase in survival could be the result of earlier diagnosis and improved treatment of cancer; it could also be due to artefact after the introduction of screening programs (adding lead time), changes in the definition of cancer, stage migration or several of these factors, producing both real and artefactual trends. In this thesis, I report methods that I modified and applied, some technical issues in the use of such data, and an analysis of data from the State of New South Wales (NSW), Australia, illustrating their use in evaluating and potentially improving the quality of cancer care, showing how data quality might affect the conclusions of such analyses. This thesis describes studies of comparative survival based on population-based cancer registry data, with three published papers and one accepted manuscript (subject to minor revision). In the first paper, I describe a modified method for estimating spatial variation in cancer survival using empirical Bayes methods (which was published in Cancer Causes and Control 2004). I demonstrate in this paper that the empirical Bayes method is preferable to standard approaches and show how it can be used to identify cancer types where a focus on reducing area differentials in survival might lead to important gains in survival. In the second paper (published in the European Journal of Cancer 2005), I apply this method to a more complete analysis of spatial variation in survival from colorectal cancer in NSW and show that estimates of spatial variation in colorectal cancer can help to identify subgroups of patients for whom better application of treatment guidelines could improve outcome. I also show how estimates of the numbers of lives that could be extended might assist in setting priorities for treatment improvement. In the third paper, I examine time trends in survival from 28 cancers in NSW between 1980 and 1996 (published in the International Journal of Cancer 2006) and conclude that for many cancers, falls in excess deaths in NSW from 1980 to 1996 are unlikely to be attributable to earlier diagnosis or stage migration; thus, advances in cancer treatment have probably contributed to them. In the accepted manuscript, I described an extension of the work reported in the second paper, investigating the accuracy of staging information recorded in the registry database and assessing the impact of error in its measurement on estimates of spatial variation in survival from colorectal cancer. The results indicate that misclassified registry stage can have an important impact on estimates of spatial variation in stage-specific survival from colorectal cancer. Thus, if cancer registry data are to be used effectively in evaluating and improving cancer care, the quality of stage data might have to be improved. Taken together, the four papers show that creative, informed use of population-based cancer registry data, with appropriate statistical methods and acknowledgement of the limitations of the data, can be a valuable tool for evaluating and possibly improving cancer care. Use of these findings to stimulate evaluation of the quality of cancer care should enhance the value of the investment in cancer registries. They should also stimulate improvement in the quality of cancer registry data, particularly that on stage at diagnosis. The methods developed in this thesis may also be used to improve estimation of geographical variation in other count-based health measures when the available data are sparse.
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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 de modèle implicite, une méthode variationnelle reposant sur une représentation implicite des formes. Après avoir amélioré sa formulation mathématique, nous montrons son potentiel sur des problèmes cliniques difficiles. Nous introduisons ensuite différentes généralisations, indépendantes mais complémentaires, visant à enrichir le modèle de forme et d'apparence utilisé. La diversité des applications cliniques traitées prouve la généricité et l'efficacité de nos contributions.
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Books on the topic "Variational bayes methods"

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

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service), SpringerLink (Online, ed. Bases, outils et principes pour l'analyse variationnelle. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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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. Berlin/Heidelberg: 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 social behavior, psychopathology, emotion, cognition, and stress in healthy volunteers, patient populations, and an array of nonhuman species including nonhuman primates, rodents, insects, and fish. Chapters draw on molecular methods covering candidate genes, genome-wide association studies, copy number variations, gene expression studies, and epigenetics while addressing the ethical, legal, and social issues to emerge from this new and exciting research approach.
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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 methods, a central concern of a discipline that bases its conclusions on evidence drawn from the real world of social interaction. The third section deals directly with a number of issues in multilingualism and language contact. The fourth section focuses on a core area of sociolinguistics: the study of language variation and change. The fifth section focuses on macrosociolinguistics and explores language policy, ideology, and attitudes in a wide range of contexts. The final section of the volume discusses sociolinguistics in a number of different domains including law, medicine, sign-language interpretation, language awareness, language revitalization, and social activism.
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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, 387–406. Dordrecht: 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, 207–22. Cham: 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, 123–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_16.

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Purcell, Shaun M. "Genetic Methodologies and Applications." In Neurobiology of Mental Illness, edited by Karl Deisseroth, 160–71. 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 underway in psychiatric genetics include studies of de novo variation in exome sequencing, large deletion and duplication copy number variants, rare and low-frequency variants segregating in populations, and common polymorphisms.
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Conference papers on the topic "Variational bayes methods"

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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}. California: 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 method, called the moments accountant, and (2) the privacy amplification effect of subsampling mini-batches from large-scale data in stochastic learning. We empirically demonstrate the effectiveness of our method on LDA topic models, evaluated on Wikipedia. In the full paper we extend our method to a broad class of models, including Bayesian logistic regression and sigmoid belief networks.
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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. California: 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 used to encode observables to latent embeddings, so that the evidence lower bound (ELBO) can be optimized using the Stochastic Gradient Variational Bayes (SGVB) estimator and the reparameterization trick. Quantitative comparisons with strong baselines are included in this paper, and experimental results show that VaDE significantly outperforms the state-of-the-art clustering methods on 5 benchmarks from various modalities. Moreover, by VaDE's generative nature, we show its capability of generating highly realistic samples for any specified cluster, without using supervised information during training.
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Kaji, Daisuke, Kazuho Watanabe, and Masahiro Kobayashi. "Multi-Decoder RNN Autoencoder Based on Variational Bayes Method." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206686.

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Yin, Fei, and Cheng-lin Liu. "A Variational Bayes Method for Handwritten Text Line Segmentation." In 2009 10th International Conference on Document Analysis and Recognition. IEEE, 2009. http://dx.doi.org/10.1109/icdar.2009.98.

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Liu, Hao, Lirong He, Haoli Bai, Bo Dai, Kun Bai, and Zenglin Xu. "Structured Inference for Recurrent Hidden Semi-markov Model." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/339.

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Segmentation and labeling for high dimensional time series is an important yet challenging task in a number of applications, such as behavior understanding and medical diagnosis. Recent advances to model the nonlinear dynamics in such time series data, has suggested to involve recurrent neural networks into Hidden Markov Models. However, this involvement has caused the inference procedure much more complicated, often leading to intractable inference, especially for the discrete variables of segmentation and labeling. To achieve both flexibility and tractability in modeling nonlinear dynamics of discrete variables, we present a structured and stochastic sequential neural network (SSNN), which composes with a generative network and an inference network. In detail, the generative network aims to not only capture the long-term dependencies but also model the uncertainty of the segmentation labels via semi-Markov models. More importantly, for efficient and accurate inference, the proposed bi-directional inference network reparameterizes the categorical segmentation with the Gumbel-Softmax approximation and resorts to the Stochastic Gradient Variational Bayes. We evaluate the proposed model in a number of tasks, including speech modeling, automatic segmentation and labeling in behavior understanding, and sequential multi-objects recognition. Experimental results have demonstrated that our proposed model can achieve significant improvement over the state-of-the-art methods.
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Tamai, Tomoki, and Koujin Takeda. "Variational Bayes method for matrix factorization to two sparse factorized matrices." In 2018 International Symposium on Information Theory and Its Applications (ISITA). IEEE, 2018. http://dx.doi.org/10.23919/isita.2018.8664315.

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