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Relevant bibliographies by topics / Spike-and-slab priors

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Academic literature on the topic 'Spike-and-slab priors'

Author: Grafiati

Published: 5 October 2024

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Journal articles on the topic "Spike-and-slab priors"

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Ročková, Veronika, and Edward I. George. "Negotiating multicollinearity with spike-and-slab priors." METRON 72, no. 2 (June 11, 2014): 217–29. http://dx.doi.org/10.1007/s40300-014-0047-y.

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Rockova, Veronika, and Kenichiro McAlinn. "Dynamic Variable Selection with Spike-and-Slab Process Priors." Bayesian Analysis 16, no. 1 (2021): 233–69. http://dx.doi.org/10.1214/20-ba1199.

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Antonelli, Joseph, Giovanni Parmigiani, and Francesca Dominici. "High-Dimensional Confounding Adjustment Using Continuous Spike and Slab Priors." Bayesian Analysis 14, no. 3 (September 2019): 805–28. http://dx.doi.org/10.1214/18-ba1131.

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Hernández-Lobato, José Miguel, Daniel Hernández-Lobato, and Alberto Suárez. "Expectation propagation in linear regression models with spike-and-slab priors." Machine Learning 99, no. 3 (December 10, 2014): 437–87. http://dx.doi.org/10.1007/s10994-014-5475-7.

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Scheipl, Fabian, Ludwig Fahrmeir, and Thomas Kneib. "Spike-and-Slab Priors for Function Selection in Structured Additive Regression Models." Journal of the American Statistical Association 107, no. 500 (October 17, 2012): 1518–32. http://dx.doi.org/10.1080/01621459.2012.737742.

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Yen, Tso-Jung. "A majorization–minimization approach to variable selection using spike and slab priors." Annals of Statistics 39, no. 3 (June 2011): 1748–75. http://dx.doi.org/10.1214/11-aos884.

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Koch, Brandon, David M. Vock, Julian Wolfson, and Laura Boehm Vock. "Variable selection and estimation in causal inference using Bayesian spike and slab priors." Statistical Methods in Medical Research 29, no. 9 (January 15, 2020): 2445–69. http://dx.doi.org/10.1177/0962280219898497.

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Unbiased estimation of causal effects with observational data requires adjustment for confounding variables that are related to both the outcome and treatment assignment. Standard variable selection techniques aim to maximize predictive ability of the outcome model, but they ignore covariate associations with treatment and may not adjust for important confounders weakly associated to outcome. We propose a novel method for estimating causal effects that simultaneously considers models for both outcome and treatment, which we call the bilevel spike and slab causal estimator (BSSCE). By using a Bayesian formulation, BSSCE estimates the posterior distribution of all model parameters and provides straightforward and reliable inference. Spike and slab priors are used on each covariate coefficient which aim to minimize the mean squared error of the treatment effect estimator. Theoretical properties of the treatment effect estimator are derived justifying the prior used in BSSCE. Simulations show that BSSCE can substantially reduce mean squared error over numerous methods and performs especially well with large numbers of covariates, including situations where the number of covariates is greater than the sample size. We illustrate BSSCE by estimating the causal effect of vasoactive therapy vs. fluid resuscitation on hypotensive episode length for patients in the Multiparameter Intelligent Monitoring in Intensive Care III critical care database.

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Xi, Ruibin, Yunxiao Li, and Yiming Hu. "Bayesian Quantile Regression Based on the Empirical Likelihood with Spike and Slab Priors." Bayesian Analysis 11, no. 3 (September 2016): 821–55. http://dx.doi.org/10.1214/15-ba975.

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Legramanti, Sirio, Daniele Durante, and David B. Dunson. "Bayesian cumulative shrinkage for infinite factorizations." Biometrika 107, no. 3 (May 27, 2020): 745–52. http://dx.doi.org/10.1093/biomet/asaa008.

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Summary The dimension of the parameter space is typically unknown in a variety of models that rely on factorizations. For example, in factor analysis the number of latent factors is not known and has to be inferred from the data. Although classical shrinkage priors are useful in such contexts, increasing shrinkage priors can provide a more effective approach that progressively penalizes expansions with growing complexity. In this article we propose a novel increasing shrinkage prior, called the cumulative shrinkage process, for the parameters that control the dimension in overcomplete formulations. Our construction has broad applicability and is based on an interpretable sequence of spike-and-slab distributions which assign increasing mass to the spike as the model complexity grows. Using factor analysis as an illustrative example, we show that this formulation has theoretical and practical advantages relative to current competitors, including an improved ability to recover the model dimension. An adaptive Markov chain Monte Carlo algorithm is proposed, and the performance gains are outlined in simulations and in an application to personality data.

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Yi, Jieyi, and Niansheng Tang. "Variational Bayesian Inference in High-Dimensional Linear Mixed Models." Mathematics 10, no. 3 (January 31, 2022): 463. http://dx.doi.org/10.3390/math10030463.

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In high-dimensional regression models, the Bayesian lasso with the Gaussian spike and slab priors is widely adopted to select variables and estimate unknown parameters. However, it involves large matrix computations in a standard Gibbs sampler. To solve this issue, the Skinny Gibbs sampler is employed to draw observations required for Bayesian variable selection. However, when the sample size is much smaller than the number of variables, the computation is rather time-consuming. As an alternative to the Skinny Gibbs sampler, we develop a variational Bayesian approach to simultaneously select variables and estimate parameters in high-dimensional linear mixed models under the Gaussian spike and slab priors of population-specific fixed-effects regression coefficients, which are reformulated as a mixture of a normal distribution and an exponential distribution. The coordinate ascent algorithm, which can be implemented efficiently, is proposed to optimize the evidence lower bound. The Bayes factor, which can be computed with the path sampling technique, is presented to compare two competing models in the variational Bayesian framework. Simulation studies are conducted to assess the performance of the proposed variational Bayesian method. An empirical example is analyzed by the proposed methodologies.

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Dissertations / Theses on the topic "Spike-and-slab priors"

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Agarwal, Anjali. "Bayesian variable selection with spike-and-slab priors." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461940937.

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Naveau, Marion. "Procédures de sélection de variables en grande dimension dans les modèles non-linéaires à effets mixtes. Application en amélioration des plantes." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASM031.

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Les modèles à effets mixtes analysent des observations collectées de façon répétée sur plusieurs individus, attribuant la variabilité à différentes sources (intra-individuelle, inter-individuelle, résiduelle). Prendre en compte cette variabilité est essentiel pour caractériser sans biais les mécanismes biologiques sous-jacents. Ces modèles utilisent des covariables et des effets aléatoires pour décrire la variabilité entre individus : les covariables décrivent les différences dues à des caractéristiques observées, tandis que les effets aléatoires représentent la variabilité non attribuable aux covariables mesurées. Dans un contexte de grande dimension, où le nombre de covariables dépasse celui des individus, identifier les covariables influentes est difficile, car la sélection porte sur des variables latentes du modèle. De nombreuses procédures ont été mises au point pour les modèles linéaires à effets mixtes, mais les contributions pour les modèles non-linéaires sont rares et manquent de fondements théoriques. Cette thèse vise à développer une procédure de sélection de covariables en grande dimension pour les modèles non-linéaires à effets mixtes, en étudiant leurs implémentations pratiques et leurs propriétés théoriques. Cette procédure est basée sur l'utilisation d'un prior spike-and-slab gaussien et de l'algorithme SAEM (Stochastic Approximation of Expectation Maximisation Algorithm). Des taux de contraction a posteriori autour des vraies valeurs des paramètres dans un modèle non-linéaire à effets mixtes sous prior spike-and-slab discret ont été obtenus, comparables à ceux observés dans des modèles linéaires. Les travaux conduits dans cette thèse sont motivés par des questions appliquées en amélioration des plantes, où ces modèles décrivent le développement des plantes en fonction de leurs génotypes et des conditions environnementales. Les covariables considérées sont généralement nombreuses puisque les variétés sont caractérisées par des milliers de marqueurs génétiques, dont la plupart n'ont aucun effet sur certains traits phénotypiques. La méthode statistique développée dans la thèse est appliquée à un jeu de données réel relatif à cette application
Mixed-effects models analyze observations collected repeatedly from several individuals, attributing variability to different sources (intra-individual, inter-individual, residual). Accounting for this variability is essential to characterize the underlying biological mechanisms without biais. These models use covariates and random effects to describe variability among individuals: covariates explain differences due to observed characteristics, while random effects represent the variability not attributable to measured covariates. In high-dimensional context, where the number of covariates exceeds the number of individuals, identifying influential covariates is challenging, as selection focuses on latent variables in the model. Many procedures have been developed for linear mixed-effects models, but contributions for non-linear models are rare and lack theoretical foundations. This thesis aims to develop a high-dimensional covariate selection procedure for non-linear mixed-effects models by studying their practical implementations and theoretical properties. This procedure is based on the use of a gaussian spike-and-slab prior and the SAEM algorithm (Stochastic Approximation of Expectation Maximisation Algorithm). Posterior contraction rates around true parameter values in a non-linear mixed-effects model under a discrete spike-and-slab prior have been obtained, comparable to those observed in linear models. The work in this thesis is motivated by practical questions in plant breeding, where these models describe plant development as a function of their genotypes and environmental conditions. The considered covariates are generally numerous since varieties are characterized by thousands of genetic markers, most of which have no effect on certain phenotypic traits. The statistical method developed in the thesis is applied to a real dataset related to this application

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Mismer, Romain. "Convergence et spike and Slab Bayesian posterior distributions in some high dimensional models." Thesis, Sorbonne Paris Cité, 2019. http://www.theses.fr/2019USPCC064.

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On s'intéresse d'abord au modèle de suite gaussienne parcimonieuse. Une approche bayésienne empirique sur l'a priori Spike and Slab permet d'obtenir la convergence à vitesse minimax du moment d'ordre 2 a posteriori pour des Slabs Cauchy et on prouve un résultat de sous-optimalité pour un Slab Laplace. Un meilleur choix de Slab permet d'obtenir la constante exacte. Dans le modèle d'estimation de densité, un a priori arbre de Polya tel que les variables de l'arbre ont une distribution de type Spike and Slab donne la convergence à vitesse minimax et adaptative pour la norme sup de la loi a posteriori et un théorème Bernstein-von Mises non paramétrique
The first main focus is the sparse Gaussian sequence model. An Empirical Bayes approach is used on the Spike and Slab prior to derive minimax convergence of the posterior second moment for Cauchy Slabs and a suboptimality result for the Laplace Slab is proved. Next, with a special choice of Slab convergence with the sharp minimax constant is derived. The second main focus is the density estimation model using a special Polya tree prior where the variables in the tree construction follow a Spike and Slab type distribution. Adaptive minimax convergence in the supremum norm of the posterior distribution as well as a nonparametric Bernstein-von Mises theorem are obtained

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Sharp, Kevin John. "Effective Bayesian inference for sparse factor analysis models." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/effective-bayesian-inference-for-sparse-factor-analysis-models(4facfde0-0aae-4f09-aeaa-960111e854ff).html.

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We study how to perform effective Bayesian inference in high-dimensional sparse Factor Analysis models with a zero-norm, sparsity-inducing prior on the model parameters. Such priors represent a methodological ideal, but Bayesian inference in such models is usually regarded as impractical. We test this view. After empirically characterising the properties of existing algorithmic approaches, we use techniques from statistical mechanics to derive a theory of optimal learning in the restricted setting of sparse PCA with a single factor. Finally, we describe a novel `Dense Message Passing' algorithm (DMP) which achieves near-optimal performance on synthetic data generated from this model.DMP exploits properties of high-dimensional problems to operate successfully on a densely connected graphical model. Similar algorithms have been developed in the statistical physics community and previously applied to inference problems in coding and sparse classification. We demonstrate that DMP out-performs both a newly proposed variational hybrid algorithm and two other recently published algorithms (SPCA and emPCA) on synthetic data while it explains at least the same amount of variance, for a given level of sparsity, in two gene expression datasets used in previous studies of sparse PCA.A significant potential advantage of DMP is that it provides an estimate of the marginal likelihood which can be used for hyperparameter optimisation. We show that, for the single factor case, this estimate exhibits good qualitative agreement both with theoretical predictions and with the hyperparameter posterior inferred by a collapsed Gibbs sampler. Preliminary work on an extension to inference of multiple factors indicates its potential for selecting an optimal model from amongst candidates which differ both in numbers of factors and their levels of sparsity.

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Xu, Lizhen. "Bayesian Methods for Genetic Association Studies." Thesis, 2012. http://hdl.handle.net/1807/34972.

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We develop statistical methods for tackling two important problems in genetic association studies. First, we propose a Bayesian approach to overcome the winner's curse in genetic studies. Second, we consider a Bayesian latent variable model for analyzing longitudinal family data with pleiotropic phenotypes. Winner's curse in genetic association studies refers to the estimation bias of the reported odds ratios (OR) for an associated genetic variant from the initial discovery samples. It is a consequence of the sequential procedure in which the estimated effect of an associated genetic marker must first pass a stringent significance threshold. We propose a hierarchical Bayes method in which a spike-and-slab prior is used to account for the possibility that the significant test result may be due to chance. We examine the robustness of the method using different priors corresponding to different degrees of confidence in the testing results and propose a Bayesian model averaging procedure to combine estimates produced by different models. The Bayesian estimators yield smaller variance compared to the conditional likelihood estimator and outperform the latter in the low power studies. We investigate the performance of the method with simulations and applications to four real data examples. Pleiotropy occurs when a single genetic factor influences multiple quantitative or qualitative phenotypes, and it is present in many genetic studies of complex human traits. The longitudinal family studies combine the features of longitudinal studies in individuals and cross-sectional studies in families. Therefore, they provide more information about the genetic and environmental factors associated with the trait of interest. We propose a Bayesian latent variable modeling approach to model multiple phenotypes simultaneously in order to detect the pleiotropic effect and allow for longitudinal and/or family data. An efficient MCMC algorithm is developed to obtain the posterior samples by using hierarchical centering and parameter expansion techniques. We apply spike and slab prior methods to test whether the phenotypes are significantly associated with the latent disease status. We compute Bayes factors using path sampling and discuss their application in testing the significance of factor loadings and the indirect fixed effects. We examine the performance of our methods via extensive simulations and apply them to the blood pressure data from a genetic study of type 1 diabetes (T1D) complications.

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Book chapters on the topic "Spike-and-slab priors"

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Vannucci, Marina. "Discrete Spike-and-Slab Priors: Models and Computational Aspects." In Handbook of Bayesian Variable Selection, 3–24. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003089018-1.

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Narisetty, Naveen N. "Theoretical and Computational Aspects of Continuous Spike-and-Slab Priors." In Handbook of Bayesian Variable Selection, 57–80. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003089018-3.

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Zhou, Shuang, and Debdeep Pati. "Recent Theoretical Advances with the Discrete Spike-and-Slab Priors." In Handbook of Bayesian Variable Selection, 25–56. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003089018-2.

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Wu, Shengyi, Kaito Shimamura, Kohei Yoshikawa, Kazuaki Murayama, and Shuichi Kawano. "Variable Fusion for Bayesian Linear Regression via Spike-and-slab Priors." In Intelligent Decision Technologies, 491–501. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2765-1_41.

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Nayek, Rajdip, Keith Worden, and Elizabeth J. Cross. "Equation Discovery Using an Efficient Variational Bayesian Approach with Spike-and-Slab Priors." In Model Validation and Uncertainty Quantification, Volume 3, 149–61. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77348-9_19.

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Conference papers on the topic "Spike-and-slab priors"

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Suo, Yuanming, Minh Dao, Trac Tran, Umamahesh Srinivas, and Vishal Monga. "Hierarchical sparse modeling using Spike and Slab priors." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638229.

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Monga, Vishal. "Sparsity constrained estimation via spike and slab priors." In 2017 51st Annual Conference on Information Sciences and Systems (CISS). IEEE, 2017. http://dx.doi.org/10.1109/ciss.2017.7926168.

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Fang, Shikai, Shandian Zhe, Kuang-chih Lee, Kai Zhang, and Jennifer Neville. "Online Bayesian Sparse Learning with Spike and Slab Priors." In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00023.

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Mousavi, Hojjat S., Umamahesh Srinivas, Vishal Monga, Yuanming Suo, Minh Dao, and Trac D. Tran. "Multi-task image classification via collaborative, hierarchical spike-and-slab priors." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025860.

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Shuku, T., and K. K. Phoon. "Bayesian Estimation for Subsurface Models using Spike-and-Slab Prior." In 8th International Symposium on Reliability Engineering and Risk Management. Singapore: Research Publishing Services, 2022. http://dx.doi.org/10.3850/978-981-18-5184-1_ms-13-045-cd.

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Liu, Yuhang, Wenyong Dong, Wanjuan Song, and Lei Zhang. "Bayesian Nonnegative Matrix Factorization with a Truncated Spike-and-Slab Prior." In 2019 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2019. http://dx.doi.org/10.1109/icme.2019.00251.

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Lv, Fuzai, Changhao Zhang, Zhifeng Tang, and Pengfei Zhang. "Block-Sparse Signal Recovery Based on Adaptive Matching Pursuit via Spike and Slab Prior." In 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2020. http://dx.doi.org/10.1109/sam48682.2020.9104311.

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Zhang, Xiaoxu, Li Hao, and Jiaqi Liu. "Spike and Slab Prior Based Joint Sparse Channel Estimation and Multiuser Detection in MTC Communications." In 2020 International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2020. http://dx.doi.org/10.1109/wcsp49889.2020.9299766.

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Sun, Weitian, Lei Yang, Yuchen Dou, Xuan Li, and Cheng Fang. "Auto-focused Sparse Bayesian Learning for ISAR Imagery Based on Spike-and-Slab Prior Via Variational Approximation." In 2021 International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE, 2021. http://dx.doi.org/10.1109/iccais52680.2021.9624613.

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