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Relevant bibliographies by topics / Priors spike-and-slab

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Academic literature on the topic 'Priors spike-and-slab'

Author: Grafiati

Published: 5 October 2024

Last updated: 19 July 2025

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Journal articles on the topic "Priors spike-and-slab"

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Ročková, Veronika, and Edward I. George. "Negotiating multicollinearity with spike-and-slab priors." METRON 72, no. 2 (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 (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 (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 (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 (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 (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 B

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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 (2016): 821–55. http://dx.doi.org/10.1214/15-ba975.

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Zhang, Juanjuan, Weixian Wang, Mingming Yang, and Maozai Tian. "Variational Bayesian Variable Selection in Logistic Regression Based on Spike-and-Slab Lasso." Mathematics 13, no. 13 (2025): 2205. https://doi.org/10.3390/math13132205.

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Logistic regression is often used to solve classification problems. This article combines the advantages of Bayesian methods and spike-and-slab Lasso to select variables in high-dimensional logistic regression. The method of introducing a new hidden variable or approximating the lower bound is used to solve the problem of logistic functions without conjugate priors. The Laplace distribution in spike-and-slab Lasso is expressed as a hierarchical form of normal distribution and exponential distribution, so that all parameters in the model are posterior distributions that are easy to deal with. C

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Yi, Jieyi, and Niansheng Tang. "Variational Bayesian Inference in High-Dimensional Linear Mixed Models." Mathematics 10, no. 3 (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 v

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Dissertations / Theses on the topic "Priors spike-and-slab"

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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

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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 poster

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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' algorith

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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 mar

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Book chapters on the topic "Priors spike-and-slab"

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Vannucci, Marina. "Discrete Spike-and-Slab Priors: Models and Computational Aspects." In Handbook of Bayesian Variable Selection. 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. 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. 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. 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. 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 "Priors spike-and-slab"

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Masuda, Tatsuki, Kei Nakagawa, and Takahiro Hoshino. "Dynamic Dual Sparse Topic Model: Integrating Temporal Dynamics and Sparsity with Spike and Slab Priors into Topic Model." In 2024 16th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2024. http://dx.doi.org/10.1109/iiai-aai63651.2024.00063.

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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. 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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