Academic literature on the topic 'Priors spike-and-slab'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Priors spike-and-slab.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Priors spike-and-slab"
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.
Full textRockova, 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.
Full textAntonelli, 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.
Full textHernández-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.
Full textScheipl, 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.
Full textYen, 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.
Full textKoch, 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.
Full textXi, 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.
Full textLegramanti, 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.
Full textYi, 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.
Full textDissertations / Theses on the topic "Priors spike-and-slab"
Agarwal, Anjali. "Bayesian variable selection with spike-and-slab priors." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461940937.
Full textNaveau, 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.
Full textMixed-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
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.
Full textThe 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
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.
Full textXu, Lizhen. "Bayesian Methods for Genetic Association Studies." Thesis, 2012. http://hdl.handle.net/1807/34972.
Full textBook chapters on the topic "Priors spike-and-slab"
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.
Full textNarisetty, 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.
Full textZhou, 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.
Full textWu, 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.
Full textNayek, 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.
Full textConference papers on the topic "Priors spike-and-slab"
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.
Full textMonga, 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.
Full textFang, 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.
Full textMousavi, 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.
Full textShuku, 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.
Full textLiu, 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.
Full textLv, 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.
Full textZhang, 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.
Full textSun, 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.
Full text