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

Author: Grafiati

Published: 5 October 2024

Last updated: 31 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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9

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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Legramanti, Sirio, Daniele Durante, and David B. Dunson. "Bayesian cumulative shrinkage for infinite factorizations." Biometrika 107, no. 3 (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 formulat

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12

Chen, Su, and Stephen G. Walker. "Fast Bayesian variable selection for high dimensional linear models: Marginal solo spike and slab priors." Electronic Journal of Statistics 13, no. 1 (2019): 284–309. http://dx.doi.org/10.1214/18-ejs1529.

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13

Leach, Justin M., Lloyd J. Edwards, Rajesh Kana, Kristina Visscher, Nengjun Yi, and Inmaculada Aban. "The spike-and-slab elastic net as a classification tool in Alzheimer’s disease." PLOS ONE 17, no. 2 (2022): e0262367. http://dx.doi.org/10.1371/journal.pone.0262367.

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Alzheimer’s disease (AD) is the leading cause of dementia and has received considerable research attention, including using neuroimaging biomarkers to classify patients and/or predict disease progression. Generalized linear models, e.g., logistic regression, can be used as classifiers, but since the spatial measurements are correlated and often outnumber subjects, penalized and/or Bayesian models will be identifiable, while classical models often will not. Many useful models, e.g., the elastic net and spike-and-slab lasso, perform automatic variable selection, which removes extraneous predicto

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14

Nayek, R., R. Fuentes, K. Worden, and E. J. Cross. "On spike-and-slab priors for Bayesian equation discovery of nonlinear dynamical systems via sparse linear regression." Mechanical Systems and Signal Processing 161 (December 2021): 107986. http://dx.doi.org/10.1016/j.ymssp.2021.107986.

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15

Fan, Yue, Xiao Wang, and Qinke Peng. "Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information." Computational and Mathematical Methods in Medicine 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/8307530.

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Gene regulatory networks (GRNs) play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are u

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16

Mohammed, Shariq, Dipak K. Dey, and Yuping Zhang. "Bayesian variable selection using spike‐and‐slab priors with application to high dimensional electroencephalography data by local modelling." Journal of the Royal Statistical Society: Series C (Applied Statistics) 68, no. 5 (2019): 1305–26. http://dx.doi.org/10.1111/rssc.12369.

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17

Liu, Yunli, and Xin Tong. "A Tutorial on Bayesian Linear Regression with Compositional Predictors Using JAGS." Journal of Behavioral Data Science 4, no. 1 (2024): 1–24. http://dx.doi.org/10.35566/jbds/tongliu.

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This tutorial offers an exploration of advanced Bayesian methodologies for compositional data analysis, specifically the Bayesian Lasso and Bayesian Spike-and-Slab Lasso (SSL) techniques. Our focus is on a novel Bayesian methodology that integrates Lasso and SSL priors, enhancing both parameter estimation and variable selection for linear regression with compositional predictors. The tutorial is structured to streamline the learning process, breaking down complex analyses into a series of straightforward steps. We demonstrate these methods using R and JAGS, employing simulated datasets to illu

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18

Brandt, Holger, Jenna Cambria, and Augustin Kelava. "An Adaptive Bayesian Lasso Approach with Spike-and-Slab Priors to Identify Multiple Linear and Nonlinear Effects in Structural Equation Models." Structural Equation Modeling: A Multidisciplinary Journal 25, no. 6 (2018): 946–60. http://dx.doi.org/10.1080/10705511.2018.1474114.

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19

Bassetti, Federico, and Lucia Ladelli. "Mixture of Species Sampling Models." Mathematics 9, no. 23 (2021): 3127. http://dx.doi.org/10.3390/math9233127.

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We introduce mixtures of species sampling sequences (mSSS) and discuss how these sequences are related to various types of Bayesian models. As a particular case, we recover species sampling sequences with general (not necessarily diffuse) base measures. These models include some “spike-and-slab” non-parametric priors recently introduced to provide sparsity. Furthermore, we show how mSSS arise while considering hierarchical species sampling random probabilities (e.g., the hierarchical Dirichlet process). Extending previous results, we prove that mSSS are obtained by assigning the values of an e

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20

Martínez, Carlos Alberto, Kshitij Khare, Arunava Banerjee, and Mauricio A. Elzo. "Joint genome-wide prediction in several populations accounting for randomness of genotypes: A hierarchical Bayes approach. II: Multivariate spike and slab priors for marker effects and derivation of approximate Bayes and fractional Bayes factors for the complete family of models." Journal of Theoretical Biology 417 (March 2017): 131–41. http://dx.doi.org/10.1016/j.jtbi.2016.12.022.

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21

Lu, Xiaoqiang, Yuan Yuan, and Pingkun Yan. "Sparse coding for image denoising using spike and slab prior." Neurocomputing 106 (April 2013): 12–20. http://dx.doi.org/10.1016/j.neucom.2012.09.014.

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22

Haller, Olivia C., Tricia Z. King, Xin Ma, Negar Fani, and Suprateek Kundu. "5 White Matter Tract Shape as a Predictor of PTSD Symptom Severity in Trauma-Exposed Black American Women." Journal of the International Neuropsychological Society 29, s1 (2023): 519–20. http://dx.doi.org/10.1017/s1355617723006690.

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Objective:Machine learning studies of PTSD show promise for identifying neurobiological signatures of this disorder, but studies to date have largely excluded Black American women, who experience disproportionately greater trauma and have relatively higher rates of PTSD. PTSD is characterized by four symptom clusters: trauma reexperiencing, trauma avoidance, hyperarousal, and anhedonia. A prior machine learning study reported successful PTSD symptom cluster severity prediction using functional MRI data but did not examine white matter predictors. White matter microstructural integrity has been

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23

Ročková, Veronika. "Bayesian estimation of sparse signals with a continuous spike-and-slab prior." Annals of Statistics 46, no. 1 (2018): 401–37. http://dx.doi.org/10.1214/17-aos1554.

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24

Canale, A., A. Lijoi, B. Nipoti, and I. Prünster. "On the Pitman–Yor process with spike and slab base measure." Biometrika 104, no. 3 (2017): 681–97. http://dx.doi.org/10.1093/biomet/asx041.

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Summary For the most popular discrete nonparametric models, beyond the Dirichlet process, the prior guess at the shape of the data-generating distribution, also known as the base measure, is assumed to be diffuse. Such a specification greatly simplifies the derivation of analytical results, allowing for a straightforward implementation of Bayesian nonparametric inferential procedures. However, in several applied problems the available prior information leads naturally to the incorporation of an atom into the base measure, and then the Dirichlet process is essentially the only tractable choice

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25

Serra, Juan G., Javier Mateos, Rafael Molina, and Aggelos K. Katsaggelos. "Variational EM method for blur estimation using the spike-and-slab image prior." Digital Signal Processing 88 (May 2019): 116–29. http://dx.doi.org/10.1016/j.dsp.2019.01.004.

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26

Zhang, Qi, Yihui Zhang, and Yemao Xia. "Bayesian Feature Extraction for Two-Part Latent Variable Model with Polytomous Manifestations." Mathematics 12, no. 5 (2024): 783. http://dx.doi.org/10.3390/math12050783.

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Semi-continuous data are very common in social sciences and economics. In this paper, a Bayesian variable selection procedure is developed to assess the influence of observed and/or unobserved exogenous factors on semi-continuous data. Our formulation is based on a two-part latent variable model with polytomous responses. We consider two schemes for the penalties of regression coefficients and factor loadings: a Bayesian spike and slab bimodal prior and a Bayesian lasso prior. Within the Bayesian framework, we implement a Markov chain Monte Carlo sampling method to conduct posterior inference.

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27

Mohammed, Shariq, Dipak K. Dey, and Yuping Zhang. "Classification of high‐dimensional electroencephalography data with location selection using structured spike‐and‐slab prior." Statistical Analysis and Data Mining: The ASA Data Science Journal 13, no. 5 (2020): 465–81. http://dx.doi.org/10.1002/sam.11477.

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28

Teixeira, Josiele da Silva, Helio dos Santos Migon, Leonardo Tavares Stutz, Diego Campos Knupp, and Antônio José da Silva Neto. "Structural Damage Identification via Bayesian Inference with a New Hierarchical Modeling and Spike-and-Slab Prior." Ciência e Natura 46, esp. 1 (2024): e87212. https://doi.org/10.5902/2179460x87212.

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The present work aims to formulate and solve the inverse problem of structural damage identification using Bayesian Inference. In the solution of the direct problem, the Finite Element Method (FEM) is considered. The modeling of the damage field is performed through the cohesion parameter, which continuously describes the integrity of the structure. The damage identification problem is formulated as an inverse parameter estimation problem, where the posterior probability distribution of the cohesion parameters is sampled using the Adaptive Markov Chain Monte Carlo method and a Spike-Slab prior

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29

Meng, Xiangming, Sheng Wu, Michael Riis Andersen, Jiang Zhu, and Zuyao Ni. "Efficient recovery of structured sparse signals via approximate message passing with structured spike and slab prior." China Communications 15, no. 6 (2018): 1–17. http://dx.doi.org/10.1109/cc.2018.8398220.

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30

Thomson, W., S. Jabbari, A. E. Taylor, W. Arlt, and D. J. Smith. "Simultaneous parameter estimation and variable selection via the logit-normal continuous analogue of the spike-and-slab prior." Journal of The Royal Society Interface 16, no. 150 (2019): 20180572. http://dx.doi.org/10.1098/rsif.2018.0572.

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We introduce a Bayesian prior distribution, the logit-normal continuous analogue of the spike-and-slab, which enables flexible parameter estimation and variable/model selection in a variety of settings. We demonstrate its use and efficacy in three case studies—a simulation study and two studies on real biological data from the fields of metabolomics and genomics. The prior allows the use of classical statistical models, which are easily interpretable and well known to applied scientists, but performs comparably to common machine learning methods in terms of generalizability to previously unsee

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31

Zhu, Xiaowei, Yu Han, Shichong Li, and Xinyin Wang. "A spatial-temporal topic model with sparse prior and RNN prior for bursty topic discovering in social networks." Journal of Intelligent & Fuzzy Systems 42, no. 4 (2022): 3909–22. http://dx.doi.org/10.3233/jifs-212135.

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With the rapid growth of social network users, the social network has accumulated massive social network topics. However, due to the randomness of content, it becomes sparse and noisy, accompanied by many daily chats and meaningless topics, which brings challenges to bursty topics discovery. To deal with these problems, this paper proposes the spatial-temporal topic model with sparse prior and recurrent neural networks (RNN) prior for bursty topic discovering (ST-SRTM). The semantic relationship of words is learned through RNN to alleviate the sparsity. The spatial-temporal areas information i

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32

Marnissi, Yosra, Yasmine Hawwari, Amadou Assoumane, Dany Abboud, and Mohamed El-Badaoui. "On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals." Applied Sciences 11, no. 6 (2021): 2626. http://dx.doi.org/10.3390/app11062626.

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The compressive sensing (CS) of mechanical signals is an emerging research topic for remote condition monitoring. The signals generated by machines are mostly periodic due to the rotating nature of its components. Often, these vibrations witness strong interactions among two or multiple rotating sources, leading to modulation phenomena. This paper is specifically concerned with the CS of this particular class of signals using a Bayesian approach. The main contribution of this paper is to consider the particular spectral structure of these signals through two families of hierarchical models. Th

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Zhang, Juanjuan, Weixian Wang, and Maozai Tian. "Variational Bayesian Quantile Regression with Non-Ignorable Missing Response Data." Axioms 14, no. 6 (2025): 408. https://doi.org/10.3390/axioms14060408.

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For non-ignorable missing response variables, the mechanism of whether the response variable is missing can be modeled through logistic regression. In Bayesian computation, the lack of a conjugate prior for the logistic function poses a significant challenge. Introducing a new Pólya-Gamma variable and employing lower-bound approximation are two common methods for parameter inference in conjugate Bayesian logistic regression. It can be observed that these two methods yield essentially the same variational posterior in the calculation of the variational Bayesian posterior. This paper applies a p

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34

Chen, Xian Bo, Xing Hao Ding, and Hui Liu. "MRI Denoising Based on a Non-Parametric Bayesian Image Sparse Representation Method." Advanced Materials Research 219-220 (March 2011): 1354–58. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1354.

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Magnetic Resonance images are often corrupted by Gaussian noise which highly affects the quality of MR images. In this paper, a Non-Parametric hierarchical Bayesian image sparse representation method is proposed to wipe out Gaussian distribution noise coupling in MR images. In this method a spike-slab prior is imposed on sparse coefficients, and a redundant dictionary is learned from the corrupted image. Experimental results show that the method not only improves the effect of MRI denoising, but also can obtain good estimation of the noise variance. Compared to non-local filter method, this mo

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35

Xu, Chendong, and Qisong Wu. "High-Resolution Through-the-Wall Radar Imaging with Exploitation of Target Structure." Applied Sciences 12, no. 22 (2022): 11684. http://dx.doi.org/10.3390/app122211684.

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It is quite challenging for through-the-wall radar imaging (TWRI) to achieve high-resolution ghost-free imaging with limited measurements in an indoor multipath scenario. In this paper, a novel high-resolution TWRI algorithm with the exploitation of the target clustered structure in a hierarchical Bayesian framework is proposed. More specifically, an extended spike-and-slab clustered prior is imposed to statistically encourage the cluster formations in both downrange and crossrange domains of the target region, and a generative model of the proposed approach is provided. Then, a Markov Chain M

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36

Zhao, Yuanying, and Dengke Xu. "A Bayesian Variable Selection Method for Spatial Autoregressive Quantile Models." Mathematics 11, no. 4 (2023): 987. http://dx.doi.org/10.3390/math11040987.

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In this paper, a Bayesian variable selection method for spatial autoregressive (SAR) quantile models is proposed on the basis of spike and slab prior for regression parameters. The SAR quantile models, which are more generalized than SAR models and quantile regression models, are specified by adopting the asymmetric Laplace distribution for the error term in the classical SAR models. The proposed approach could perform simultaneously robust parametric estimation and variable selection in the context of SAR quantile models. Bayesian statistical inferences are implemented by a detailed Markov ch

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37

Ding, Xing Hao, and Xian Bo Chen. "Image Sparse Representation Based on a Nonparametric Bayesian Model." Applied Mechanics and Materials 103 (September 2011): 109–14. http://dx.doi.org/10.4028/www.scientific.net/amm.103.109.

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In recent years there has been a growing interest in the research of image sparse representation. Sparse representation based on over-complete dictionary become another hot topic in the field of image processing. In this paper a Nonparametric Bayesian model based on hierarchical Bayesian theory is proposed. In this model a sparse spike-slab prior is imposed on sparse coefficients and the Non-parametric Bayesian techniques based on sparse image representation are considering for learning dictionary. Proposed model can learn an over-complete dictionary from original image. Furthermore, the unkno

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38

Culpepper, Steven Andrew, and Yinghan Chen. "Development and Application of an Exploratory Reduced Reparameterized Unified Model." Journal of Educational and Behavioral Statistics 44, no. 1 (2018): 3–24. http://dx.doi.org/10.3102/1076998618791306.

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Exploratory cognitive diagnosis models (CDMs) estimate the Q matrix, which is a binary matrix that indicates the attributes needed for affirmative responses to each item. Estimation of Q is an important next step for improving classifications and broadening application of CDMs. Prior research primarily focused on an exploratory version of the restrictive deterministic-input, noisy-and-gate model, and research is needed to develop exploratory methods for more flexible CDMs. We consider Bayesian methods for estimating an exploratory version of the more flexible reduced reparameterized unified mo

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Shi, Lei, Junping Du, and Feifei Kou. "A Sparse Topic Model for Bursty Topic Discovery in Social Networks." International Arab Journal of Information Technology 17, no. 5 (2020): 816–24. http://dx.doi.org/10.34028/iajit/17/5/15.

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Bursty topic discovery aims to automatically identify bursty events and continuously keep track of known events. The existing methods focus on the topic model. However, the sparsity of short text brings the challenge to the traditional topic models because the words are too few to learn from the original corpus. To tackle this problem, we propose a Sparse Topic Model (STM) for bursty topic discovery. First, we distinguish the modeling between the bursty topic and the common topic to detect the change of the words in time and discover the bursty words. Second, we introduce “Spike and Slab” prio

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Shaddox, Elin, Christine B. Peterson, Francesco C. Stingo, et al. "Bayesian inference of networks across multiple sample groups and data types." Biostatistics 21, no. 3 (2018): 561–76. http://dx.doi.org/10.1093/biostatistics/kxy078.

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Summary In this article, we develop a graphical modeling framework for the inference of networks across multiple sample groups and data types. In medical studies, this setting arises whenever a set of subjects, which may be heterogeneous due to differing disease stage or subtype, is profiled across multiple platforms, such as metabolomics, proteomics, or transcriptomics data. Our proposed Bayesian hierarchical model first links the network structures within each platform using a Markov random field prior to relate edge selection across sample groups, and then links the network similarity param

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41

Frühwirth-Schnatter, Sylvia. "Generalized cumulative shrinkage process priors with applications to sparse Bayesian factor analysis." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 381, no. 2247 (2023). http://dx.doi.org/10.1098/rsta.2022.0148.

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The paper discusses shrinkage priors which impose increasing shrinkage in a sequence of parameters. We review the cumulative shrinkage process (CUSP) prior of Legramanti et al. (Legramanti et al . 2020 Biometrika 107 , 745–752. ( doi:10.1093/biomet/asaa008 )), which is a spike-and-slab shrinkage prior where the spike probability is stochastically increasing and constructed from the stick-breaking representation of a Dirichlet process prior. As a first contribution, this CUSP prior is extended by involving arbitrary stick-breaking representations arising from beta distributions. As a second con

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42

Malsiner-Walli, Gertraud, and Helga Wagner. "Comparing Spike and Slab Priors for Bayesian Variable Selection." Austrian Journal of Statistics 40, no. 4 (2016). http://dx.doi.org/10.17713/ajs.v40i4.215.

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An important task in building regression models is to decide which regressors should be included in the final model. In a Bayesian approach, variable selection can be performed using mixture priors with a spike and a slab component for the effects subject to selection. As the spike is concentrated at zero, variable selection is based on the probability of assigning the corresponding regression effect to the slab component. These posterior inclusion probabilities can be determined by MCMC sampling. In this paper we compare the MCMC implementations for several spike and slab priors with regard t

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43

Ohigashi, Tomohiro, Kazushi Maruo, Takashi Sozu, Ryo Sawamoto, and Masahiko Gosho. "Potential Bias Models With Bayesian Shrinkage Priors for Dynamic Borrowing of Multiple Historical Control Data." Pharmaceutical Statistics, November 17, 2024. http://dx.doi.org/10.1002/pst.2453.

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ABSTRACTWhen multiple historical controls are available, it is necessary to consider the conflicts between current and historical controls and the relationships among historical controls. One of the assumptions concerning the relationships between the parameters of interest of current and historical controls is known as the “Potential biases.” Within the “Potential biases” assumption, the differences between the parameters of interest of the current control and of each historical control are defined as “potential bias parameters.” We define a class of models called “potential biases model” tha

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44

Samorodnitsky, Sarah, Katherine A. Hoadley, and Eric F. Lock. "A hierarchical spike-and-slab model for pan-cancer survival using pan-omic data." BMC Bioinformatics 23, no. 1 (2022). http://dx.doi.org/10.1186/s12859-022-04770-3.

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Abstract Background Pan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict clinical outcomes. We address the issue of prediction across multiple high-dimensional sources of data and sample sets by using molecular patterns identified by BIDIFAC+, a method for integrative dimension reduction of bidimensionally-linked matrices, in a Bayesian hierarchical model. Ou

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Cappello, Lorenzo, Oscar Hernan Madrid Padilla, and Julia A. Palacios. "Bayesian change point detection with spike and slab priors." Journal of Computational and Graphical Statistics, February 21, 2023, 1–24. http://dx.doi.org/10.1080/10618600.2023.2182312.

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46

Antonelli, Joseph, Ander Wilson, and Brent Coull. "Bayesian distributed lag interaction models using spike and slab priors." ISEE Conference Abstracts 2021, no. 1 (2021). http://dx.doi.org/10.1289/isee.2021.o-sy-069.

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47

Jantre, Sanket, Shrijita Bhattacharya, and Tapabrata Maiti. "Spike-and-Slab Shrinkage Priors for Structurally Sparse Bayesian Neural Networks." IEEE Transactions on Neural Networks and Learning Systems, 2024, 1–13. http://dx.doi.org/10.1109/tnnls.2024.3485529.

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48

van Haasteren, Rutger. "Use Model Averaging instead of Model Selection in Pulsar Timing." Monthly Notices of the Royal Astronomical Society: Letters, November 19, 2024. http://dx.doi.org/10.1093/mnrasl/slae108.

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Abstract Over the past decade and a half, adoption of Bayesian inference in pulsar timing analysis has led to increasingly sophisticated models. The recent announcement of evidence for a stochastic background of gravitational waves by various pulsar timing array projects highlighted Bayesian inference as a central tool for parameter estimation and model selection. Despite its success, Bayesian inference is occasionally misused in the pulsar timing community. A common workflow is that the data is analyzed in multiple steps: a first analysis of single pulsars individually, and a subsequent analy

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Tendeiro, Jorge N., and Henk A. L. Kiers. "With Bayesian estimation one can get all that Bayes factors offer, and more." Psychonomic Bulletin & Review, September 9, 2022. http://dx.doi.org/10.3758/s13423-022-02164-3.

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AbstractIn classical statistics, there is a close link between null hypothesis significance testing (NHST) and parameter estimation via confidence intervals. However, for the Bayesian counterpart, a link between null hypothesis Bayesian testing (NHBT) and Bayesian estimation via a posterior distribution is less straightforward, but does exist, and has recently been reiterated by Rouder, Haaf, and Vandekerckhove (2018). It hinges on a combination of a point mass probability and a probability density function as prior (denoted as the spike-and-slab prior). In the present paper, it is first caref

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Zhu, Rui, Sufang Chen, Dong Jiang, et al. "Enhancing Nonlinear Subspace Identification Using Sparse Bayesian Learning with Spike and Slab Priors." Journal of Vibration Engineering & Technologies, June 7, 2023. http://dx.doi.org/10.1007/s42417-023-01030-3.

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