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Journal articles on the topic 'Hierarchical Bayesian Priors'

Author: Grafiati

Published: 6 September 2023

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1

Song, Chengyuan, Dongchu Sun, Kun Fan, and Rongji Mu. "Posterior Propriety of an Objective Prior in a 4-Level Normal Hierarchical Model." Mathematical Problems in Engineering 2020 (February 14, 2020): 1–10. http://dx.doi.org/10.1155/2020/8236934.

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The use of hierarchical Bayesian models in statistical practice is extensive, yet it is dangerous to implement the Gibbs sampler without checking that the posterior is proper. Formal approaches to objective Bayesian analysis, such as the Jeffreys-rule approach or reference prior approach, are only implementable in simple hierarchical settings. In this paper, we consider a 4-level multivariate normal hierarchical model. We demonstrate the posterior using our recommended prior which is proper in the 4-level normal hierarchical models. A primary advantage of the recommended prior over other propo

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Jiao, Yan, Christopher Hayes, and Enric Cortés. "Hierarchical Bayesian approach for population dynamics modelling of fish complexes without species-specific data." ICES Journal of Marine Science 66, no. 2 (2008): 367–77. http://dx.doi.org/10.1093/icesjms/fsn162.

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Abstract Jiao, Y., Hayes, C., and Cortés, E. 2009. Hierarchical Bayesian approach for population dynamics modelling of fish complexes without species-specific data. – ICES Journal of Marine Science, 66: 367–377. Modelling the population dynamics of fish complexes is challenging, and many species have been assessed and managed as a complex that was treated as a single species. Two Bayesian state-space surplus production models with multilevel priors (hierarchical models) were developed to simulate variability in population growth rates of species in a complex, using the hammerhead shark complex

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Zhang, Hai, Puyu Wang, Qing Dong, and Pu Wang. "Sparse Bayesian linear regression using generalized normal priors." International Journal of Wavelets, Multiresolution and Information Processing 15, no. 03 (2017): 1750021. http://dx.doi.org/10.1142/s0219691317500217.

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A sparse Bayesian linear regression model is proposed that generalizes the Bayesian Lasso to a class of Bayesian models with scale mixtures of normal distributions as priors for the regression coefficients. We assume a hierarchical Bayesian model with a binary indicator for whether a predictor variable is included in the model, a generalized normal prior distribution for the coefficients of the included variables, and a Student-t error model for robustness to heavy tailed noise. Our model out-performs other popular sparse regression estimators on synthetic and real data.

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Chan, Joshua C. C. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs." International Journal of Forecasting 37, no. 3 (2021): 1212–26. http://dx.doi.org/10.1016/j.ijforecast.2021.01.002.

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Scarpa, Bruno, and David B. Dunson. "Bayesian Hierarchical Functional Data Analysis Via Contaminated Informative Priors." Biometrics 65, no. 3 (2009): 772–80. http://dx.doi.org/10.1111/j.1541-0420.2008.01163.x.

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6

Gu, Xiaojing, Henry Leung, and Xingsheng Gu. "Bayesian Sparse Estimation Using Double Lomax Priors." Mathematical Problems in Engineering 2013 (2013): 1–17. http://dx.doi.org/10.1155/2013/176249.

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Sparsity-promoting prior along with Bayesian inference is an effective approach in solving sparse linear models (SLMs). In this paper, we first introduce a new sparsity-promoting prior coined as Double Lomax prior, which corresponds to a three-level hierarchical model, and then we derive a full variational Bayesian (VB) inference procedure. When noninformative hyperprior is assumed, we further show that the proposed method has one more latent variable than the canonical automatic relevance determination (ARD). This variable has a smoothing effect on the solution trajectories, thus providing im

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Liang, Xinya, Akihito Kamata, and Ji Li. "Hierarchical Bayes Approach to Estimate the Treatment Effect for Randomized Controlled Trials." Educational and Psychological Measurement 80, no. 6 (2020): 1090–114. http://dx.doi.org/10.1177/0013164420909885.

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One important issue in Bayesian estimation is the determination of an effective informative prior. In hierarchical Bayes models, the uncertainty of hyperparameters in a prior can be further modeled via their own priors, namely, hyper priors. This study introduces a framework to construct hyper priors for both the mean and the variance hyperparameters for estimating the treatment effect in a two-group randomized controlled trial. Assuming a random sample of treatment effect sizes is obtained from past studies, the hyper priors can be constructed based on the sampling distributions of the effect

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Nam, Hyun Woo. "Modeling hyper-priors for Bayesian IRT equating: Fixed hyper-parameters or Hierarchical hyper-priors." Korean Society for Educational Evaluation 32, no. 4 (2019): 777–95. http://dx.doi.org/10.31158/jeev.2019.32.4.777.

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Wang, Mengxi, Qingwang Liu, Liyong Fu, Guangxing Wang, and Xiongqing Zhang. "Airborne LIDAR-Derived Aboveground Biomass Estimates Using a Hierarchical Bayesian Approach." Remote Sensing 11, no. 9 (2019): 1050. http://dx.doi.org/10.3390/rs11091050.

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Conventional ground survey data are very accurate, but expensive. Airborne lidar data can reduce the costs and effort required to conduct large-scale forest surveys. It is critical to improve biomass estimation and evaluate carbon stock when we use lidar data. Bayesian methods integrate prior information about unknown parameters, reduce the parameter estimation uncertainty, and improve model performance. This study focused on predicting the independent tree aboveground biomass (AGB) with a hierarchical Bayesian model using airborne LIDAR data and comparing the hierarchical Bayesian model with

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Krishnan, Ranganath, Mahesh Subedar, and Omesh Tickoo. "Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4477–84. http://dx.doi.org/10.1609/aaai.v34i04.5875.

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Stochastic variational inference for Bayesian deep neural network (DNN) requires specifying priors and approximate posterior distributions over neural network weights. Specifying meaningful weight priors is a challenging problem, particularly for scaling variational inference to deeper architectures involving high dimensional weight space. We propose MOdel Priors with Empirical Bayes using DNN (MOPED) method to choose informed weight priors in Bayesian neural networks. We formulate a two-stage hierarchical modeling, first find the maximum likelihood estimates of weights with DNN, and then set

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Cucchi, Karina, Falk Heße, Nura Kawa, Changhong Wang, and Yoram Rubin. "Ex-situ priors: A Bayesian hierarchical framework for defining informative prior distributions in hydrogeology." Advances in Water Resources 126 (April 2019): 65–78. http://dx.doi.org/10.1016/j.advwatres.2019.02.003.

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12

Shan, Bowei. "Estimation of Response Functions Based on Variational Bayes Algorithm in Dynamic Images Sequences." BioMed Research International 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/4851401.

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We proposed a nonparametric Bayesian model based on variational Bayes algorithm to estimate the response functions in dynamic medical imaging. In dynamic renal scintigraphy, the impulse response or retention functions are rather complicated and finding a suitable parametric form is problematic. In this paper, we estimated the response functions using nonparametric Bayesian priors. These priors were designed to favor desirable properties of the functions, such as sparsity or smoothness. These assumptions were used within hierarchical priors of the variational Bayes algorithm. We performed our a

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Negrín-Hernández, Miguel-Angel, María Martel-Escobar, and Francisco-José Vázquez-Polo. "Bayesian Meta-Analysis for Binary Data and Prior Distribution on Models." International Journal of Environmental Research and Public Health 18, no. 2 (2021): 809. http://dx.doi.org/10.3390/ijerph18020809.

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In meta-analysis, the structure of the between-sample heterogeneity plays a crucial role in estimating the meta-parameter. A Bayesian meta-analysis for binary data has recently been proposed that measures this heterogeneity by clustering the samples and then determining the posterior probability of the cluster models through model selection. The meta-parameter is then estimated using Bayesian model averaging techniques. Although an objective Bayesian meta-analysis is proposed for each type of heterogeneity, we concentrate the attention of this paper on priors over the models. We consider four

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Ghoreishi, S. K. "Bayesian analysis of hierarchical heteroscedastic linear models using Dirichlet-Laplace priors." Journal of Statistical Theory and Applications 16, no. 1 (2017): 53. http://dx.doi.org/10.2991/jsta.2017.16.1.5.

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Bhattacharya, Samir K., and Ram C. Tiwari. "Hierarchical Bayesian reliability analysis using Erlang families of priors and hyperpriors." Microelectronics Reliability 32, no. 1-2 (1992): 241–47. http://dx.doi.org/10.1016/0026-2714(92)90102-q.

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Peters, Megan A. K., Ling-Qi Zhang, and Ladan Shams. "The material-weight illusion is a Bayes-optimal percept under competing density priors." PeerJ 6 (October 11, 2018): e5760. http://dx.doi.org/10.7717/peerj.5760.

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The material-weight illusion (MWI) is one example in a class of weight perception illusions that seem to defy principled explanation. In this illusion, when an observer lifts two objects of the same size and mass, but that appear to be made of different materials, the denser-looking (e.g., metal-look) object is perceived as lighter than the less-dense-looking (e.g., polystyrene-look) object. Like the size-weight illusion (SWI), this perceptual illusion occurs in the opposite direction of predictions from an optimal Bayesian inference process, which predicts that the denser-looking object shoul

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Mohite, Siddharth R., Priyadarshini Rajkumar, Shreya Anand, et al. "Inferring Kilonova Population Properties with a Hierarchical Bayesian Framework. I. Nondetection Methodology and Single-event Analyses." Astrophysical Journal 925, no. 1 (2022): 58. http://dx.doi.org/10.3847/1538-4357/ac3981.

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Abstract We present nimbus: a hierarchical Bayesian framework to infer the intrinsic luminosity parameters of kilonovae (KNe) associated with gravitational-wave (GW) events, based purely on nondetections. This framework makes use of GW 3D distance information and electromagnetic upper limits from multiple surveys for multiple events and self-consistently accounts for the finite sky coverage and probability of astrophysical origin. The framework is agnostic to the brightness evolution assumed and can account for multiple electromagnetic passbands simultaneously. Our analyses highlight the impor

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18

Nabi, Sareh, Houssam Nassif, Joseph Hong, Hamed Mamani, and Guido Imbens. "Bayesian Meta-Prior Learning Using Empirical Bayes." Management Science 68, no. 3 (2022): 1737–55. http://dx.doi.org/10.1287/mnsc.2021.4136.

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Adding domain knowledge to a learning system is known to improve results. In multiparameter Bayesian frameworks, such knowledge is incorporated as a prior. On the other hand, the various model parameters can have different learning rates in real-world problems, especially with skewed data. Two often-faced challenges in operation management and management science applications are the absence of informative priors and the inability to control parameter learning rates. In this study, we propose a hierarchical empirical Bayes approach that addresses both challenges and that can generalize to any B

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LIU, Shuai, Licheng JIAO, Shuyuan YANG, and Hongying LIU. "Hierarchical Sparse Bayesian Learning with Beta Process Priors for Hyperspectral Imagery Restoration." IEICE Transactions on Information and Systems E100.D, no. 2 (2017): 350–58. http://dx.doi.org/10.1587/transinf.2016edp7322.

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Keefe, Matthew J., Marco A. R. Ferreira, and Christopher T. Franck. "Objective Bayesian Analysis for Gaussian Hierarchical Models with Intrinsic Conditional Autoregressive Priors." Bayesian Analysis 14, no. 1 (2019): 181–209. http://dx.doi.org/10.1214/18-ba1107.

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21

Bell, Andrew, and Kelvyn Jones. "Bayesian informative priors with Yang and Land’s hierarchical age–period–cohort model." Quality & Quantity 49, no. 1 (2013): 255–66. http://dx.doi.org/10.1007/s11135-013-9985-3.

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22

Donegan, Connor, Yongwan Chun, and Amy E. Hughes. "Bayesian estimation of spatial filters with Moran’s eigenvectors and hierarchical shrinkage priors." Spatial Statistics 38 (August 2020): 100450. http://dx.doi.org/10.1016/j.spasta.2020.100450.

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23

Zhou, Feng, and Xueru Bai. "High-Resolution Sparse Subband Imaging Based on Bayesian Learning With Hierarchical Priors." IEEE Transactions on Geoscience and Remote Sensing 56, no. 8 (2018): 4568–80. http://dx.doi.org/10.1109/tgrs.2018.2827072.

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24

Abdelnour, Farras, Christopher Genovese, and Theodore Huppert. "Hierarchical Bayesian regularization of reconstructions for diffuse optical tomography using multiple priors." Biomedical Optics Express 1, no. 4 (2010): 1084. http://dx.doi.org/10.1364/boe.1.001084.

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25

McCandless, Lawrence C., Paul Gustafson, Adrian R. Levy, and Sylvia Richardson. "Hierarchical priors for bias parameters in Bayesian sensitivity analysis for unmeasured confounding." Statistics in Medicine 31, no. 4 (2012): 383–96. http://dx.doi.org/10.1002/sim.4453.

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26

Odegaard, Brian, and Ladan Shams. "The Relationship Between Audiovisual Binding Tendencies and Prodromal Features of Schizophrenia in the General Population." Clinical Psychological Science 5, no. 4 (2017): 733–41. http://dx.doi.org/10.1177/2167702617704014.

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Current theoretical accounts of schizophrenia have considered the disorder within the framework of hierarchical Bayesian inference, positing that symptoms arise from a deficit in the brain’s capacity to combine incoming sensory information with preexisting priors. Here, we present the first investigation to examine the relationship between priors governing multisensory perception and subclinical, prodromal features of schizophrenia in the general population. We tested participants in two complementary tasks (one spatial, one temporal) and employed a Bayesian model to estimate both the precisio

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MacNab, Ying C. "On Gaussian Markov random fields and Bayesian disease mapping." Statistical Methods in Medical Research 20, no. 1 (2010): 49–68. http://dx.doi.org/10.1177/0962280210371561.

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We discuss the nature of Gaussian Markov random fields (GMRFs) as they are typically formulated via full conditionals, also named conditional autoregressive or CAR formulations, to represent small area relative risks ensemble priors within a Bayesian hierarchical model framework for statistical inference in disease mapping and spatial regression. We present a partial review on GMRF/CAR and multivariate GMRF prior formulations in univariate and multivariate disease mapping models and communicate insights into various prior characteristics for representing disease risks variability and ‘spatial

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GILL, JEFF, and JOHN R. FREEMAN. "Dynamic elicited priors for updating covert networks." Network Science 1, no. 1 (2013): 68–94. http://dx.doi.org/10.1017/nws.2012.6.

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AbstractThe study of covert networks is plagued by the fact that individuals conceal their attributes and associations. To address this problem, we develop a technology for eliciting this information from qualitative subject-matter experts to inform statistical social network analysis. We show how the information from the subjective probability distributions can be used as input to Bayesian hierarchical models for network data. In the spirit of “proof of concept,” the results of a test of the technology are reported. Our findings show that human subjects can use the elicitation tool effectivel

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Yang, Bishan, Claire Cardie, and Peter Frazier. "A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution." Transactions of the Association for Computational Linguistics 3 (December 2015): 517–28. http://dx.doi.org/10.1162/tacl_a_00155.

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We present a novel hierarchical distance-dependent Bayesian model for event coreference resolution. While existing generative models for event coreference resolution are completely unsupervised, our model allows for the incorporation of pairwise distances between event mentions — information that is widely used in supervised coreference models to guide the generative clustering processing for better event clustering both within and across documents. We model the distances between event mentions using a feature-rich learnable distance function and encode them as Bayesian priors for nonparametri

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Delgado, H. E., L. M. Sarro, G. Clementini, T. Muraveva, and A. Garofalo. "Hierarchical Bayesian model to inferPL(Z)relations usingGaiaparallaxes." Astronomy & Astrophysics 623 (March 2019): A156. http://dx.doi.org/10.1051/0004-6361/201832945.

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In a recent study we analysed period–luminosity–metallicity (PLZ) relations for RR Lyrae stars using theGaiaData Release 2 (DR2) parallaxes. It built on a previous work that was based on the firstGaiaData Release (DR1), and also included period–luminosity (PL) relations for Cepheids and RR Lyrae stars. The method used to infer the relations fromGaiaDR2 data and one of the methods used forGaiaDR1 data was based on a Bayesian model, the full description of which was deferred to a subsequent publication. This paper presents the Bayesian method for the inference of the parameters ofPL(Z) relations

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Liu, Qinghua, Yuanxin He, Kai Ding, and Quanmin Xie. "Complex Multisnapshot Sparse Bayesian Learning for Offgrid DOA Estimation." International Journal of Antennas and Propagation 2022 (February 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/4500243.

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Direction of arrival (DOA) estimation has recently been developed based on sparse signal reconstruction (SSR). Sparse Bayesian learning (SBL) is a typical method of SSR. In SBL, the two-layer hierarchical model in Gaussian scale mixtures (GSMs) has been used to model sparsity-inducing priors. However, this model is mainly applied to real-valued signal models. In order to apply SBL to complex-valued signal models, a general class of sparsity-inducing priors is proposed for complex-valued signal models by complex Gaussian scale mixtures (CGSMs), and the special cases correspond to complex versio

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Muller, Peter, and Gary L. Rosner. "A Bayesian Population Model With Hierarchical Mixture Priors Applied to Blood Count Data." Journal of the American Statistical Association 92, no. 440 (1997): 1279. http://dx.doi.org/10.2307/2965398.

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Yu, Rongjie, and Mohamed Abdel-Aty. "Investigating different approaches to develop informative priors in hierarchical Bayesian safety performance functions." Accident Analysis & Prevention 56 (July 2013): 51–58. http://dx.doi.org/10.1016/j.aap.2013.03.023.

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Yang, Jie, and Yixin Yang. "Sparse Bayesian DOA Estimation Using Hierarchical Synthesis Lasso Priors for Off-Grid Signals." IEEE Transactions on Signal Processing 68 (2020): 872–84. http://dx.doi.org/10.1109/tsp.2020.2967665.

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Müller, Peter, and Gary L. Rosner. "A Bayesian Population Model with Hierarchical Mixture Priors Applied to Blood Count Data." Journal of the American Statistical Association 92, no. 440 (1997): 1279–92. http://dx.doi.org/10.1080/01621459.1997.10473649.

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36

Leoni, Leonardo, Farshad BahooToroody, Saeed Khalaj, Filippo De Carlo, Ahmad BahooToroody, and Mohammad Mahdi Abaei. "Bayesian Estimation for Reliability Engineering: Addressing the Influence of Prior Choice." International Journal of Environmental Research and Public Health 18, no. 7 (2021): 3349. http://dx.doi.org/10.3390/ijerph18073349.

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Over the last few decades, reliability analysis has attracted significant interest due to its importance in risk and asset integrity management. Meanwhile, Bayesian inference has proven its advantages over other statistical tools, such as maximum likelihood estimation (MLE) and least square estimation (LSE), in estimating the parameters characterizing failure modelling. Indeed, Bayesian inference can incorporate prior beliefs and information into the analysis, which could partially overcome the lack of data. Accordingly, this paper aims to provide a closed-mathematical representation of Bayesi

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Millar, Russell B., and Wayne S. Stewart. "Automatic calculation of the sensitivity of Bayesian fisheries models to informative priors." Canadian Journal of Fisheries and Aquatic Sciences 62, no. 5 (2005): 1028–36. http://dx.doi.org/10.1139/f04-240.

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The derivatives of Bayes estimators, with respect to changes in hyper-parameters of the prior density, are posterior covariances. Hence, these derivatives can be readily estimated from a posterior sample and the calculation is shown to be especially straightforward for parameters having a marginal prior that is of exponential family form. Three examples are given. The first fits a Ricker curve to stockrecruit data and, for several important management parameters, examines the sensitivity of the Bayes estimates to the informative log-normal priors placed on the maximum annual reproductive rate

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Zhang, Li, and Ying-Ying Zhang. "The Bayesian Posterior and Marginal Densities of the Hierarchical Gamma–Gamma, Gamma–Inverse Gamma, Inverse Gamma–Gamma, and Inverse Gamma–Inverse Gamma Models with Conjugate Priors." Mathematics 10, no. 21 (2022): 4005. http://dx.doi.org/10.3390/math10214005.

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Positive, continuous, and right-skewed data are fit by a mixture of gamma and inverse gamma distributions. For 16 hierarchical models of gamma and inverse gamma distributions, there are only 8 of them that have conjugate priors. We first discuss some common typical problems for the eight hierarchical models that do not have conjugate priors. Then, we calculate the Bayesian posterior densities and marginal densities of the eight hierarchical models that have conjugate priors. After that, we discuss the relations among the eight analytical marginal densities. Furthermore, we find some relations

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Pibouleau, Leslie, and Sylvie Chevret. "BAYESIAN HIERARCHICAL META-ANALYSIS MODEL FOR MEDICAL DEVICE EVALUATION: APPLICATION TO INTRACRANIAL STENTS." International Journal of Technology Assessment in Health Care 29, no. 2 (2013): 123–30. http://dx.doi.org/10.1017/s0266462313000093.

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Objectives: The aim of this study was to propose a statistical model that takes into account clinical data on earlier versions when evaluating the latest version of an implantable medical device (IMD).Methods: We compared the performances of a Bayesian three-level hierarchical meta-analysis model with those of a Bayesian random-effects model through a simulation study. Posterior mean estimates of the success rate for each IMD version were computed as well as the probability that the latest version improved in effectiveness. Models were compared using the Deviance Information Criterion (DIC), t

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Thorson, James T., and Jim Berkson. "Multispecies estimation of Bayesian priors for catchability trends and density dependence in the US Gulf of Mexico." Canadian Journal of Fisheries and Aquatic Sciences 67, no. 6 (2010): 936–54. http://dx.doi.org/10.1139/f10-040.

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Fishery-dependent catch-per-unit-effort (CPUE) derived indices of stock abundance are commonly used in fishery stock assessment models and may be significantly biased due to changes in catchability over time. Factors causing time-varying catchability include density-dependent habitat selection and technology improvements such as global positioning systems. In this study, we develop a novel multispecies method to estimate Bayesian priors for catchability functional parameters. This method uses the deviance information criterion to select a parsimonious functional model for catchability among 10

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Montano, Diego. "Multivariate hierarchical Bayesian models and choice of priors in the analysis of survey data." Journal of Applied Statistics 44, no. 16 (2016): 3011–32. http://dx.doi.org/10.1080/02664763.2016.1267120.

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Dunson, David B., and Kenneth R. Tindall. "Bayesian Analysis of Mutational Spectra." Genetics 156, no. 3 (2000): 1411–18. http://dx.doi.org/10.1093/genetics/156.3.1411.

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Abstract Studies that examine both the frequency of gene mutation and the pattern or spectrum of mutational changes can be used to identify chemical mutagens and to explore the molecular mechanisms of mutagenesis. In this article, we propose a Bayesian hierarchical modeling approach for the analysis of mutational spectra. We assume that the total number of independent mutations and the numbers of mutations falling into different response categories, defined by location within a gene and/or type of alteration, follow binomial and multinomial sampling distributions, respectively. We use prior di

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Pacifico, Antonio. "Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure." Econometrics 10, no. 3 (2022): 28. http://dx.doi.org/10.3390/econometrics10030028.

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This paper improves the existing literature on the shrinkage of high dimensional model and parameter spaces through Bayesian priors and Markov Chains algorithms. A hierarchical semiparametric Bayes approach is developed to overtake limits and misspecificity involved in compressed regression models. Methodologically, a multicountry large structural Panel Vector Autoregression is compressed through a robust model averaging to select the best subset across all possible combinations of predictors, where robust stands for the use of mixtures of proper conjugate priors. Concerning dynamic analysis,

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Cunanan, Kristen M., Alexia Iasonos, Ronglai Shen, and Mithat Gönen. "Variance prior specification for a basket trial design using Bayesian hierarchical modeling." Clinical Trials 16, no. 2 (2018): 142–53. http://dx.doi.org/10.1177/1740774518812779.

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Background: In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distrib

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Mangipudi, Abhi, Eric Thrane, and Csaba Balazs. "Bayesian WIMP detection with the Cherenkov Telescope Array." Journal of Cosmology and Astroparticle Physics 2022, no. 11 (2022): 010. http://dx.doi.org/10.1088/1475-7516/2022/11/010.

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Abstract Over the past decades Bayesian methods have become increasingly popular in astronomy and physics as stochastic samplers have enabled efficient investigation of high-dimensional likelihood surfaces. In this work we develop a hierarchical Bayesian inference framework to detect the presence of dark matter annihilation events in data from the Cherenkov Telescope Array (CTA). Gamma-ray events are weighted based on their measured sky position Ω̂ m and energy Em in order to derive a posterior distribution for the dark matter's velocity averaged cross section 〈σv〉. The dark matter signal mode

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Asmarian, Naeimehossadat, Seyyed Mohammad Taghi Ayatollahi, Zahra Sharafi, and Najaf Zare. "Bayesian Spatial Joint Model for Disease Mapping of Zero-Inflated Data with R-INLA: A Simulation Study and an Application to Male Breast Cancer in Iran." International Journal of Environmental Research and Public Health 16, no. 22 (2019): 4460. http://dx.doi.org/10.3390/ijerph16224460.

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Hierarchical Bayesian log-linear models for Poisson-distributed response data, especially Besag, York and Mollié (BYM) model, are widely used for disease mapping. In some cases, due to the high proportion of zero, Bayesian zero-inflated Poisson models are applied for disease mapping. This study proposes a Bayesian spatial joint model of Bernoulli distribution and Poisson distribution to map disease count data with excessive zeros. Here, the spatial random effect is simultaneously considered into both logistic and log-linear models in a Bayesian hierarchical framework. In addition, we focus on

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Yang, Haoyuan, Xiuqin Su, Jing Wu, and Songmao Chen. "Non-blind image blur removal method based on a Bayesian hierarchical model with hyperparameter priors." Optik 204 (February 2020): 164178. http://dx.doi.org/10.1016/j.ijleo.2020.164178.

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van Erp, Bart, Wouter W. L. Nuijten, Thijs van de Laar, and Bert de Vries. "Automating Model Comparison in Factor Graphs." Entropy 25, no. 8 (2023): 1138. http://dx.doi.org/10.3390/e25081138.

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Bayesian state and parameter estimation are automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often overlooked despite its importance. This paper efficiently automates Bayesian model averaging, selection, and combination by message passing on a Forney-style factor graph with a custom mixture node. Parameter and state inference, and model comparison can then be executed simultaneously using message passing with scale factors. This approach shorte

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Silva, Fabyano Fonseca e., Thelma Sáfadi, Joel Augusto Muniz, et al. "Bayesian analysis of autoregressive panel data model: application in genetic evaluation of beef cattle." Scientia Agricola 68, no. 2 (2011): 237–45. http://dx.doi.org/10.1590/s0103-90162011000200015.

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The animal breeding values forecasting at futures times is a relevant technological innovation in the field of Animal Science, since its enables a previous indication of animals that will be either kept by the producer for breeding purposes or discarded. This study discusses an MCMC Bayesian methodology applied to panel data in a time series context. We consider Bayesian analysis of an autoregressive, AR(p), panel data model of order p, using an exact likelihood function, comparative analysis of prior distributions and predictive distributions of future observations. The methodology was tested

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Hassan, Masoud M. "A Fully Bayesian Logistic Regression Model for Classification of ZADA Diabetes Dataset." Science Journal of University of Zakho 8, no. 3 (2020): 105–11. http://dx.doi.org/10.25271/sjuoz.2020.8.3.707.

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Classification of diabetes data with existing data mining and machine learning algorithms is challenging and the predictions are not always accurate. We aim to build a model that effectively addresses these challenges (misclassification) and can accurately diagnose and classify diabetes. In this study, we investigated the use of Bayesian Logistic Regression (BLR) for mining such data to diagnose and classify various diabetes conditions. This approach is fully Bayesian suited for automating Markov Chain Monte Carlo (MCMC) simulation. Using Bayesian methods in analysing medical data is useful be

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