Bayesian Data Analysis

Bayesian predictive model comparison via parallel sampling

Congdon, P. D. (2004) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.03.016 · Cited by 25

Explainable neural-networked variational inference: A new and fast paradigm with automatic differentiation for high-dimensional Bayesian inverse problems

Mo, J., & Yan, W. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111337 · Cited by 24

Testing Gibrat's legacy: A Bayesian approach to study the growth of firms

Cefis, E., Ciccarelli, M., & Orsenigo, L. (2007) · Structural Change and Economic Dynamics · doi:10.1016/j.strueco.2007.04.002 · Cited by 24

Inference of directional selection and mutation parameters assuming equilibrium

Vogl, C., & Bergman, J. (2015) · Theoretical Population Biology · doi:10.1016/j.tpb.2015.10.003 · Cited by 24

Testing multivariate normality in incomplete data of small sample size

Tan, M. T., Fang, H., Tian, G., & Wei, G. (2004) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2004.02.014 · Cited by 24

A model for non-parametric spatially varying regression effects

Congdon, P. D. (2004) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2004.08.008 · Cited by 24

Calibration of ADM1 using the Monte Carlo Markov Chain for modeling of anaerobic biodigestion of sugarcane vinasse in an AnSBBR

Couto, P. T., Brustello, M., Albanez, R., Rodrigues, J. A. D., Zaiat, M., & Ribeiro, R. (2018) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2018.11.014 · Cited by 23

Measuring and illustrating statistical evidence in a cost-effectiveness analysis

Hoch, J. S., & Blume, J. D. (2007) · Journal of Health Economics · doi:10.1016/j.jhealeco.2007.07.002 · Cited by 23

Performance Evaluation of Document Structure Extraction Algorithms

Liang, J., Phillips, I. T., & Haralick, R. M. (2001) · Computer Vision and Image Understanding · doi:10.1006/cviu.2001.0933 · Cited by 23

Bayesian model averaging for mortality forecasting using leave-future-out validation

Barigou, K., Goffard, P.-O., Loisel, S., & Salhi, Y. (2022) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2022.01.011 · Cited by 22

Stability and inactivation of SARS-CoV-2 on food contact surfaces

Jung, S., Kim, D., Ahn, H., Go, H., Wang, Z., Yeo, D., Woo, S., Seo, Y., Hossain, M. I., Choi, I., et al. (2022) · Food Control · doi:10.1016/j.foodcont.2022.109306 · Cited by 22

Joint segmentation of wind speed and direction using a hierarchical model

Dobigeon, N., & Tourneret, J. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.04.016 · Cited by 22

Comparison of the dose-response relationships of 2 lipid-lowering agents: A Bayesian meta-analysis

Berry, D. A., Berry, S. M., McKellar, J., & Pearson, T. A. (2003) · American Heart Journal · doi:10.1016/s0002-8703(03)00106-6 · Cited by 21

Longer-term response to experimental manipulation of fallen timber on forest floors of floodplain forest in south-eastern Australia

Nally, R. M. (2006) · Forest Ecology and Management · doi:10.1016/j.foreco.2006.03.024 · Cited by 20

The synergy of machine learning and CALPHAD: Revitalizing traditional approaches

Shen, C. (2025) · Computational Materials Science · doi:10.1016/j.commatsci.2025.113970 · Cited by 19

From pup production to quotas: current status of harp seals in the Greenland Sea

Øigård, T. A., Haug, T., & Nilssen, K. T. (2013) · ICES Journal of Marine Science · doi:10.1093/icesjms/fst155 · Cited by 19

Estimating human exposure to pyrethroids’ mixtures from biomonitoring data using physiologically based pharmacokinetic modeling

Quindroit, P., Crépet, A., & Brochot, C. (2020) · Environmental Research · doi:10.1016/j.envres.2020.110281 · Cited by 19

Proper and improper use and interpretation of Beta-binomial models in the analysis of replicated difference and preference tests

Meyners, M. (2007) · Food Quality and Preference · doi:10.1016/j.foodqual.2007.01.001 · Cited by 19

Comparison of Challenge Models for Determining the Colonization Dose of Campylobacter jejuni in Broiler Chicks

Line, J. E., Hiett, K. L., & Conlan, A. J. K. (2008) · Poultry Science · doi:10.3382/ps.2008-00027 · Cited by 19

A Bayesian statistical approach for the evaluation of CMAQ

Swall, J., & Davis, J. M. (2006) · Atmospheric Environment · doi:10.1016/j.atmosenv.2005.12.058 · Cited by 18

A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting

Sousa, M., Tomé, A. M., & Moreira, J. E. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128434 · Cited by 25

A systematic meta-analysis of the efficacy of treatments for a global honey bee pathogen - the Varroa mite

O’Connell, D. P., Healy, K., Wilton, J., Botías, C., & Jones, J. C. (2025) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.178228 · Cited by 21

Is there an emotional dimension to road safety? A spatial analysis for traffic crashes considering streetscape perception and built environment

Liu, Y., Chen, T., Chung, H., Jang, K., & Xu, P. (2025) · Analytic Methods in Accident Research · doi:10.1016/j.amar.2025.100374 · Cited by 18

Survivability assessment of fastnet lighthouse

Antonini, A., Raby, A. C., Brownjohn, J., Pappas, A., & D'Ayala, D. F. (2019) · Coastal Engineering · doi:10.1016/j.coastaleng.2019.03.007 · Cited by 18

Human capital and international portfolio diversification: A reappraisal

Bretscher, L., Julliard, C., & Rosa, C. (2015) · Journal of International Economics · doi:10.1016/j.jinteco.2015.12.007 · Cited by 18

Smart database design for concrete durability analysis - An application in the Hongkong-Zhuhai-Macau bridge

Ma, P., Zhang, Y., Li, K., Li, Q., Wang, J., Li, L., & Yuan, Z. (2022) · Cement and Concrete Research · doi:10.1016/j.cemconres.2022.107033 · Cited by 17

Estimating annual generation rates of total P and total N for different land uses in Tasmania, Australia

Broad, S., & Corkrey, R. (2011) · Journal of Environmental Management · doi:10.1016/j.jenvman.2011.01.023 · Cited by 17

Single-subject analysis of N400 event-related potential component with five different methods

Kallionpää, R., Pesonen, H., Scheinin, A., Sandman, N., Laitio, R. M., Scheinin, H., Revonsuo, A., & Valli, K. (2019) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2019.06.012 · Cited by 17

Evaluation of von bertalanffy growth curves for the introduced red king crab (Paralithodes camtschaticus) in Norwegian waters

Windsland, K., Hvingel, C., Nilssen, E. M., & Sundet, J. H. (2013) · Fisheries Research · doi:10.1016/j.fishres.2013.03.003 · Cited by 17

Relevance of functional flexibility for heterogeneous sales response models

Hruschka, H. (2005) · European Journal of Operational Research · doi:10.1016/j.ejor.2005.05.003 · Cited by 16

On the foundations of likelihood principle

de Cristofaro, R. (2003) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2003.08.011 · Cited by 17

Adaptive sampling design for multi-task learning of Gaussian processes in manufacturing

Mehta, M., & Shao, C. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2021.09.006 · Cited by 16

Assessing fishing capacity of two tuna fish species using different time-series data in Pakistan, Northern Arabian Sea

Kalhoro, M. A., Liu, Q., Zhu, L., Jiang, Z., & Liang, Z. (2024) · Estuarine Coastal and Shelf Science · doi:10.1016/j.ecss.2024.108692 · Cited by 16

Reducing drift in differential tracking

Rahimi, A., Morency, L., & Darrell, T. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.12.004 · Cited by 16

Estimating the probability of freedom of classical swine fever virus of the East-Belgium wild-boar population

Mintiens, K., Verloo, D., Venot, É., Laevens, H., Dufey, J., Dewulf, J., Boelaert, F., Kerkhofs, P., & Koenen, F. (2005) · Preventive Veterinary Medicine · doi:10.1016/j.prevetmed.2005.03.008 · Cited by 16

A novel imprecise stochastic process model for time-variant or dynamic uncertainty quantification

Li, J., & Jiang, C. (2022) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2022.01.004 · Cited by 15

Land-use-based freshwater sediment source fingerprinting using hydrogen isotope compositions of long-chain fatty acids

Hirave, P., Nelson, D. B., Glendell, M., & Alewell, C. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.162638 · Cited by 15

Multi-objective optimization for sustainable and economical polycarbonate production with reaction kinetics inference for real-world industrial process

Lee, E., Kim, M., Moon, I., & Kim, J. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.151484 · Cited by 14

Efficient value of information analysis for optimal monitoring placement of reinforced slopes by collaborative reliability updating

Tian, H.-M., Cao, Z.-J., Li, D. Q., & Wang, Y. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.110877 · Cited by 14

Bayesian calibration of the 40K decay scheme with implications for 40K-based geochronology

Carter, J. N., Hasler, C. E. J., Fuentes, A. J., Tholt, A. J., Morgan, L. E., & Renne, P. R. (2025) · Geochimica et Cosmochimica Acta · doi:10.1016/j.gca.2025.03.024 · Cited by 14

A spatially adaptive conditional autoregressive prior for area health data

Congdon, P. D. (2008) · Statistical Methodology · doi:10.1016/j.stamet.2008.02.005 · Cited by 14

Examining the role of random parameters and unobserved heterogeneity in the frequency-severity of rural freeway run-off-road and fixed-object crashes: A Bayesian hierarchical-geospatial approach

Effati, M., & Ramezanpoor, A. (2025) · Accident Analysis & Prevention · doi:10.1016/j.aap.2025.108005 · Cited by 15

Bottom-up and top-down uncertainty quantification for measurements

Burr, T. L., Croft, S., Favalli, A., Krieger, T., & Weaver, B. (2020) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2020.104224 · Cited by 13

Landscape pattern exhibits threshold-driven effect on nitrogen export of typical land use in subtropical hilly watershed under specific hydrological regimes

Meng, C., Liu, H., Wang, Y., Shen, J., Liu, F., Xia, Y., Li, Y., & Wu, J. (2023) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2023.138322 · Cited by 13

Generative AI responses are a dime a dozen; Making them count is the challenge – Evaluating information presentation styles in healthcare chatbots using hierarchical Bayesian regression models

Koscelny, S. N., Sadralashrafi, S., & Neyens, D. M. (2025) · Applied Ergonomics · doi:10.1016/j.apergo.2025.104515 · Cited by 15

Death Penalty Charging in Los Angeles County

Weiss, R. E., Berk, R. A., LI, W., & Farrell-Ross, M. (1999) · Sociological Methods & Research · doi:10.1177/0049124199028001005 · Cited by 13

Bayesian optimal experimental design for constitutive model calibration

Ricciardi, D. E., Seidl, D. T., Lester, B., Jones, A. R., & Jones, E. M. C. (2023) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2023.108881 · Cited by 12

Approximate Bayesian Computation for structural identification of ancient tie-rods using noisy modal data

Monchetti, S., Pepi, C., Viscardi, C., & Gioffrè, M. (2024) · Probabilistic Engineering Mechanics · doi:10.1016/j.probengmech.2024.103674 · Cited by 12

Association Between Social Determinants of Health and Glycemic Control Among African American People with Type 2 diabetes: The Jackson Heart Study

Hu, J., Kline, D., Tan, A., Zhao, S., Brock, G. N., Mion, L. C., Efird, J. T., Wang, D., Sims, M., Wu, B., et al. (2022) · Annals of Behavioral Medicine · doi:10.1093/abm/kaac026 · Cited by 13

Stable seasonal pattern models for forecast revision: A comparative study

Yelland, P. M. (2006) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2006.04.005 · Cited by 12

The inverse Riesz probability distribution on symmetric matrices

Tounsi, M., & Zine, R. (2012) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2012.05.013 · Cited by 12

Including uncertainties in harmonic hosting capacity calculation of a fast EV charging station utilizing Bayesian statistics and harmonic correlation

Nakhodchi, N., Bakhtiari, H., Bollen, M., & Rönnberg, S. K. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.108933 · Cited by 11

Hierarchical Bayesian modeling for calibration and uncertainty quantification of constitutive material models: Application to a uniaxial steel material model

Kurumbhati, M. M., Ramancha, M. K., Aakash, B. S., Conte, J. P., & Lotfizadeh, K. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.118409 · Cited by 11

A general probabilistic framework for impact localisation based on flexural wave propagation

Xiao, D. L., Rodrigues, F. D. S., Khodaei, Z. S., & Aliabadi, M. H. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112320 · Cited by 11

SBFEM and Bayesian inference for efficient multiple flaw detection in structures

Thananjayan, P., Ramu, P., & Natarajan, S. (2023) · Engineering Analysis with Boundary Elements · doi:10.1016/j.enganabound.2023.06.001 · Cited by 11

Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamics

Rosas, F. E., Candia‐Rivera, D., Luppi, A. I., Guo, Y., & Mediano, P. A. M. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107857 · Cited by 11

Linking measures of adult nicotine dependence to a common latent continuum and a comparison with adolescent patterns

Strong, D. R., Schonbrun, Y. C., Schaffran, C., Griesler, P. C., & Kandel, D. B. (2011) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2011.07.003 · Cited by 11

Performance of empirical Bayes estimation techniques used in probabilistic risk assessment

Gribok, A. V., Agarwal, V., & Yadav, V. (2020) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.106805 · Cited by 11

Ambient vibration-based cable tension monitoring and uncertainty analysis in cable-stayed bridge from a fully probabilistic perspective

Jiang, W.-J., & Kim, C. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.119755 · Cited by 10

Directed site exploration for permeable reactive barrier design

Lee, J., Graettinger, A. J., Moylan, J., & Reeves, H. W. (2008) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2008.05.026 · Cited by 10

Probability matrix decomposition models and main-effects generalized linear models for the analysis of replicated binary associations

Meulders, M., de Boeck, P. A. L., & Mechelen, I. V. (2001) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00029-9 · Cited by 10

Bayesian Statistics

Lynch, S. M. (2005) · Encyclopedia of Social Measurement · doi:10.1016/b0-12-369398-5/00156-0 · Cited by 10

A framework of zero-inflated Bayesian negative binomial regression models for spatiotemporal data

He, Q., & Huang, H. (2023) · Journal of Statistical Planning and Inference · doi:10.1016/j.jspi.2023.106098 · Cited by 9

Gridless three-dimensional acoustic imaging based on the concept of sonons: Reconstruction of source directivity and equivalent spatial distribution

Niu, Z., Antoni, J., & Bouley, S. (2024) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2024.118266 · Cited by 9

Accelerated training of deep learning surrogate models for surface displacement and flow, with application to MCMC-based history matching of CO 2 storage operations

Han, Y., Hamon, F. P., & Durlofsky, L. J. (2024) · Geoenergy Science and Engineering · doi:10.1016/j.geoen.2024.213589 · Cited by 9

Optimizing reservoir operations with Z-numbers: Addressing uncertainty and reliability

Nourani, V., Najafi, H., Nikoufar, B., & Huang‬‬‬‬, J. J. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.132903 · Cited by 9

The stochastic behavior of electricity prices under scrutiny: Evidence from spot and futures markets

Bégin, J., Gómez, F., Ignatieva, K., & Li, H. (2025) · Energy Economics · doi:10.1016/j.eneco.2025.108296 · Cited by 9

Bayesian hierarchical surplus production model of the common whelk Buccinum undatum in Icelandic waters

Woods, P. J., & Jónasson, J. P. (2017) · Fisheries Research · doi:10.1016/j.fishres.2017.05.011 · Cited by 9

Differential endorsement of suicidal ideation and attempt in bipolar versus unipolar depression: a testlet response theory analysis

Weinstock, L. M., Strong, D. R., Uebelacker, L. A., & Miller, I. W. (2016) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.04.034 · Cited by 9

Ecological labeling and wildlife conservation: Citizens’ perceptions of the elephant ivory-labeling system in China

Xie, Y. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.134709 · Cited by 9

Spatial distribution and determinants of tuberculosis incidence in Mozambique: A nationwide Bayesian disease mapping study

Cuboia, N., Reis‐Pardal, J., Pfumo‐Cuboia, I., Manhiça, I., Mutaquiha, C., Nitrogénio, L., Zindoga, P., & Azevedo, L. F. (2023) · Spatial and Spatio-temporal Epidemiology · doi:10.1016/j.sste.2023.100632 · Cited by 8

Impact of strategic public health interventions to reduce tuberculosis incidence in Brazil: a Bayesian structural time-series scenario analysis

Villalva-Serra, K., Barreto‐Duarte, B., Rodrigues, M. M. D. S., Queiroz, A. T. L., Martínez, L., Croda, J., Rolla, V. C., Kritski, A. L., Cordeiro‐Santos, M., Sterling, T. R., et al. (2024) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2024.100963 · Cited by 8

Bayesian, Systems-based, Multilevel Analysis of Associations for Complex Phenotypes: from Interpretation to Decision

Antal, P., Millinghoffer, A., Hullám, G., Hajós, G., Sárközy, P., Gézsi, A., Szalai, C., & Falus, A. (2014) · doi:10.1093/acprof:oso/9780198709022.003.0013 · Cited by 8

High-Dimensional Bayesian inference for model updating with neural likelihood approximator powered by dimensionality-reducing flow-based generative model

Zeng, J., & Yan, W. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.112688 · Cited by 8

A Bayesian Method for Estimating the Accuracy of Recalled Depression

Rutter, C. M., & Simon, G. E. (2004) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1046/j.1467-9876.2003.05211.x · Cited by 8

Bayesian inversion of laboratory experiments of transport through limestone fractures

Lehmann, F., Rajabi, M. M., Belfort, B., Delay, F., Fahs, M., Ackerer, P., & Younès, A. (2022) · Journal of Contaminant Hydrology · doi:10.1016/j.jconhyd.2022.104045 · Cited by 8

Calibration of a bumble bee foraging model using Approximate Bayesian Computation

Baey, C., Smith, H. G., Rundlöf, M., Olsson, O., Clough, Y., & Sahlin, U. (2022) · Ecological Modelling · doi:10.1016/j.ecolmodel.2022.110251 · Cited by 8

Applying Multiple Imputation with Geostatistical Models to Account for Item Nonresponse in Environmental Data

Muñoz, B., Lesser, V. M., & Smith, R. A. (2010) · Journal of Modern Applied Statistical Methods · doi:10.22237/jmasm/1272687960 · Cited by 8

Towards industrial deployment of pressure tomography for CO2 storage monitoring: Uncertainty and megatonne scale-up

Jackson, S. J., Gunning, J. S., Ennis‐King, J., Dance, T., & Jenkins, C. (2025) · International journal of greenhouse gas control · doi:10.1016/j.ijggc.2024.104299 · Cited by 7

Bayesian estimation of information-theoretic metrics for sparsely sampled distributions

Piga, A., Font-Pomarol, L., Sales‐Pardo, M., & Guimerà, R. (2024) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2024.114564 · Cited by 7

What’s in a sample? Epistemic uncertainty and metacognitive awareness in risk taking

Olschewski, S., & Scheibehenne, B. (2024) · Cognitive Psychology · doi:10.1016/j.cogpsych.2024.101642 · Cited by 13

Efficient combustion kinetic parameter optimization via variational inference

Wang, Y., Liu, C., Tao, C., Law, C. K., & Yang, B. (2024) · Proceedings of the Combustion Institute · doi:10.1016/j.proci.2024.105550 · Cited by 7

The generality of the emotion effect on magnitude sensitivity

Gong, M., & Baron, J. (2010) · Journal of Economic Psychology · doi:10.1016/j.joep.2010.10.002 · Cited by 7

A model-based analysis of the “combined-stimulation advantage”

Seldran, F., Micheyl, C., Truy, É., Berger-Vachon, C., Van, H. T., & Gallego, S. (2011) · Hearing Research · doi:10.1016/j.heares.2011.06.004 · Cited by 7

Assessing Psychiatric Morbidity from Community Registers: Methods for Bayesian Adjustment

Congdon, P. D., Smith, A., & Dean, C. (1998) · Urban Studies · doi:10.1080/0042098983908 · Cited by 7

Temperature, relative humidity and elderly type 2 diabetes mortality: A spatiotemporal analysis in Shandong, China

Zheng, W., Chu, J., Bambrick, H., Wang, N., Mengersen, K. L., Guo, X., & Hu, W. (2024) · International Journal of Hygiene and Environmental Health · doi:10.1016/j.ijheh.2024.114442 · Cited by 8

Bayesian inference of length distributions of human DNA

Correia, J. P., Silva, R., Anselmo, D. H. A. L., & da Silva, J. R. P. (2022) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.112244 · Cited by 6

Bayesian calibration of interatomic potentials for binary alloys

Hegde, A., Weiss, E., Windl, W., Najm, H. N., & Safta, C. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111660 · Cited by 6

An extended application ‘Brain Q’ processing EEG and MEG data of finger stimulation extended from ‘Zeffiro’ based on machine learning and signal processing

He, Q., & Pursiainen, S. (2021) · Cognitive Systems Research · doi:10.1016/j.cogsys.2020.08.006 · Cited by 7

Bomb-produced radiocarbon age validation of Greenland halibut (Reinhardtius hippoglossoides) suggests a new maximum longevity

Brogan, J. D., Kastelle, C. R., Helser, T. E., & Anderl, D. M. (2021) · Fisheries Research · doi:10.1016/j.fishres.2021.106000 · Cited by 6

New GPS-derived velocity field for Marmara region: Implications for the kinematics along the North Anatolian Fault Zone

Özkan, A., Yavaşoğlu, H., Tiryakioğlu, İ., Uslular, G., Karabulut, M. F., Ulrich, P., & Masson, F. (2025) · Tectonophysics · doi:10.1016/j.tecto.2025.230828 · Cited by 5

Longitudinal association between adolescent sexual identity and sleep quality: The mediating roles of peer victimization and perceived social support

Kwon, M., Livingston, J. A., Wang, W., & Hequembourg, A. L. (2024) · Sleep Health · doi:10.1016/j.sleh.2024.09.012 · Cited by 7

The effects of socio-economic and environmental factors on Korean suicidal ideation: A Bayesian multilevel analysis

Jung, H. W., & Park, H. (2025) · Public Health · doi:10.1016/j.puhe.2025.01.001 · Cited by 6

Reinforcement learning in integrated circuits: Design, synthesis, layout, and hardware security

Taşkıran, H., Hacımustafaoğlu, F. E., Afacan, E., & Dündar, G. (2025) · Integration · doi:10.1016/j.vlsi.2025.102460 · Cited by 5

The Fast Stochastic Matching Pursuit for neutrino and dark matter experiments

Wang, Y., Zhang, A., Wu, Y., Xu, B. D., Liu, X., Chen, J., Wang, Z., & Chen, S. (2025) · Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · doi:10.1016/j.nima.2025.170986 · Cited by 5

Using the EM algorithm for inference in a mixture of distributions with censored but partially identifiable data

Contreras‐Cristán, A. (2006) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2006.07.041 · Cited by 5

Bayesian background estimation

Fischer, R. D. (2001) · AIP conference proceedings · doi:10.1063/1.1381857 · Cited by 5

Statistical Analysis: Multilevel Methods

Mason, W. M. (2001) · Elsevier eBooks · doi:10.1016/b0-08-043076-7/00470-8 · Cited by 5

Hierarchical Bayesian estimation of motor-evoked potential recruitment curves yields accurate and robust estimates

Tyagi, V., Murray, L. M., Asan, A. S., Mandigo, C. E., Virk, M. S., Harel, N. Y., Carmel, J. B., & McIntosh, J. R. (2025) · Brain stimulation · doi:10.1016/j.brs.2025.09.008 · Cited by 4

Sparse Bayesian correntropy learning for robust muscle activity reconstruction from noisy brain recordings

Li, Y., Chen, B., Yoshimura, N., Koike, Y., & Yamashita, O. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106899 · Cited by 4

Bayesian Data Analysis Ed. 2

Gelman, A., Carlin, J. B., & Stern, H. S. (2003) · Taylor & Francis eBooks · Cited by 15

Bayesian methods for data analysis, third edition

Lu, Z. (2010) · Journal of Applied Statistics · doi:10.1080/02664760902811621 · Cited by 48

Computational Advances for and from Bayesian Analysis

Andrieu, C., Arnaud Doucet, B., & Robert, C. P. (2004) · Statistical Science · doi:10.1214/088342304000000071 · Cited by 62

Bayesian Statistical Methods

Reich, B. J., & Ghosh, S. K. (2019) · Chapman & Hall/CRC eBooks · doi:10.1201/9780429202292 · Cited by 59

Bayesian data analysis using R

Kerman, J., & Gelman, A. (2008)