The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Bayesian Data Analysis
Abstract
Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Using examples largely from the authors' own experiences, the book focuses on modern computational tools and obtains inferences using computer simulations. Its unique features include thorough discussions of the methods for
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The inverse-gamma(α, β) model for σ 2 α can also be expressed as an inverse-χ 2 distribution with scale s 2 α = β/α and degrees of freedom ν α = 2α (Gelman et al., 2003).”
From Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) (2006) · cites this paper as Gelman et al., 2003
In the citing paper
“With J = 1 or 2 groups, this actually results in an improper posterior density, essentially concluding σ α = ∞ and doing no shrinkage (see Gelman et al., 2003, Exercise 5.8).”
From Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper) (2006) · cites this paper as Gelman et al., 2003, Exercise 5.8
In the citing paper
“Their capacity to handle uncertainty, incorporate prior knowledge, and update beliefs based on evidence makes them effective for modeling complex systems (Gelman et al. 1995; MacKay 2003).”
From The myth of the Bayesian brain (2025) · cites this paper as Gelman et al. 1995
In the citing paper
“We adopt Bayesian statistics for its advantages in being able to hypothesize null effects[27,73].”
From Explanations Can Reduce Overreliance on AI Systems During Decision-Making (2023) · cites this paper as [27]
In the citing paper
“To improve the efficiency of this procedure, I use a Hamiltonian Monte Carlo algorithm (Gelman et al. 2013) and employ a hierarchical setup that considers each of the four sets of parameters as draws from four common population distributions:”
From Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data (2015) · cites this paper as Gelman et al. 2013
In the citing paper
“Because this model is a Bayesian extension of a conventional MFRM, its parameter estimates approach those of a non-Bayesian conventional MFRM when we have a large amount of data, which is a desirable property.”
From A Bayesian many-facet Rasch model with Markov modeling for rater severity drift (2022) · cites this paper as Gelman et al., 2013
In the citing paper
“The parameter estimates of a Bayesian model are known to approach those of its non-Bayesian counterpart as the amount of data increases. This is because the influence of the prior distribution decreases (Gelman et al., 2013).”
From A Bayesian many-facet Rasch model with Markov modeling for rater severity drift (2022) · cites this paper as Gelman et al., 2013
In the citing paper
“The use of model families is also accomodated naturally within the framework of hierarchical Bayesian models [1] and is proposed to address the well known issue of model dilution [4].”
From Comparing families of dynamic causal models (2010) · cites this paper as [1]
In the citing paper
“The hierarchical Bayesian approach is an intermediate method compared to the completely pooled model and individualized model, capturing the whole population’s characteristics while allowing individual differences [43].”
From Predicting depressive symptom severity through individuals’ nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study (2021) · cites this paper as [43]
Citations
Cites
(1988) · Cited by 16,582
Statistical Methods for Research Workers
Fisher, R. A. (1945) · American Journal of Clinical Pathology · doi:10.1093/ajcp/15.11.552b · Cited by 10,883
Jeffreys, H., Moran, P. A. P., & Chatfield, C. (1922) · Nature · doi:10.1038/109132a0 · Cited by 7,877
Bayesian Inference in Statistical Analysis.
Kadane, J. B., Box, G. E. P., & Tiao, G. C. (1975) · Journal of the American Statistical Association · doi:10.2307/2285412 · Cited by 3,853
Flagged first, then by citations
Cited by
Sorted by citations
Items 601–700 of 1,161
Bayesian Methods in Conservation Biology
Wade, P. R. (2000) · Conservation Biology · doi:10.1046/j.1523-1739.2000.99415.x · Cited by 325
Flynn, D. F. B., & Wolkovich, E. M. (2018) · New Phytologist · doi:10.1111/nph.15232 · Cited by 328
Mixed model prediction and small area estimation
Jiang, J., & Lahiri, P. (2006) · Test · doi:10.1007/bf02595419 · Cited by 328
Kumar, M., Torr, P. H. S., & Zisserman, A. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.249 · Cited by 327
Modelling functional integration: a comparison of structural equation and dynamic causal models
Penny, W. D., Stephan, K. E., Mechelli, A., & Friston, K. J. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.041 · Cited by 325
Advances in Collaborative Filtering
Koren, Y., Rendle, S., & Bell, R. (2022) · Recommender Systems Handbook · doi:10.1007/978-1-0716-2197-4_3 · Cited by 324
Analysis of cross category dependence in market basket selection
Russell, G. J., & Petersen, A. (2000) · Journal of Retailing · doi:10.1016/s0022-4359(00)00030-0 · Cited by 311
Kissling, W. D., Ahumada, J., Bowser, A., Fernández, M., Fernández, N., García, E. A., Guralnick, R., Isaac, N. J. B., Kelling, S., Los, W., et al. (2017) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12359 · Cited by 323
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysisPreprint
Benavoli, A., Giorgio Corani, A., Demšar, J., & Marco Zaffalon, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04316 · Cited by 324
Much of late life cognitive decline is not due to common neurodegenerative pathologies
Boyle, P. A., Wilson, R. S., Yu, L., Barr, A. M., Honer, W. G., Schneider, J. A., & Bennett, D. A. (2013) · Annals of Neurology · doi:10.1002/ana.23964 · Cited by 323
Brady, T. F., Konkle, T., & Alvarez, G. A. (2009) · Journal of Experimental Psychology General · doi:10.1037/a0016797 · Cited by 338
Spatial Inference of Admixture Proportions and Secondary Contact Zones
Durand, E., Jay, F., Gaggiotti, O. E., & François, O. (2009) · Molecular Biology and Evolution · doi:10.1093/molbev/msp106 · Cited by 331
Hierarchical diffusion models for two-choice response times.
Vandekerckhove, J., Tuerlinckx, F., & Lee, M. D. (2011) · Psychological Methods · doi:10.1037/a0021765 · Cited by 343
Ancient DNA Reveals Key Stages in the Formation of Central European Mitochondrial Genetic Diversity
Brandt, G., Haak, W., Adler, C. J., Roth, C., Szécsényi-Nagy, A., Karimnia, S., Möller-Rieker, S., Meller, H., Ganslmeier, R., Friederich, S., et al. (2013) · Science · doi:10.1126/science.1241844 · Cited by 322
Time-Varying Network Tomography: Router Link Data
Cao, J. L., Davis, D., Wiel, S. V., & Yu, B. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474303 · Cited by 322
Analyzing longitudinal data with missing values
Enders, C. K. (2011) · Rehabilitation Psychology · doi:10.1037/a0025579 · Cited by 321
THE NEUTRON STAR MASS DISTRIBUTION
Kiziltan, B., Kottas, A., Yoreo, M. D., & Thorsett, S. E. (2013) · The Astrophysical Journal · doi:10.1088/0004-637x/778/1/66 · Cited by 321
Validation of Software for Bayesian Models Using Posterior Quantiles
Cook, S. R., Gelman, A., & Rubin, D. B. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x136976 · Cited by 345
Self-citationGATHER: a systems approach to interpreting genomic signatures
Chang, J. T., & Nevins, J. R. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl483 · Cited by 315
Comparative Effectiveness of First-Line Medications for Primary Open-Angle Glaucoma
Li, T., Lindsley, K. B., Rouse, B., Hong, H., Shi, Q., Friedman, D. S., Wormald, R., & Dickersin, K. (2015) · Ophthalmology · doi:10.1016/j.ophtha.2015.09.005 · Cited by 323
Jansen, J. P., Trikalinos, T., Cappelleri, J. C., Daw, J., Andes, S., Eldessouki, R., & Salanti, G. (2014) · Value in Health · doi:10.1016/j.jval.2014.01.004 · Cited by 319
Polytomies and Bayesian Phylogenetic Inference
Lewis, P. O., Holder, M. T., & Holsinger, K. E. (2005) · Systematic Biology · doi:10.1080/10635150590924208 · Cited by 316
How to compare cross-lagged associations in a multilevel autoregressive model.
Schuurman, N. K., Ferrer, E. E., Sonnenschein, M., & Hamaker, E. L. (2016) · Psychological Methods · doi:10.1037/met0000062 · Cited by 318
Roy-Byrne, P. P., Craske, M. G., Stein, M. B., Sullivan, G., Bystritsky, A., Katon, W., Golinelli, D., & Sherbourne, C. D. (2005) · Archives of General Psychiatry · doi:10.1001/archpsyc.62.3.290 · Cited by 314
Bartneck, C., Duenser, A., Moltchanova, E., & Zawieska, K. (2015) · PLOS ONE · doi:10.1371/journal.pone.0121595 · Cited by 317
Estimation of parameters in complex 15N tracing models by Monte Carlo sampling
Müller, C., Rütting, T., Kattge, J., Laughlin, R. J., & Stevens, R. J. (2006) · Soil Biology and Biochemistry · doi:10.1016/j.soilbio.2006.09.021 · Cited by 319
Differential expression in SAGE: accounting for normal between-library variation
Baggerly, K., Deng, L., Morris, J. S., & Aldaz, C. M. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg173 · Cited by 316
The Optimizer’s Curse: Skepticism and Postdecision Surprise in Decision Analysis
Smith, J. E., & Winkler, R. L. (2006) · Management Science · doi:10.1287/mnsc.1050.0451 · Cited by 315
Oum, T. H., Yan, J., & Yu, C. (2008) · Journal of Urban Economics · doi:10.1016/j.jue.2008.03.001 · Cited by 315
Bayesian MCMC flood frequency analysis with historical information
Junior, D. S. R., & Stedinger, J. R. (2005) · Journal of Hydrology · doi:10.1016/j.jhydrol.2005.02.028 · Cited by 325
Bayesian Random-Effects Meta-Analysis Using the bayesmeta R Package
Röver, C. (2020) · Journal of Statistical Software · doi:10.18637/jss.v093.i06 · Cited by 310
Carpenter, S. R., Cole, J. J., Kitchell, J. F., & Pace, M. L. (1998) · Limnology and Oceanography · doi:10.4319/lo.1998.43.1.0073 · Cited by 310
Combining Multiple Imputation and Inverse‐Probability Weighting
Seaman, S. R., White, I. R., Copas, A. J., & Li, L. (2011) · Biometrics · doi:10.1111/j.1541-0420.2011.01666.x · Cited by 308
Bayesian analysis of empirical software engineering cost models
Chulani, S., BOEHM, B. W., & Steece, B. M. (1999) · IEEE Transactions on Software Engineering · doi:10.1109/32.799958 · Cited by 310
Efficient Construction of Reversible Jump Markov Chain Monte Carlo Proposal Distributions
Brooks, S. P., Giudici, P., & Roberts, G. O. (2003) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.03711 · Cited by 309
A Global Measure of Judicial Independence, 1948–2012
Linzer, D. A., & Staton, J. K. (2015) · Journal of Law and Courts · doi:10.1086/682150 · Cited by 314
Kantorová, V., Wheldon, M. C., Ueffing, P., & Dasgupta, A. (2020) · PLoS Medicine · doi:10.1371/journal.pmed.1003026 · Cited by 317
Brendel, K., Comets, E., Laffont, C. M., Laveille, C., & Mentré, F. (2006) · Pharmaceutical Research · doi:10.1007/s11095-006-9067-5 · Cited by 308
Cheal, A. J., MacNeil, M. A., Cripps, E. J., Emslie, M. J., Jonker, M. J., Schaffelke, B., & Sweatman, H. P. A. (2010) · Coral Reefs · doi:10.1007/s00338-010-0661-y · Cited by 308
Bayesian inversion for finite fault earthquake source models I—theory and algorithm
Minson, S. E., Simons, M., & Beck, J. L. (2013) · Geophysical Journal International · doi:10.1093/gji/ggt180 · Cited by 326
The cost of inbreeding in Arabidopsis
Bustamante, C. D., Nielsen, R., Sawyer, S. A., Olsen, K. M., Purugganan, M. D., & Hartl, D. L. (2002) · Nature · doi:10.1038/416531a · Cited by 311
How cognitive modeling can benefit from hierarchical Bayesian models
Lee, M. D. (2010) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2010.08.013 · Cited by 313
Quezada‐Romegialli, C., Jackson, A. L., Hayden, B., Kahilainen, K. K., Lopes, C., & Harrod, C. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13009 · Cited by 306
The optimal choice of climate change policy in the presence of uncertainty
Pizer, W. A. (1999) · Resource and Energy Economics · doi:10.1016/s0928-7655(99)00005-6 · Cited by 305
Walford, R. L., Mock, D., Verdery, R. B., & MacCallum, T. (2002) · The Journals of Gerontology Series A · doi:10.1093/gerona/57.6.b211 · Cited by 302
A psychometrics of individual differences in experimental tasks
Rouder, J. N., & Haaf, J. M. (2019) · Psychonomic Bulletin & Review · doi:10.3758/s13423-018-1558-y · Cited by 321
A tutorial introduction to Bayesian models of cognitive development
Perfors, A., Tenenbaum, J. B., Griffiths, T. L., & Xu, F. (2011) · Cognition · doi:10.1016/j.cognition.2010.11.015 · Cited by 300
Efficient simulation and integrated likelihood estimation in state space models
Chan, J. C. C., & Jeliazkov, I. (2009) · International Journal of Mathematical Modelling and Numerical Optimisation · doi:10.1504/ijmmno.2009.030090 · Cited by 303
WAND, M. P. (2003) · Computational Statistics · doi:10.1007/s001800300142 · Cited by 295
Of bits and wows: A Bayesian theory of surprise with applications to attention
Baldi, P., & Itti, L. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.12.007 · Cited by 294
Convergence Diagnostics for Markov Chain Monte Carlo
Roy, V. (2019) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-031219-041300 · Cited by 301
Demonstration of universal parametric entangling gates on a multi-qubit lattice
Reagor, M. J., Osborn, C. B., Tezak, N., Staley, A., Prawiroatmodjo, G. E. D. K., Scheer, M. G., Alidoust, N., Sete, E. A., Didier, N., Silva, M. P. D., et al. (2018) · Science Advances · doi:10.1126/sciadv.aao3603 · Cited by 298
Household cooking fuel estimates at global and country level for 1990 to 2030
Stoner, O., Lewis, J., Martínez, I. L., Gumy, S., Economou, T., & Adair‐Rohani, H. (2021) · Nature Communications · doi:10.1038/s41467-021-26036-x · Cited by 299
Comparing dynamic causal models using AIC, BIC and free energy
Penny, W. D. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.039 · Cited by 298
When Null Hypothesis Significance Testing Is Unsuitable for Research: A Reassessment
Szucs, D., & Ioannidis, J. P. A. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00390 · Cited by 298
Learning hierarchical models of scenes, objects, and parts
Sudderth, E. B., Torralba, A. B., Freeman, W. T., & Willsky, A. S. (2005) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2005.137 · Cited by 297
Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy
Sharma, S. (2017) · Annual Review of Astronomy and Astrophysics · doi:10.1146/annurev-astro-082214-122339 · Cited by 298
Effat, M. B., & Francesco Ciucci, A. (2017) · Electrochimica Acta · doi:10.1016/j.electacta.2017.07.050 · Cited by 300
Schultzberg, M., & Muthén, B. (2017) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2017.1392862 · Cited by 291
A machine learning Automated Recommendation Tool for synthetic biology
Radivojević, T., Costello, Z., Workman, K., & Martin, H. G. (2020) · Nature Communications · doi:10.1038/s41467-020-18008-4 · Cited by 314
A Statistical Model for Multiparty Electoral Data
Katz, J. N., & King, G. (1999) · American Political Science Review · doi:10.2307/2585758 · Cited by 293
Climate Warming, Marine Protected Areas and the Ocean-Scale Integrity of Coral Reef Ecosystems
Graham, N. A. J., McClanahan, T. R., MacNeil, M. A., Wilson, S. K., Polunin, N. V. C., Jennings, S., Chabanet, P., Clark, S., Spalding, M. D., Letourneur, Y., et al. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003039 · Cited by 301
Døving, E., & Gooderham, P. N. (2008) · Strategic Management Journal · doi:10.1002/smj.683 · Cited by 295
STRUCTURE AND EVOLUTION OF PRE-MAIN-SEQUENCE CIRCUMSTELLAR DISKS
Isella, A., Carpenter, J. M., & Sargent, A. I. (2009) · The Astrophysical Journal · doi:10.1088/0004-637x/701/1/260 · Cited by 295
Multilevel data and Bayesian analysis in traffic safety
Huang, H., & Abdel-Aty, M. A. (2010) · Accident Analysis & Prevention · doi:10.1016/j.aap.2010.03.013 · Cited by 295
The Power of Single-Nucleotide Polymorphisms for Large-Scale Parentage Inference
Anderson, E. C., & Garza, J. C. (2005) · Genetics · doi:10.1534/genetics.105.048074 · Cited by 292
Applying Generalized Linear Models
Chen, H. (1998) · Technometrics · doi:10.1080/00401706.1998.10485202 · Cited by 295
A tutorial on approximate Bayesian computation
Turner, B. M., & Zandt, T. V. (2012) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2012.02.005 · Cited by 294
Depensation in fish stocks: a hierarchic Bayesian meta-analysis
Liermann, M. C., & Hilborn, R. W. (1997) · Canadian Journal of Fisheries and Aquatic Sciences · doi:10.1139/f97-105 · Cited by 293
Rupp, A. A., & Templin, J. L. (2008) · Measurement Interdisciplinary Research and Perspectives · doi:10.1080/15366360802490866 · Cited by 293
PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals
Biwer, C. M., Capano, C. D., De, S., Cabero, M., Brown, D. A., Nitz, A., & Raymond, V. (2019) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/aaef0b · Cited by 345
Deviance Information Criterion for Comparing Stochastic Volatility Models
Berg, A., Meyer, R., & Yu, J. (2004) · Journal of Business and Economic Statistics · doi:10.1198/073500103288619430 · Cited by 292
Tropical forest dynamics across a rainfall gradient and the impact of an El Niño dry season
Condit, R., Aguilar, S., Hernández, A., Pérez, R., Lao, S. L. D., Angehr, G. R., Hubbell, S. P., & Foster, R. B. (2004) · Journal of Tropical Ecology · doi:10.1017/s0266467403001081 · Cited by 290
Probabilistic Tsunami Hazard Analysis: Multiple Sources and Global Applications
Grezio, A., Babeyko, A. Y., Baptista, M. A., Behrens, J., Costa, A., Davies, G., Geist, E. L., Glimsdal, S., Gonzalez, F. I., Griffin, J., et al. (2017) · Reviews of Geophysics · doi:10.1002/2017rg000579 · Cited by 290
Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review
Thombre, S., Zhao, Z. M., Ramm-Schmidt, H., García, J. M. V., Malkamäki, T., Nikolskiy, S., Hammarberg, T., Nuortie, H., Bhuiyan, M. Z. H., Särkkä, S., et al. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3023957 · Cited by 321
Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach
Klauer, K. C. (2009) · Psychometrika · doi:10.1007/s11336-009-9141-0 · Cited by 289
Olden, J. D., Kennard, M. J., & Pusey, B. J. (2011) · Ecohydrology · doi:10.1002/eco.251 · Cited by 289
A Prospective Study of Chronic Pain after Thoracic Surgery
Bayman, E. O., Parekh, K. R., Keech, J. C., Selte, A., & Brennan, T. J. (2017) · Anesthesiology · doi:10.1097/aln.0000000000001576 · Cited by 291
Natural computing for mechanical systems research: A tutorial overview
Worden, K., Staszewski, W. J., & Hensman, J. J. (2010) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2010.07.013 · Cited by 297
A multispecies occupancy model for two or more interacting species
Rota, C. T., Ferreira, M. A. R., Kays, R., Forrester, T. D., Kalies, E. L., McShea, W. J., Parsons, A. W., & Millspaugh, J. J. (2016) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12587 · Cited by 288
Markov Chain Monte Carlo: Can We Trust the Third Significant Figure?
Flegal, J. M., Haran, M., & Jones, G. L. (2008) · Statistical Science · doi:10.1214/08-sts257 · Cited by 287
Estimating the Integrated Likelihood via Posterior Simulation Using the Harmonic Mean Identity
Raftery, A. E., Newton, M. A., Satagopan, J. M., & Krivitsky, P. N. (2007) · doi:10.1093/oso/9780199214655.003.0015 · Cited by 287
Synthesizing multiple data types for biological conservation using integrated population models
Zipkin, E. F., & Saunders, S. P. (2017) · Biological Conservation · doi:10.1016/j.biocon.2017.10.017 · Cited by 287
Bayesian Data Fusion for Adaptable Image Pansharpening
Fasbender, D., Radoux, J., & Bogaert, P. (2008) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2008.917131 · Cited by 285
The learnability of abstract syntactic principles
Perfors, A., Tenenbaum, J. B., & Regier, T. (2010) · Cognition · doi:10.1016/j.cognition.2010.11.001 · Cited by 303
An Empirical Bayes Approach to Mantel‐Haenszel DIF Analysis
Zwick, R., Thayer, D. T., & Lewis, C. (1999) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.1999.tb00543.x · Cited by 285
Capacity of a Diffusion-Based Molecular Communication System With Channel Memory and Molecular Noise
Pierobon, M., & Akyildiz, I. F. (2012) · IEEE Transactions on Information Theory · doi:10.1109/tit.2012.2219496 · Cited by 285
Why Inflation Rose and Fell: Policy-Makers' Beliefs and U. S. Postwar Stabilization Policy*
Primiceri, G. E. (2006) · The Quarterly Journal of Economics · doi:10.1162/qjec.121.3.867 · Cited by 285
Sparse Learning via Iterative Minimization With Application to MIMO Radar Imaging
Tan, X. G., Roberts, W. J., Li, J., & Stoica, P. (2010) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2010.2096218 · Cited by 287
Users Polarization on Facebook and Youtube
Bessi, A., Zollo, F., Del Vicario, M., Puliga, M., Scala, A., Caldarelli, G., Uzzi, B., & Quattrociocchi, W. (2016) · PLOS ONE · doi:10.1371/journal.pone.0159641 · Cited by 285
The drift diffusion model as the choice rule in reinforcement learning
Pedersen, M. L., Frank, M. J., & Biele, G. (2016) · Psychonomic Bulletin & Review · doi:10.3758/s13423-016-1199-y · Cited by 295
A guide to state–space modeling of ecological time series
Auger‐Méthé, M., Newman, K. B., Cole, D. J., Empacher, F., Gryba, R., King, A. A., Barajas, V. L., Flemming, J. M., Nielsen, A., Petris, G., et al. (2021) · Ecological Monographs · doi:10.1002/ecm.1470 · Cited by 335
Expanded uncertainty quantification in inverse problems: Hierarchical Bayes and empirical Bayes
Malinverno, A., & Briggs, V. A. (2004) · Geophysics · doi:10.1190/1.1778243 · Cited by 283
Low‐Choy, S., O’Leary, R., & Mengersen, K. L. (2009) · Ecology · doi:10.1890/07-1886.1 · Cited by 283
BAYSPLINE: A New Calibration for the Alkenone Paleothermometer
Tierney, J. E., & Tingley, M. P. (2018) · Paleoceanography and Paleoclimatology · doi:10.1002/2017pa003201 · Cited by 294
A method for efficiently sampling from distributions with correlated dimensions.
Turner, B. M., Sederberg, P. B., Brown, S. D., & Mark Steyvers, B. (2013) · Psychological Methods · doi:10.1037/a0032222 · Cited by 291
Bayesian Factor Analysis for Mixed Ordinal and Continuous Responses
Quinn, K. M. (2004) · Political Analysis · doi:10.1093/pan/mph022 · Cited by 282
Learning Bayesian Network Model Structure from Data
Margaritis, D. (2003) · Cited by 282
Sen, S., Vig, J., & Riedl, J. (2009) · International World Wide Web Conference (WWW) · doi:10.1145/1526709.1526800 · Cited by 282
Developmental Profiles of Eczema, Wheeze, and Rhinitis: Two Population-Based Birth Cohort Studies
Belgrave, D. C. M., Granell, R., Simpson, A., Guiver, J., Bishop, C., Buchan, I. E., Henderson, A. J., & Čustović, A. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001748 · Cited by 293
Metadata
- Authors
- Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B.
- Affiliations
- Royal Children's Hospital (AU)
- Venue
- Chapman & Hall/CRC eBooks
- Publisher
- Chapman and Hall/CRC
- Published
- 2003-07-29
- OpenAlex
- W2045656233
- Cited by
- 27,276
- References
- 4
- Citations by year
- 2026: 1089 · 2025: 1536 · 2024: 1370 · 2023: 1223 · 2022: 1481 · 2021: 1849 · 2020: 1770 · 2019: 1645 · 2018: 1347 · 2017: 1184 · 2016: 1184 · 2015: 1171 · 2014: 1048 · 2013: 1060 · 2012: 1052
- FWCI
- 48.90
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Chapman & Hall/CRC eBooks (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- Bayesian analysis, Bayesian inference, computational tools, computer simulation, statistical modeling, model criticism
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
