Fetching the paper…
Reading the bibliography…
The Bayesian approach to data analysis provides a powerful way to handle uncertainty in all observations, model parameters, and model structure using probability theory.
eming, W. E., and Stephan, F. F. (1940). On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. Annals of Mathematical Statistics
1940
Earlier work this paper cites.
enderson, C. R. (1950). Estimation of genetic parameters (abstract). Annals of Mathematical Statistics
1950
Earlier work this paper cites.
indley, D. V. (1956). On a measure of the information provided by an experiment. Annals of Mathematical Statistics
1956
Earlier work this paper cites.
acquez, J. A. (1972). Compartmental Analysis in Biology and Medicine
1972
Earlier work this paper cites.
ovick, M. R., Jackson, P. H., Thayer, D. T., and Cole, N. S. (1972). Estimating multiple regressions in m m -groups: a cross validation study. British Journal of Mathematical and Statistical Psychology
1972
Earlier work this paper cites.
allows, C. L. (1973). Some comments on C p C_{p} . Technometrics
1973
Earlier work this paper cites.
tone, M. (1974). Cross-validatory choice and assessment of statistical predictions (with discussion). Journal of the Royal Statistical Society B
1974
Earlier work this paper cites.
tone, M. (1977). An asymptotic equivalence of choice of model cross-validation and Akaike’s criterion. Journal of the Royal Statistical Society B
1977
Earlier work this paper cites.
ox, G. E. P. (1980). Sampling and Bayes inference in scientific modelling and robustness. Journal of the Royal Statistical Society A
1980
Earlier work this paper cites.
oel, P. K., and DeGroot, M. H. (1981). Information about hyperparameters in hierarchical models. Journal of the American Statistical Association
1981
Earlier work this paper cites.
ubin, D. B. (1984). Bayesianly justifiable and relevant frequency calculations for the applied statistician. Annals of Statistics
1984
Earlier work this paper cites.
ierney, L., and Kardane, J.B. (1986). Accurate approximations for posterior moments and marginal densities. Journal of the American Statistical Association
1986
Earlier work this paper cites.
kaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Proceedings of the Second International Symposium on Information Theory
1992
Earlier work this paper cites.
layton, D. G. (1992). Models for the analysis of cohort and case-control studies with inaccurately measured exposures. In Statistical Models for Longitudinal Studies of Exposure and Health
1992
Earlier work this paper cites.
eal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto
1993
Earlier work this paper cites.
ichardson, S., and Gilks, W. R. (1993). A Bayesian approach to measurement error problems in epidemiology using conditional independence models. American Journal of Epidemiology
1993
Earlier work this paper cites.
erry, D. (1995). Statistics: A Bayesian Perspective
1995
Earlier work this paper cites.
elman, A., Bois, F. Y., and Jiang, J. (1996). Physiological pharmacokinetic analysis using population modeling and informative prior distributions. Journal of the American Statistical Association
1996
Earlier work this paper cites.
elman, A., Meng, X. L., and Stern, H. S. (1996). Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Statistica Sinica
1996
Earlier work this paper cites.
rice, P. N., Nero, A. V., and Gelman, A. (1996). Bayesian prediction of mean indoor radon concentrations for Minnesota counties. Health Physics
1996
Earlier work this paper cites.
unt, A., and Thomas, D. (1999). The Pragmatic Programmer
1999
Earlier work this paper cites.
in, C. Y., Gelman, A., Price, P. N., and Krantz, D. H. (1999). Analysis of local decisions using hierarchical modeling, applied to home radon measurement and remediation (with discussion). Statistical Science
1999
Earlier work this paper cites.
inger, E., Van Hoewyk, J., Gebler, N., Raghunathan, T., and McGonagle, K. (1999). The effects of incentives on response rates in interviewer-mediated surveys. Journal of Official Statistics
1999
Earlier work this paper cites.
eng, X. L., and van Dyk, D. A. (2001). The art of data augmentation. Journal of Computational and Graphical Statistics
2001
Earlier work this paper cites.
audenbush, S. W., and Bryk, A. S. (2002). Hierarchical Linear Models
2002
Earlier work this paper cites.
elman, A. (2003). A Bayesian formulation of exploratory data analysis and goodness-of-fit testing. International Statistical Review
2003
Earlier work this paper cites.
elman, A., Stevens, M., and Chan, V. (2003). Regression modeling and meta-analysis for decision making: A cost-benefit analysis of a incentives in telephone surveys. Journal of Business and Economic Statistics
2003
Earlier work this paper cites.
2003
Earlier work this paper cites.
nwin, A., Volinsky, C., and Winkler, S. (2003). Parallel coordinates for exploratory modelling analysis. Computational Statistics and Data Analysis
2003
Earlier work this paper cites.
elman, A. (2004). Parameterization and Bayesian modeling. Journal of the American Statistical Association
2004
Earlier work this paper cites.
erman, J., and Gelman, A. (2004). Fully Bayesian computing. www.stat.columbia.edu/~gelman/research/unpublished/fullybayesiancomputing-nonblinded.pdf
2004
Earlier work this paper cites.
iu, Y., Harding, A., Gilbert, R., and Journel, A. G. (2005). A workflow for multiple-point geostatistical simulation. In Geostatistics Banff 2004
2004
Earlier work this paper cites.
2004
Earlier work this paper cites.
cConnell, S. (2004). Code Complete
2004
Earlier work this paper cites.
krondal, A. and Rabe-Hesketh, S. (2004). Generalized Latent Variable Modeling: Multilevel, Longitudinal and Structural Equation Models
2004
Earlier work this paper cites.
ook, S., Gelman, A., and Rubin, D. B. (2006). Validation of software for Bayesian models using posterior quantiles. Journal of Computational and Graphical Statistics
2006
Earlier work this paper cites.
’Hagan, A., Buck, C. E., Daneshkhah, A., Eiser, J. R., Garthwaite, P. H., Jenkinson, D. J., Oakely, J. E., and Rakow, T. (2006). Uncertain Judgements: Eliciting Experts’ Probabilities
2006
Earlier work this paper cites.
asmussen, C. E., and Williams, C. K. I. (2006). Gaussian Processes for Machine Learning
2006
Earlier work this paper cites.
2006
Earlier work this paper cites.
litzer, J., Dredze, M., and Pereira, F. (2007). Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics
2007
Earlier work this paper cites.
elman, A., and Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models
2007
Earlier work this paper cites.
erman, J., and Gelman, A. (2007). Manipulating and summarizing posterior simulations using random variable objects. Statistics and Computing
2007
Earlier work this paper cites.
ickham, H. (2006). Exploratory model analysis with R and GGobi. had.co.nz/model-vis/2007-jsm.pdf
2007
Earlier work this paper cites.
ins, L., Koop, D., Anderson, E. W., Callahan, S. P., Santos, E., Scheidegger, C. E., Freire, J., and Silva, C. T. (2008). Examining statistics of workflow evolution provenance: A first study. In Scientific and Statistical Database Management, SSDBM 2008
2008
Earlier work this paper cites.
hi, X., and Stevens, R. (2008). SWARM: a scientific workflow for supporting bayesian approaches to improve metabolic models. CLADE ’08: Proceedings of the 6th International Workshop on Challenges of Large Applications in Distributed Environments
2008
Earlier work this paper cites.
2008
Earlier work this paper cites.
erger, J. O., Bernardo, J. M., and Sun, D. (2009). The formal definition of reference priors. Annals of Statistics
2009
Earlier work this paper cites.
aumé, H. (2009). Frustratingly easy domain adaptation. arxiv.org/abs/0907.1815
2009
Earlier work this paper cites.
inkel, J. R., and Manning, C. D. (2009). Hierarchical Bayesian domain adaptation. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
2009
Earlier work this paper cites.
ong, J. S. (2009). The Workflow of Data Analysis Using Stata
2009
Earlier work this paper cites.
irš, G., and Štrumbelj, E. (2009). Bayesian combination of probabilistic classifiers using multivariate normal mixtures. Journal of Machine Learning Research
2009
Earlier work this paper cites.
ue, H., Martino, S., and Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society B
2009
Earlier work this paper cites.
2009
Cited alongside, same era.
odges, J. S., and Reich, B. J. (2010). Adding spatially-correlated errors can mess up the fixed effect you love. American Statistician
2010
Cited alongside, same era.
ontgomery, J. M., and Nyhan, B. (2010). Bayesian model averaging: Theoretical developments and practical applications. Political Analysis
2010
Cited alongside, same era.
elman, A. (2011). Expanded graphical models: Inference, model comparison, model checking, fake-data debugging, and model understanding. www.stat.columbia.edu/~gelman/presentations/ggr2handout.pdf
2011
Cited alongside, same era.
ill, J. L. (2011). Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics
iordano, R. (2018). StanSensitivity. github.com/rgiordan/StanSensitivity
2018
Later among the works it cites.
ayo, D. (2018). Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars
2018
Later among the works it cites.
illar, R. B. (2018). Conditional vs marginal estimation of the predictive loss of hierarchical models using WAIC and cross-validation. Statistics and Computing
2018
Later among the works it cites.
odrák, M. (2018). Reparameterizing the sigmoid model of gene regulation for Bayesian inference. Computational Methods in Systems Biology. CMSB 2018. Lecture Notes in Computer Science, vol. 11095
2018
Later among the works it cites.
iebler, A., Sørbye, S. H., Simpson, D., and Rue, H. (2018). An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research
2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2011
Cited alongside, same era.
eal, R. M. (2011). MCMC using Hamiltonian dynamics. In Handbook of Markov Chain Monte Carlo
2011
Cited alongside, same era.
earl, J., and Bareinboim, E. (2011). Transportability of causal and statistical relations: A formal approach. In Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference
2011
Cited alongside, same era.
obert, C., and Casella, G. (2011). A short history of Markov chain Monte Carlo: Subjective recollections from incomplete data. Statistical Science
2011
Cited alongside, same era.
immons, J., Nelson, L., and Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allow presenting anything as significant. Psychological Science
2011
Cited alongside, same era.
elman, A., Hill, J., and Yajima, M. (2012). Why we (usually) don’t have to worry about multiple comparisons. Journal of Research on Educational Effectiveness
2012
Cited alongside, same era.
erk, R., Brown, L., Buja, A., Zhang, K., and Zhao, L. (2013). Valid post-selection inference. Annals of Statistics
2013
Cited alongside, same era.
hung, Y., Rabe-Hesketh, S., Gelman, A., Liu, J. C., and Dorie, A. (2013). A non-degenerate penalized likelihood estimator for hierarchical variance parameters in multilevel models. Psychometrika
2013
Cited alongside, same era.
Later among the works it cites.
2018
Later among the works it cites.
hirani-Mehr, H., Rothschild, D., Goel, S., and Gelman, A. (2018). Disentangling bias and variance in election polls. Journal of the American Statistical Association
2018
Later among the works it cites.
aylor, S. J., and Lethem, B. (2018). Forecasting at scale. American Statistician
2018
Later among the works it cites.
eber, S., Gelman, A., Lee, D., Betancourt, M., Vehtari, A., and Racine-Poon, A. (2018). Bayesian aggregation of average data: An application in drug development. Annals of Applied Statistics
2018
Later among the works it cites.
roadie, M. (2018). Two simple putting models in golf. statmodeling.stat.columbia.edu/wp-content/uploads/2019/03/putt_models_20181017.pdf
2019
Later among the works it cites.
hen, C., Li, O., Barnett, A., Su, J., and Rudin, C. (2019). This looks like that: Deep learning for interpretable image recognition. 33rd Conference on Neural Information Processing Systems
2019
Later among the works it cites.
evezer, B., Nardin, L. G., Baumgaertner, B., and Buzbas, E. O. (2019). Scientific discovery in a model-centric framework: Reproducibility, innovation, and epistemic diversity. PLoS One
2019
Later among the works it cites.
ragicevic, P., Jansen, Y., Sarma, A., Kay, M., and Chevalier, F. (2019). Increasing the transparency of research papers with explorable multiverse analyses. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
2019
Later among the works it cites.
uglstad, G. A., Simpson, D., Lindgren, F., and Rue, H. (2019). Constructing priors that penalize the complexity of Gaussian random fields. Journal of the American Statistical Association
2019
Later among the works it cites.
abry, J., Simpson, D., Vehtari, A., Betancourt, M., and Gelman, A. (2019). Visualization in Bayesian workflow (with discussion and rejoinder). Journal of the Royal Statistical Society A
2019
Later among the works it cites.
elman, A. (2019). Model building and expansion for golf putting. Stan Case Studies
2019
Later among the works it cites.
haramani, Z., Steinruecken, C., Smith, E., Janz, E., and Peharz, R. (2019). The Automatic Statistician: An artificial intelligence for data science. www.automaticstatistician.com/index
2019
Later among the works it cites.
ale, A., Kay, M., and Hullman, J. (2019). Decision-making under uncertainty in research synthesis: Designing for the garden of forking paths. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
2019
Later among the works it cites.
ennedy, L., Simpson, D., and Gelman, A. (2019). The experiment is just as important as the likelihood in understanding the prior: A cautionary note on robust cognitive modeling. Computational Brain and Behavior
2019
Later among the works it cites.
umar, R., Carroll, C., Hartikainen, A., and Martin, O. A. (2019). ArviZ a unified library for exploratory analysis of Bayesian models in Python. Journal of Open Source Software
2019
Later among the works it cites.
ee, M. D., Criss, A. H., Devezer, B., Donkin, C., Etz, A., Leite, F. P., Matzke, D., Rouder, J. N., Trueblood, J. S., White, C. N., and Vandekerckhove, J. (2019). Robust modeling in cognitive science. Computational Brain and Behavior
2019
Later among the works it cites.
erkle, E. C., Furr, D., and Rabe-Hesketh, S. (2019). Bayesian comparison of latent variable models: Conditional versus marginal likelihoods. Psychometrika
2019
Later among the works it cites.
avarro, D. J. (2019). Between the devil and the deep blue sea: Tensions between scientific judgement and statistical model selection. Computational Brain and Behavior
2019
Later among the works it cites.
ehtari, A. (2019). Cross-validation for hierarchical models. avehtari.github.io/modelselection/rats_kcv.html
2019
Later among the works it cites.
frabandpey, H., Peltola, T., Piironen, J., Vehtari, A., and Kaski, S. (2020). Making Bayesian predictive models interpretable: A decision theoretic approach. Machine Learning
2020
Closest in time.
evezer, B., Navarro, D. J., Vanderkerckhove, J., and Buzbas, E. O. (2020). The case for formal methodology in scientific reform. doi.org/10.1101/2020.04.26.048306
2020
Closest in time.
laxman, S., Mishra, S., Gandy, A., et al. (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature
2020
Closest in time.
elman, A., et al. (2020). Prior choice recommendations. github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations
2020
Closest in time.
elman, A., Hill, J., and Vehtari, A. (2020). Regression and Other Stories
2020
Closest in time.
elman, A., Hullman, J., Wlezien, C., and Morris, G. E. (2020). Information, incentives, and goals in election forecasts. Judgment and Decision Making
2020
Closest in time.
hitza, Y., and Gelman, A. (2020). Voter registration databases and MRP: Toward the use of large scale databases in public opinion research. Political Analysis
2020
Closest in time.
rinsztajn, L., Semenova, E., Margossian, C. C., and Riou, J. (2020). Bayesian workflow for disease transmission modeling in Stan. mc-stan.org/users/documentation/case-studies/boarding_school_case_study.html
2020
Closest in time.
offman, M., and Ma, Y. (2020). Black-box variational inference as a parametric approximation to Langevin dynamics. Proceedings of Machine Learning and Systems
2020
Closest in time.
argossian, C. C, and Gelman, A (2020). Bayesian Model of Planetary Motion: exploring ideas for a modeling workflow when dealing with ordinary differential equations and multimodality. Technical Report
2020
Closest in time.
argossian, C. C., Vehtari, A., Simpson, D., and Agrawal, R. (2020b). Approximate Bayesian inference for latent Gaussian models in Stan. Stan Con 2020
2020
Closest in time.
orris, G. E., Gelman, A., and Heidemanns, M. (2020). How the Economist presidential forecast works. projects.economist.com/us-2020-forecast/president/how-this-works
2020
Closest in time.
avarro, D. J. (2020). If mathematical psychology did not exist we might need to invent it: A comment on theory building in psychology. Perspectives on Psychological Science
2020
Closest in time.
ott, D. J., Wang, X., Evans, M., and Englert, B. G. (2020). Checking for prior-data conflict using prior-to-posterior divergences. Statistical Science
2020
Closest in time.
aananen, T., Piironen, J., Bürkner, P.-C., and Vehtari, A. (2020). Implicitly adaptive importance sampling. Statistics and Computing
2020
Closest in time.
arma, A., and Kay, M. (2020). Prior setting in practice: Strategies and rationales used in choosing prior distributions for Bayesian analysis. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
2020
Closest in time.
tan Development Team (2020). Stan User’s Guide
2020
Closest in time.
alts, S., Betancourt, M., Simpson, D., Vehtari, A., and Gelman, A. (2020). Validating Bayesian inference algorithms with simulation-based calibration. www.stat.columbia.edu/~gelman/research/unpublished/sbc.pdf
2020
Closest in time.
ehtari A., Gabry J., Magnusson M., Yao Y., Bürkner P., Paananen T., Gelman A. (2020). loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models. R package version 2.3.1, mc-stan.org/loo
2020
Closest in time.
ehtari, A., and Gabry, J. (2020). Bayesian stacking and pseudo-BMA weights using the loo package. mc-stan.org/loo/articles/loo2-weights.html
2020
Closest in time.
ehtari, A., Gelman, A., Simpson, D., Carpenter, D., and Bürkner, P.-C. (2020). Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC. Bayesian Analysis
2020
Closest in time.
ehtari, A., Gelman, A., Sivula, T., Jylanki, P., Tran, D., Sahai, S., Blomstedt, P., Cunningham, J. P., Schiminovich, D., and Robert, C. P. (2020). Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data. Journal of Machine Learning Research
2020
Closest in time.
u, B., and Kumbier, K. (2020). Veridical data science. Proceedings of the National Academy of Sciences
2020
Closest in time.
hang, Y. D., Naughton, B. P., Bondell, H. D., and Reich, B. J. (2020). Bayesian regression using a prior on the model fit: The R2-D2 shrinkage prior. Journal of the American Statistical Association
2020
Closest in time.
iordano, R., Broderick, T., and Jordan, M. I. (2018). Covariances, robustness, and variational Bayes. Journal of Machine Learning Research
2029
Closest in time.
2044
Closest in time.
iederlová, V., Modrák, M., Tsyklauri, O., Huranová, M., and Štěpánek, O. (2019). Meta-analysis of genotype-phenotype associations in Bardet-Biedl Syndrome uncovers differences among causative genes. Human Mutation
2087
Closest in time.