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Bayesian modeling provides a principled approach to quantifying uncertainty in model parameters and model structure and has seen a surge of applications in recent years.
Rank-normalization, folding, and localization: An improved $\widehat{R}$ for assessing convergence of MCMC
Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and Paul-Christian Bürkner · 1903
Earlier work this paper cites.
A new class of flexible link functions with application to species co-occurrence in cape floristic region
Xun Jiang, Dipak K. Dey, Rachel Prunier, Adam M. Wilson, and Kent E. Holsinger · 1932
Earlier work this paper cites.
A survey of Bayesian predictive methods for model assessment, selection and comparison
Aki Vehtari and Janne Ojanen · 1935
Earlier work this paper cites.
Causal inference in statistics: An overview
Judea Pearl · 1935
Earlier work this paper cites.
Generalized linear models
John Ashworth Nelder and Robert WM Wedderburn · 1972
Earlier work this paper cites.
Estimating causal effects of treatments in randomized and nonrandomized studies
Donald B Rubin · 1974
Earlier work this paper cites.
Bayesian inference for causal effects: The role of randomization
Donald B Rubin · 1978
Earlier work this paper cites.
A generalized probability density function for double-bounded random processes
P. Kumaraswamy · 1980
Earlier work this paper cites.
Logistic-normal distributions:Some properties and uses
J. Atchison and S.M. Shen · 1980
Earlier work this paper cites.
Comparing the predictive powers of alternative multiple regression models
Michael R Hagerty and V Srinivasan · 1991
Earlier work this paper cites.
Some parametric models on the simplex
O. E. Barndorff-Nielsen and B. Jørgensen · 1991
Earlier work this paper cites.
A note on Wadley’s problem with overdispersion
Byron JT Morgan and DM Smith · 1992
Earlier work this paper cites.
Multivariate statistical modelling based on generalized linear models , volume 425
Ludwig Fahrmeir, Gerhard Tutz, Wolfgang Hennevogl, and Eliane Salem · 1994
Earlier work this paper cites.
Continuous Univariate Distributions, Volume 2
Norman L. Johnson, Samuel Kotz, and Narayanaswamy Balakrishnan · 1995
Earlier work this paper cites.
Bayesian deviance, the effective number of parameters, and the comparison of arbitrarily complex models
David J Spiegelhalter, Nicola G Best, Bradley P Carlin, and A Van der Linde · 1998
Earlier work this paper cites.
Incorporating second-order functional knowledge for better option pricing
Charles Dugas, Yoshua Bengio, François Bélisle, Claude Nadeau, and René Garcia · 2000
Earlier work this paper cites.
Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
Leo Breiman · 2001
Earlier work this paper cites.
Generalized linear models: a unified approach , volume 134
Jeff Gill, Jefferson M Gill, Michelle Torres, and Silvia Michelle Torres Pacheco · 2001
Earlier work this paper cites.
Univariate discrete distributions
Norman L Johnson, Samuel Kotz, and Adrienne W Kemp · 2005
Earlier work this paper cites.
Data analysis using regression and multilevel/hierarchical models
Andrew Gelman and Jennifer Hill · 2006
Earlier work this paper cites.
Generalized additive models for location scale and shape (GAMLSS) in R
D Mikis Stasinopoulos and Robert A Rigby · 2007
Earlier work this paper cites.
The seven properties of good models
Xavier Gabaix and David Laibson · 2008
Earlier work this paper cites.
On beta regression residuals
Patrícia L Espinheira, Silvia LP Ferrari, and Francisco Cribari-Neto · 2008
Earlier work this paper cites.
Statistical methods for categorical data analysis
Daniel Powers and Yu Xie · 2008
Earlier work this paper cites.
Reintroducing prediction to explanation
Heather E Douglas · 2009
Earlier work this paper cites.
The elements of statistical learning: data mining, inference, and prediction , volume 2
Trevor Hastie, Robert Tibshirani, Jerome H Friedman, and Jerome H Friedman · 2009
Earlier work this paper cites.
Algebraic geometry and statistical learning theory , volume 25
Sumio Watanabe · 2009
Cited alongside, same era.
Parametric links for binary choice models: A Fisherian–Bayesian colloquy
Roger Koenker and Jungmo Yoon · 2009
Cited alongside, same era.
To Explain or to Predict?
Galit Shmueli · 2010
Cited alongside, same era.
Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory
Sumio Watanabe and Manfred Opper · 2010
Cited alongside, same era.
The VGAM package for categorical data analysis
Thomas W Yee · 2010
Cited alongside, same era.
Andrew Gelman, Aki Vehtari, Daniel Simpson, Charles C. Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Bürkner, and Martin Modrák · 2011
Between the Devil and the Deep Blue Sea: Tensions Between Scientific Judgement and Statistical Model Selection
Danielle J. Navarro · 2019
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A second chance to get causal inference right: a classification of data science tasks
Miguel A Hernán, John Hsu, and Brian Healy · 2019
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Machine learning interpretability: A survey on methods and metrics
Diogo V Carvalho, Eduardo M Pereira, and Jaime S Cardoso · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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Visualization in Bayesian workflow
Jonah Gabry, Daniel Simpson, Aki Vehtari, Michael Betancourt, and Andrew Gelman · 2019
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Cited alongside, same era.
Judea Pearl · 2012
Cited alongside, same era.
Bayesian Data Analysis
Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin · 2013
Cited alongside, same era.
Measurement bias and effect restoration in causal inference
Manabu Kuroki and Judea Pearl · 2014
Cited alongside, same era.
Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis
Frank E Harrell · 2015
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Fitting Linear Mixed-Effects Models Using \pkglme4
Douglas Bates, Martin Mächler, Ben Bolker, and Steve Walker · 2015
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Improving deep neural networks using softplus units
Hao Zheng, Zhanlei Yang, Wenju Liu, Jizhong Liang, and Yanpeng Li · 2015
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Statistical Rethinking: A Bayesian Course with Examples in R and Stan
Richard McElreath · 2020
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Efficient adjustment sets for population average causal treatment effect estimation in graphical models
Andrea Rotnitzky and Ezequiel Smucler · 2020
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A Crash Course in Good and Bad Controls
Carlos Cinelli, Andrew Forney, and Judea Pearl · 2020
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Improving the accuracy of medical diagnosis with causal machine learning
Jonathan G Richens, Ciarán M Lee, and Saurabh Johri · 2020
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Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, et al · 2020
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Towards a principled bayesian workflow, 2020
Michael Betancourt · 2020
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Skewed link regression models for imbalanced binary response with applications to life insurance
Shuang Yin, Dipak K. Dey, Emiliano A. Valdez, Guojun Gan, and Jeyaraj Vadiveloo · 2020
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An introduction to proximal causal learning
Eric J Tchetgen Tchetgen, Andrew Ying, Yifan Cui, Xu Shi, and Wang Miao · 2020
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Prediction or causality? a scoping review of their conflation within current observational research
Chava L Ramspek, Ewout W Steyerberg, Richard D Riley, Frits R Rosendaal, Olaf M Dekkers, Friedo W Dekker, and Merel van Diepen · 2021
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Physics-informed machine learning
George Em Karniadakis, Ioannis G Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang · 2021
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Bayesian Item Response Modelling in R with brms and Stan
Paul-Christian Bürkner · 2021
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Prior knowledge elicitation: The past, present, and future
Petrus Mikkola, Osvaldo A Martin, Suyog Chandramouli, Marcelo Hartmann, Oriol Abril Pla, Owen Thomas, Henri Pesonen, Jukka Corander, Aki Vehtari, Samuel Kaski, Bürkner, Paul-Christian, and Klami, Arto · 2021
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Cross-validation: what does it estimate and how well does it do it?
Stephen Bates, Trevor Hastie, and Robert Tibshirani · 2021
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bayesfam: Custom families for brms, 2022
Maximilian Scholz, Yannick Dzubba, and Paul-Christian Bürkner · 2022
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Graphical criteria for efficient total effect estimation via adjustment in causal linear models
Leonard Henckel, Emilija Perković, and Marloes H Maathuis · 2022
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Incorporating causal effects into deep learning predictions on ehr data
Jia Li, Xiaowei Jia, Haoyu Yang, Vipin Kumar, Michael Steinbach, and Gyorgy Simon · 2022
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Causality for Machine Learning , page 765–804
Bernhard Schölkopf · 2022
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Stan modeling language users guide and reference manual, 2.30.0, 2022
Stan Development Team · 2022
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loo: Efficient leave-one-out cross-validation and waic for bayesian models, 2022
Aki Vehtari, Jonah Gabry, Mans Magnusson, Yuling Yao, Paul-Christian Bürkner, Topi Paananen, and Andrew Gelman · 2022
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Posterior accuracy and calibration under misspecification in bayesian generalized linear models
Maximilian Scholz and Paul-Christian Bürkner · 2023
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