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Bayes Dose

@bayesdose

A daily dose of Bayes from around the Interwebs and before.

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linkhttps://www.generable.com/blog/ calendar_today21-08-2018 20:05:12

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Model-based Inference for Causal Effects in Completely Randomized Experiments by Joon-Ho Lee et al. mc-stan.org/users/document…

Model-based Inference for Causal Effects in Completely Randomized Experiments by Joon-Ho Lee et al. 

mc-stan.org/users/document…
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Probability does not promote play (games of chance): from Abraham de Moivre to Lord Carpenter in the preface to the Doctrine of Changes.

Probability does not promote play (games of chance): from Abraham de Moivre to Lord Carpenter in the preface to the Doctrine of Changes.
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(Bayesian) Survival Models & Biostatistics for Cancer Research, withJacqueline Buros (jackiburos) via Alexandre Andorra and his Learn Bayes Stats podcast learnbayesstats.com/episode/39-sur…

(Bayesian) Survival Models &amp; Biostatistics for Cancer Research, withJacqueline Buros (<a href="/jackiburos/">jackiburos</a>) via <a href="/alex_andorra/">Alexandre Andorra</a> and his Learn Bayes Stats podcast

learnbayesstats.com/episode/39-sur…
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The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions by Raj Agrawal et al. arxiv.org/abs/1905.06501 github.com/pyro-ppl/pyro/… youtube.com/watch?v=Xnxnoq…

The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions by Raj Agrawal et al. arxiv.org/abs/1905.06501

github.com/pyro-ppl/pyro/…

youtube.com/watch?v=Xnxnoq…
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"Pathfinder: Parallel quasi-Newton variational inference" by Lu Zhang, Bob Carpenter, Andrew Gelman, and Aki Vehtari arxiv.org/abs/2108.03782

"Pathfinder: Parallel quasi-Newton variational inference" by  Lu Zhang, Bob Carpenter, Andrew Gelman, and Aki Vehtari arxiv.org/abs/2108.03782
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Stan developer Rok Češnovar is stopping by at 12 PM ET to talk to us about the current state of Stan how we got here, and where we are going. Join us on the Calibrated Webcast. hopin.com/events/the-cur…

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Stan developer Rok Češnovar talks about the current state of Stan how we got here, and where we are going. youtu.be/EJLXoDBoVko

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Regression and Other Stories by Andrew Gelman, Jennifer Hill, and Aki Vehtari is now available online. avehtari.github.io/ROS-Examples/ users.aalto.fi/~ave/ROS.pdf

Regression and Other Stories by Andrew Gelman, Jennifer Hill, and Aki Vehtari is now available online.  

avehtari.github.io/ROS-Examples/
users.aalto.fi/~ave/ROS.pdf
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Tuning-Free Generalized Hamiltonian Monte Carlo by Matt Hoffman and Pavel Sountsov. proceedings.mlr.press/v151/hoffman22…

Tuning-Free Generalized Hamiltonian Monte Carlo by Matt Hoffman and Pavel Sountsov. 

proceedings.mlr.press/v151/hoffman22…
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Bayes Rules! An Introduction to Applied Bayesian Modeling by Alicia A. Johnson, Miles Q. Ott, Mine Dogucu is available online: bayesrulesbook.com

Bayes Rules! An Introduction to Applied Bayesian Modeling by Alicia A. Johnson, Miles Q. Ott, Mine Dogucu is available online: bayesrulesbook.com
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"Past, Present, and Future of Software for Bayesian Inference" is a paper by Erik Štrumbelj and colleagues. It's a nice overview and contains data on the number of downloads of Bayes-related packages in Python and R. stat.columbia.edu/~gelman/resear…

"Past, Present, and Future of Software for Bayesian Inference" is a paper by Erik Štrumbelj and colleagues. It's a nice overview and contains data on the number of downloads of Bayes-related packages in Python and R.

stat.columbia.edu/~gelman/resear…
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New Book: Bayesian Statistical Modeling with Stan, R, and Python by Kentaro Matsuura. Here is a review: statmodeling.stat.columbia.edu/2023/11/27/boo…

New Book: Bayesian Statistical Modeling with Stan, R, and Python by Kentaro Matsuura. Here is a review: statmodeling.stat.columbia.edu/2023/11/27/boo…
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causalBETA: An R Package for Bayesian Semiparametric Causal Inference with Event-Time Outcomes by Han Ji and Arman Oganisian arxiv.org/abs/2310.12358 github.com/RuBBiT-hj/caus…

causalBETA: An R Package for Bayesian Semiparametric Causal Inference with Event-Time Outcomes by Han Ji and Arman Oganisian
arxiv.org/abs/2310.12358
github.com/RuBBiT-hj/caus…