install probabilistic foundations of statistical network ysis chapman hall crc monographs on statistics applied probability suitably simple! Aki is an associate professor in computational probabilistic modeling at Aalto University, Finland.In this episode, they tell us why they wrote this book, who it is for and they also give us their 10 tips to improve your regression modeling! Well, be prepared to be amazed! Check out his awesome work at https://bababrinkman.com/ !Links from the show:Regression and Other Stories on Cambridge Press website: http://www.cambridge.org/wm-ecommerce-web/academic/landingPage/GoodBayesian2020Amazon page (because of VAT laws, in some regions ordering from Amazon can be cheaper than from the editor directly, even with the discount): https://www.amazon.com/Regression-Stories-Analytical-Methods-Research/dp/110702398XCode, data and examples for the book: https://avehtari.github.io/ROS-Examples/Port of the book in Python and Bambi: https://github.com/bambinos/Bambi_resources/tree/master/ROSAndrew's home page: http://www.stat.columbia.edu/~gelman/Andrew's blog: https://statmodeling.stat.columbia.edu/Andrew on Twitter: https://twitter.com/statmodelingJennifer's home page: https://steinhardt.nyu.edu/people/jennifer-hillAki's teaching material: https://avehtari.github.io/Aki's home page: https://users.aalto.fi/~ave/Aki on Twitter: https://twitter.com/avehtariThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy, #19 Turing, Julia and Bayes in Economics, with Cameron Pfiffer, Do you know Turing? We also talked about the limits of regression and about going to Mars…Other good news: until October 31st 2020, you can go to http://www.cambridge.org/wm-ecommerce-web/academic/landingPage/GoodBayesian2020 and buy the book with a 20% discount by entering the promo code “GoodBayesian2020” upon checkout!That way, you’ll make up your own stories before going to sleep and dream of a world where we can easily generalize from sample to population, and where multilevel regression with poststratification is a bliss…Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Indeed, Avi lives on Galiano Island, Canada, not very far from Vancouver, surrounded by forest, overlooking the Salish Sea. His interests revolve around the means and methods of mathematical modeling and its automation. Here to narrate these marvelous statistical adventures are Andrew Gelman, Jennifer Hill and Aki Vehtari — the authors of the brand new Regression and other Stories.Andrew is a professor of statistics and political science at Columbia University. But what are they exactly? If you are interesting in becoming better at statistics and machine learning, then some time should be invested in diving deeper into Bayesian Statistics. Are you interested in learning more about how to become a data scientist? She even wrote the book R for Marketing Research and Analytics with Chris Chapman, at Springer Press.In summary, I think you’ll be pretty surprised by how Bayesian the world of marketing is…Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). While working at Stripe, Avi wrote Rainier, a Bayesian inference framework for Scala. #30 Symbolic Computation & Dynamic Linear Models, with Brandon Willard. The guests will not come directly from the Bayesian world, but will still be related to science or programming.For the first episode of the kind, I had the chance to chat with Michael Kennedy! When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. He’s the founder and chief author at Talk Python Training, where he develops many Python developer online courses. I like this podcast because is helpful for me to my statistical master degree. As Avi says, depending on your background, you might think of Rainier as aspiring to be either "Stan, but on the JVM", or "TensorFlow, but for small data".In this episode, Avi will tell us how Rainier came into life, how it fits into the probabilistic programming landscape, and what its main strengths and weaknesses are.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Elea is an Assistant Professor of Marketing at Drexel, and in this episode she’ll tell us which methods are the most useful in marketing analytics, and why.Indeed, Elea develops data analysis methods to inform marketing decisions, such as designing new products and planning advertising campaigns. Check out his awesome work at https://bababrinkman.com/ ! Check out his awesome work at https://bababrinkman.com/ !Links from the show:Bayesian Econometrics on Cameron's Blog: http://cameron.pfiffer.org/2020/03/24/bayesian-econometrics/Cameron on Twitter: https://twitter.com/cameron_pfifferCameron on GitHub: https://github.com/cpfifferTuring.jl -- Bayesian inference in Julia: https://turing.ml/dev/Gen.jl -- Programmable inference embedded in Julia: https://www.gen.dev/Soss.jl -- Probabilistic programming via source rewriting: https://github.com/cscherrer/Soss.jlThe Julia Language -- A fresh approach to technical computing: https://julialang.org/What is Probabilistic Programming -- Cornell University: http://adriansampson.net/doc/ppl.htmlMostly Harmless Econometrics Book: http://www.mostlyharmlesseconometrics.com/Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy, I hope you’re all safe! How do they even change careers? Ow, and you’ll also learn an interesting link between BNNs and Gaussian Processes…I know: Liza works on _a lot_ of projects! Latest was 127 – All About Aging with Richard Acton. You also get early access to the special episodes. As you’ll hear, our conversation spanned a large array of topics — the role of Python in science and research; how it came to be so important in data science, and why; what are Python’s threats and weaknesses and how it should evolve to not become obsolete. As you’ll hear, our conversation spanned a large array of topics — the role of Python in science and research; how it came to be so important in data science, and why; what are Python’s threats and weaknesses and how it should evolve to not become obsolete. #31 Bayesian Cognitive Modeling & Decision-Making, with Michael Lee, I don’t know if you noticed, but I have a fondness for any topic related to decision-making under uncertainty — when it’s studied scientifically of course. But I don't like talking about it – I prefer eating it.So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! Well, stay tuned, he’ll tell us more in the episode…Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). LBS Patreon page: patreon.com/learnbayesstats Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Shosh's website: https://shoshanavasserman.com/Shosh on Twitter: https://twitter.com/shoshievassHow do different reopening strategies balance health and employment: https://reopenmappingproject.com/Aggregate random coefficients logit—a generative approach: http://modernstatisticalworkflow.blogspot.com/2017/03/aggregate-random-coefficients-logita.htmlVoluntary Disclosure and Personalized Pricing: https://shoshanavasserman.com/files/2020/08/Voluntary-Disclosure-and-Personalized-Pricing.pdfSocioeconomic Network Heterogeneity and Pandemic Policy Response: https://shoshanavasserman.com/files/2020/06/Network-Heterogeneity-Pandemic-Policy.pdfBuying Data from Consumers -- The Impact of Monitoring Programs in U.S. Auto Insurance: https://shoshanavasserman.com/files/2020/05/jinvass_0420.pdfThis podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy, #27 Modeling the US Presidential Elections, with Andrew Gelman & Merlin Heidemanns, In a few days, a consequential election will take place, as citizens of the United States will go to the polls and elect their president — in fact they already started voting. Moreover, how can you align funding and publishing incentives with the principles of an open source science?Let’s do another “big picture” episode to try and answer these questions! Check out his awesome work at https://bababrinkman.com/ (https://bababrinkman.com/) !Links from the show:Seth website: http://sethaxen.com/ (http://sethaxen.com/)Seth on Twitter: https://twitter.com/sethaxen (https://twitter.com/sethaxen)Seth on GitHub: https://github.com/sethaxen (https://github.com/sethaxen)ArviZ.jl -- Exploratory analysis of Bayesian models in Julia: https://arviz-devs.github.io/ArviZ.jl/dev/ (https://arviz-devs.github.io/ArviZ.jl/dev/)PyCon2020 -- Colin Carroll -- Getting started with automatic differentiation: https://www.youtube.com/watch?v=NG21KWZSiok (https://www.youtube.com/watch?v=NG21KWZSiok)Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy Support this podcast, If you’ve studied at a business school, you probably didn’t attend any Bayesian stats course there. In this one, you’ll meet Daniel Lakens. Check out his awesome work at https://bababrinkman.com/ !Links from the show:Liza on Twitter: https://twitter.com/liza_p_semenovaLiza on GitHub: https://github.com/elizavetasemenovaLiza's blog: https://elizavetasemenova.github.io/blog/A Bayesian neural network for toxicity prediction: https://www.biorxiv.org/content/10.1101/2020.04.28.065532v2Bayesian Neural Networks for toxicity prediction -- Video presentation: https://www.youtube.com/watch?v=BCQ2oVlu_tY&t=751sBayesian workflow for disease transmission modeling in Stan: https://mc-stan.org/users/documentation/case-studies/boarding_school_case_study.htmlAndrew Gelman's comments on the SIR case-study: https://statmodeling.stat.columbia.edu/2020/06/02/this-ones-important-bayesian-workflow-for-disease-transmission-modeling-in-stan/Determining organ weight toxicity with Bayesian causal models: https://www.biorxiv.org/content/10.1101/754853v1Material for Applied Machine Learning Days ("Embracing uncertainty"): https://github.com/elizavetasemenova/EmbracingUncertaintyPredicting Drug-Induced Liver Injury with Bayesian Machine Learning: https://pubs.acs.org/doi/abs/10.1021/acs.chemrestox.9b00264Ordered Logistic Regression in Stan, PyMC3 and Turing: https://medium.com/@liza_p_semenova/ordered-logistic-regression-and-probabilistic-programming-502d8235ad3fPyMCon website: https://pymc-devs.github.io/pymcon/PyMCon Call For Proposal: https://pymc-devs.github.io/pymcon/cfpPyMCon Sponsorship Form: https://docs.google.com/forms/d/e/1FAIpQLSdRDI1z0U0ZztONOFiZt2VdsBIZtAWB4JAUA415Iw8RYqNbXQ/viewformPyMCon Volunteer Form: https://docs.google.com/forms/d/e/1FAIpQLScCLW5RkNtBz1u376xwelSsNpyWImFisSMjZGP35fYi2QHHXw/viewformThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran and Paul Oreto.This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy, #20 Regression and Other Stories, with Andrew Gelman, Jennifer Hill & Aki Vehtari, Once upon a time, there was an enchanted book filled with hundreds of little plots, applied examples and linear regressions — the prettiest creature that was ever seen. At the same level as Bishop’s book, you can also find a rigorous and detailed explanation of Bayesian statistics and modeling on David MacKay’s Information Theory, Inference, and Learning Algorithms. representing small molecules for machine learning. He is also a co-author of the popular and awarded book « Bayesian Data Analysis », Third Edition, and a core-developer of the seminal probabilistic programming framework Stan. How do you even know that you’re asking a good research question? Note: This is an excerpt from my new book-in-progress called “Uncertainty”. Are you a researcher or data scientist / analyst / ninja? We’re gonna talk about the Julia probabilistic programming landscape, Bayes in economics and causality — it’s gonna be fun ;) Again, patreon.com/learnbayesstats if you want to support the show and unlock some nice perks. Inference is based on variants of the Hamiltonian Monte Carlo sampler, and the implementation is similar to, and targets the same types of models as both Stan and PyMC3. He is also a co-author of the popular and awarded book « Bayesian Data Analysis », Third Edition, and a core-developer of the seminal probabilistic programming framework Stan. It starts as low as 3€ and you can pick from 4 different tiers: "Maximum A Posteriori" (3€): Join the Slack, where you can ask questions about the show, discuss with like-minded Bayesians and meet them in-person when you travel the world. More specifically, Maria is interested in probabilistic programming languages, and in exploring ways of applying program-analysis techniques to existing PPLs in order to improve usability of the language or efficiency of inference.As you’ll hear in the episode, she thinks a lot about the language aspect of probabilistic programming, and works on the automation of various “tricks” in probabilistic programming: automatic re-parametrization, automatic marginalization, automatic and efficient model-specific inference.As Maria also has experience with several PPLs like Stan, Edward2 and TensorFlow Probability, she’ll tell us what she thinks a good PPL design requires, and what the future of PPLs looks like to her.Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). The caret package provides an easy way to do machine learning in R. It provides a wrapper over many other machine learning R libraries and has utility functions for running cross-validation and cleaning up data. But once you defeat these monsters, you’ll be able to think about, build and interpret regression models.This episode will be filled with stories — stories about linear regressions! Does that intrigue you? The guests will not come directly from the Bayesian world, but will still be related to science or programming.For the first episode of the kind, I had the chance to chat with Michael Kennedy! It boils down to a false perspective of reality and lack of understanding of natural law within reason. Shosh is an assistant professor of economics at the Stanford Graduate School of Business. What does the probabilistic programming landscape in Julia look like? Jaynes: https://www.cambridge.org/core/books/probability-theory/9CA08E224FF30123304E6D8935CF1A99 (https://www.cambridge.org/core/books/probability-theory/9CA08E224FF30123304E6D8935CF1A99)Wittgenstein's Lectures on the Foundations of Mathematics: https://www.amazon.com/Wittgensteins-Lectures-Foundations-Mathematics-Cambridge/dp/0226904261 (https://www.amazon.com/Wittgensteins-Lectures-Foundations-Mathematics-Cambridge/dp/0226904261)This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy Support this podcast, This is it folks!

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