Today’s clip is from <a href="https://learnbayesstats.com/episode/133-making-models-more-efficient-flexible-sean-pinkney-adrian-seyboldt" rel="noopener noreferrer" target="_blank">episode 133</a> of the podcast, with Sean Pinkney & Adrian Seyboldt.The conversation delves into the concept of Zero-Sum Normal and its application in statistical modeling, particularly in hierarchical models. Alex, Sean and Adrian discuss the implications of using zero-sum constraints, the challenges of incorporating new data points, and the importance of distinguishing between sample and population effects. They also explore practical solutions for making predictions based on population parameters and the potential for developing tools to facilitate these processes.Get the full discussion <a href="https://learnbayesstats.com/episode/133-making-models-more-efficient-flexible-sean-pinkney-adrian-seyboldt" rel="noopener noreferrer" target="_blank">here</a>.<ul><li><a href="https://topmate.io/alex_andorra/503302" rel="noopener noreferrer" target="_blank">Intro to Bayes Course</a> (first 2 lessons free)</li><li><a href="https://topmate.io/alex_andorra/1011122" rel="noopener noreferrer" target="_blank">Advanced Regression Course</a> (first 2 lessons free)</li></ul> Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his <a href="https://bababrinkman.com/" rel="noopener noreferrer" target="_blank">awesome work</a>!Visit our <a href="https://www.patreon.com/learnbayesstats" rel="noopener noreferrer" target="_blank">Patreon page</a> to unlock exclusive Bayesian swag ;)TranscriptThis is an automatic transcript and may therefore contain errors. Please <a href="http://twitter.com/LearnBayesStats" rel="noopener noreferrer" target="_blank">get in touch</a> if you're willing to correct them.

Today’s clip is from episode 133 of the podcast, with Sean Pinkney & Adrian Seyboldt. The conversation delves into the concept of Zero-Sum Normal and its application in statistical modeling, particularly in hierarchical models. Alex, Sean and Adrian discuss the implications of using zero-sum constraints, the challenges of incorporating new data points, and the importance of distinguishing between sample and population effects. They also explore practical solutions for making predictions based on population parameters and the potential for developing tools to facilitate these processes. Get the full discussion here.Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free) Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work! Visit our Patreon page to unlock exclusive Bayesian swag ;) TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Today’s clip is from <a href="https://learnbayesstats.com/episode/133-making-models-more-efficient-flexible-sean-pinkney-adrian-seyboldt" rel="noopener noreferrer">episode 133</a> of the podcast, with Sean Pinkney &amp; Adrian Seyboldt.The conversation delves into the concept of Zero-Sum Normal and its application in statistical modeling, particularly in hierarchical models. Alex, Sean and Adrian discuss the implications of using zero-sum constraints, the challenges of incorporating new data points, and the importance of distinguishing between sample and population effects. They also explore practical solutions for making predictions based on population parameters and the potential for developing tools to facilitate these processes.Get the full discussion <a href="https://learnbayesstats.com/episode/133-making-models-more-efficient-flexible-sean-pinkney-adrian-seyboldt" rel="noopener noreferrer">here</a>.<ul><li><a href="https://topmate.io/alex_andorra/503302" rel="noopener noreferrer">Intro to Bayes Course</a> (first 2 lessons free)</li><li><a href="https://topmate.io/alex_andorra/1011122" rel="noopener noreferrer">Advanced Regression Course</a> (first 2 lessons free)</li></ul> Our&nbsp;theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and&nbsp;Mega Ran). Check out his <a href="https://bababrinkman.com/" rel="noopener noreferrer">awesome work</a>!Visit our <a href="https://www.patreon.com/learnbayesstats" rel="noopener noreferrer">Patreon page</a> to unlock exclusive Bayesian swag ;)TranscriptThis is an automatic transcript and may therefore contain errors. Please <a href="http://twitter.com/LearnBayesStats" rel="noopener noreferrer">get in touch</a> if you're willing to correct them.

BITESIZE | Why Your Models Might Be Wrong & How to Fix it, with Sean Pinkney & Adrian Seyboldt

Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is <a target="_blank" rel="noopener noreferrer nofollow" href="https://alexandorra.github.io/">Alex Andorra</a> by the way. By day, I'm a Senior data scientist. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages <a target="_blank" rel="noopener noreferrer nofollow" href="https://docs.pymc.io/">PyMC</a> and <a target="_blank" rel="noopener noreferrer nofollow" href="https://arviz-devs.github.io/arviz/">ArviZ</a>. I also love Nutella, 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! You can also support the show and <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.patreon.com/learnbayesstats">unlock exclusive Bayesian swag on Patreon</a>!

Science

Technology

Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is Alex Andorra by the way. By day, I'm a Senior data scientist. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. I also love Nutella, 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! You can also support the show and unlock exclusive Bayesian swag on Patreon!

Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is <a href="https://alexandorra.github.io/" rel="noopener noreferrer">Alex Andorra</a> by the way. By day, I'm a Senior data scientist. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages <a href="https://docs.pymc.io/" rel="noopener noreferrer">PyMC</a> and <a href="https://arviz-devs.github.io/arviz/" rel="noopener noreferrer">ArviZ</a>. I also love Nutella, 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! You can also support the show and <a href="https://www.patreon.com/learnbayesstats" rel="noopener noreferrer">unlock exclusive Bayesian swag on Patreon</a>!

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BITESIZE | Why Your Models Might Be Wrong & How to Fix it, with Sean Pinkney & Adrian Seyboldt

BITESIZE | Why Your Models Might Be Wrong & How to Fix it, with Sean Pinkney & Adrian Seyboldt

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