#118 Exploring the Future of Stan, with Charles Margossian & Brian Ward

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meOur 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 ;)Takeaways:User experience is crucial for the adoption of Stan.Recent innovations include adding tuples to the Stan language, new features and improved error messages.Tuples allow for more efficient data handling in Stan.Beginners often struggle with the compiled nature of Stan.Improving error messages is crucial for user experience.BridgeStan allows for integration with other programming languages and makes it very easy for people to use Stan models.Community engagement is vital for the development of Stan.New samplers are being developed to enhance performance.The future of Stan includes more user-friendly features.Chapters:00:00 Introduction to the Live Episode02:55 Meet the Stan Core Developers05:47 Brian Ward's Journey into Bayesian Statistics09:10 Charles Margossian's Contributions to Stan11:49 Recent Projects and Innovations in Stan15:07 User-Friendly Features and Enhancements18:11 Understanding Tuples and Their Importance21:06 Challenges for Beginners in Stan24:08 Pedagogical Approaches to Bayesian Statistics30:54 Optimizing Monte Carlo Estimators32:24 Reimagining Stan's Structure34:21 The Promise of Automatic Reparameterization35:49 Exploring BridgeStan40:29 The Future of Samplers in Stan43:45 Evaluating New Algorithms47:01 Specific Algorithms for Unique Problems50:00 Understanding Model Performance54:21 The Impact of Stan on Bayesian ResearchThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin...

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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, and I live in Estonia. By day, I'm a data scientist and modeler at the PyMC Labs consultancy. 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 election forecasting and, most importantly, 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!