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Learning Bayesian Statistics

Podcast Learning Bayesian Statistics
Alexandre Andorra
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 in...

Episodios disponibles

5 de 136
  • #121 Exploring Bayesian Structural Equation Modeling, with Nathaniel Forde
    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:CFA is commonly used in psychometrics to validate theoretical constructs.Theoretical structure is crucial in confirmatory factor analysis.Bayesian approaches offer flexibility in modeling complex relationships.Model validation involves both global and local fit measures.Sensitivity analysis is vital in Bayesian modeling to avoid skewed results.Complex models should be justified by their ability to answer specific questions.The choice of model complexity should balance fit and theoretical relevance. Fitting models to real data builds confidence in their validity.Divergences in model fitting indicate potential issues with model specification.Factor analysis can help clarify causal relationships between variables.Survey data is a valuable resource for understanding complex phenomena.Philosophical training enhances logical reasoning in data science.Causal inference is increasingly recognized in industry applications.Effective communication is essential for data scientists.Understanding confounding is crucial for accurate modeling.Chapters:10:11 Understanding Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA)20:11 Application of SEM and CFA in HR Analytics30:10 Challenges and Advantages of Bayesian Approaches in SEM and CFA33:58 Evaluating Bayesian Models39:50 Challenges in Model Building44:15 Causal Relationships in SEM and CFA49:01 Practical Applications of SEM and CFA51:47 Influence of Philosophy on Data Science54:51 Designing Models with Confounding in Mind57:39 Future Trends in Causal Inference01:00:03 Advice for Aspiring Data Scientists01:02:48 Future Research DirectionsThank 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,
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  • #120 Innovations in Infectious Disease Modeling, with Liza Semenova & Chris Wymant
    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 ;)-------------------------Love the insights from this episode? Make sure you never miss a beat with Chatpods! Whether you're commuting, working out, or just on the go, Chatpods lets you capture and summarize key takeaways effortlessly.Save time, stay organized, and keep your thoughts at your fingertips.Download Chatpods directly from App Store or Google Play and use it to listen to this podcast today!https://www.chatpods.com/?fr=LearningBayesianStatistics-------------------------Takeaways:Epidemiology focuses on health at various scales, while biology often looks at micro-level details.Bayesian statistics helps connect models to data and quantify uncertainty.Recent advancements in data collection have improved the quality of epidemiological research.Collaboration between domain experts and statisticians is essential for effective research.The COVID-19 pandemic has led to increased data availability and international cooperation.Modeling infectious diseases requires understanding complex dynamics and statistical methods.Challenges in coding and communication between disciplines can hinder progress.Innovations in machine learning and neural networks are shaping the future of epidemiology.The importance of understanding the context and limitations of data in research. Chapters:00:00 Introduction to Bayesian Statistics and Epidemiology03:35 Guest Backgrounds and Their Journey10:04 Understanding Computational Biology vs. Epidemiology16:11 The Role of Bayesian Statistics in Epidemiology21:40 Recent Projects and Applications in Epidemiology31:30...
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  • #119 Causal Inference, Fiction Writing and Career Changes, with Robert Kubinec
    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:Bob's research focuses on corruption and political economy.Measuring corruption is challenging due to the unobservable nature of the behavior.The challenge of studying corruption lies in obtaining honest data.Innovative survey techniques, like randomized response, can help gather sensitive data.Non-traditional backgrounds can enhance statistical research perspectives.Bayesian methods are particularly useful for estimating latent variables.Bayesian methods shine in situations with prior information.Expert surveys can help estimate uncertain outcomes effectively.Bob's novel, 'The Bayesian Hitman,' explores academia through a fictional lens.Writing fiction can enhance academic writing skills and creativity.The importance of community in statistics is emphasized, especially in the Stan community.Real-time online surveys could revolutionize data collection in social science.Chapters:00:00 Introduction to Bayesian Statistics and Bob Kubinec06:01 Bob's Academic Journey and Research Focus12:40 Measuring Corruption: Challenges and Methods18:54 Transition from Government to Academia26:41 The Influence of Non-Traditional Backgrounds in Statistics34:51 Bayesian Methods in Political Science Research42:08 Bayesian Methods in COVID Measurement51:12 The Journey of Writing a Novel01:00:24 The Intersection of Fiction and AcademiaThank 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 Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell,...
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  • #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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  • #117 Unveiling the Power of Bayesian Experimental Design, with Desi Ivanova
    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:Designing experiments is about optimal data gathering.The optimal design maximizes the amount of information.The best experiment reduces uncertainty the most.Computational challenges limit the feasibility of BED in practice.Amortized Bayesian inference can speed up computations.A good underlying model is crucial for effective BED.Adaptive experiments are more complex than static ones.The future of BED is promising with advancements in AI.Chapters:00:00 Introduction to Bayesian Experimental Design07:51 Understanding Bayesian Experimental Design19:58 Computational Challenges in Bayesian Experimental Design28:47 Innovations in Bayesian Experimental Design40:43 Practical Applications of Bayesian Experimental Design52:12 Future of Bayesian Experimental Design01:01:17 Real-World Applications and ImpactThank 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 Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov,...
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Acerca de Learning Bayesian Statistics

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 https://www.pymc-labs.io/ (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 https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/ (ArviZ). I also love https://www.pollsposition.com/ (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 https://www.patreon.com/learnbayesstats (unlock exclusive Bayesian swag on Patreon)!
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