Learning Bayesian Statistics

Alexandre Andorra
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 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!

  1. Causal Inference, Fiction Writing and Career Changes, with Robert Kubinec

    2 DAYS AGO

    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 me 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 ;) 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 Kubinec 06:01 Bob's Academic Journey and Research Focus 12:40 Measuring Corruption: Challenges and Methods 18:54 Transition from Government to Academia 26:41 The Influence of Non-Traditional Backgrounds in Statistics 34:51 Bayesian Methods in Political Science Research 42:08 Bayesian Methods in COVID Measurement 51:12 The Journey of Writing a Novel 01:00:24 The Intersection of Fiction and Academia Thank 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,...

    1h 25m
  2. Exploring the Future of Stan, with Charles Margossian & Brian Ward

    OCT 30

    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 me 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 ;) 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 Episode 02:55 Meet the Stan Core Developers 05:47 Brian Ward's Journey into Bayesian Statistics 09:10 Charles Margossian's Contributions to Stan 11:49 Recent Projects and Innovations in Stan 15:07 User-Friendly Features and Enhancements 18:11 Understanding Tuples and Their Importance 21:06 Challenges for Beginners in Stan 24:08 Pedagogical Approaches to Bayesian Statistics 30:54 Optimizing Monte Carlo Estimators 32:24 Reimagining Stan's Structure 34:21 The Promise of Automatic Reparameterization 35:49 Exploring BridgeStan 40:29 The Future of Samplers in Stan 43:45 Evaluating New Algorithms 47:01 Specific Algorithms for Unique Problems 50:00 Understanding Model Performance 54:21 The Impact of Stan on Bayesian Research Thank 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...

    59 min
  3. Unveiling the Power of Bayesian Experimental Design, with Desi Ivanova

    OCT 15

    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 me 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 ;) 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 Design 07:51 Understanding Bayesian Experimental Design 19:58 Computational Challenges in Bayesian Experimental Design 28:47 Innovations in Bayesian Experimental Design 40:43 Practical Applications of Bayesian Experimental Design 52:12 Future of Bayesian Experimental Design 01:01:17 Real-World Applications and Impact Thank 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,...

    1h 13m
  4. Mastering Soccer Analytics, with Ravi Ramineni

    OCT 2

    Mastering Soccer Analytics, with Ravi Ramineni

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! My Intuitive Bayes Online Courses1:1 Mentorship with me 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 ;) Takeaways: Building an athlete management system and a scouting and recruitment platform are key goals in football analytics.The focus is on informing training decisions, preventing injuries, and making smart player signings.Avoiding false positives in player evaluations is crucial, and data analysis plays a significant role in making informed decisions.There are similarities between different football teams, and the sport has social and emotional aspects. Transitioning from on-premises SQL servers to cloud-based systems is a significant endeavor in football analytics.Analytics is a tool that aids the decision-making process and helps mitigate biases. The impact of analytics in soccer can be seen in the decline of long-range shots.Collaboration and trust between analysts and decision-makers are crucial for successful implementation of analytics.The limitations of available data in football analytics hinder the ability to directly measure decision-making on the field. Analyzing the impact of coaches in sports analytics is challenging due to the difficulty of separating their effect from other factors. Current data limitations make it hard to evaluate coaching performance accurately.Predictive metrics and modeling play a crucial role in soccer analytics, especially in predicting the career progression of young players.Improving tracking data and expanding its availability will be a significant focus in the future of soccer analytics. Chapters: 00:00 Introduction to Ravi and His Role at Seattle Sounders  06:30 Building an Analytics Department 15:00 The Impact of Analytics on Player Recruitment and Performance  28:00 Challenges and Innovations in Soccer Analytics  42:00 Player Health, Injury Prevention, and Training  55:00 The Evolution of Data-Driven Strategies 01:10:00 Future of Analytics in Sports Thank 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,

    1h 33m
  5. Using Time Series to Estimate Uncertainty, with Nate Haines

    SEP 17

    Using Time Series to Estimate Uncertainty, with Nate Haines

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! My Intuitive Bayes Online Courses1:1 Mentorship with me 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 ;) Takeaways: State space models and traditional time series models are well-suited to forecast loss ratios in the insurance industry, although actuaries have been slow to adopt modern statistical methods.Working with limited data is a challenge, but informed priors and hierarchical models can help improve the modeling process.Bayesian model stacking allows for blending together different model predictions and taking the best of both (or all if more than 2 models) worlds.Model comparison is done using out-of-sample performance metrics, such as the expected log point-wise predictive density (ELPD). Brute leave-future-out cross-validation is often used due to the time-series nature of the data.Stacking or averaging models are trained on out-of-sample performance metrics to determine the weights for blending the predictions. Model stacking can be a powerful approach for combining predictions from candidate models. Hierarchical stacking in particular is useful when weights are assumed to vary according to covariates.BayesBlend is a Python package developed by Ledger Investing that simplifies the implementation of stacking models, including pseudo Bayesian model averaging, stacking, and hierarchical stacking.Evaluating the performance of patient time series models requires considering multiple metrics, including log likelihood-based metrics like ELPD, as well as more absolute metrics like RMSE and mean absolute error.Using robust variants of metrics like ELPD can help address issues with extreme outliers. For example, t-distribution estimators of ELPD as opposed to sample sum/mean estimators.It is important to evaluate model performance from different perspectives and consider the trade-offs between different metrics. Evaluating models based solely on traditional metrics can limit understanding and trust in the model. Consider additional factors such as interpretability, maintainability, and productionization.Simulation-based calibration (SBC) is a valuable tool for assessing parameter estimation and model correctness. It allows for the interpretation of model parameters and the identification of coding errors.In industries like insurance, where regulations may restrict model choices, classical statistical approaches still play a significant role. However, there is potential for Bayesian methods and generative AI in certain areas. p...

    1h 40m
  6. From the Field to the Lab – A Journey in Baseball Science, with Jacob Buffa

    SEP 5

    From the Field to the Lab – A Journey in Baseball Science, with Jacob Buffa

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! My Intuitive Bayes Online Courses1:1 Mentorship with me 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 ;) Takeaways: Education and visual communication are key in helping athletes understand the impact of nutrition on performance.Bayesian statistics are used to analyze player performance and injury risk.Integrating diverse data sources is a challenge but can provide valuable insights.Understanding the specific needs and characteristics of athletes is crucial in conditioning and injury prevention. The application of Bayesian statistics in baseball science requires experts in Bayesian methods.Traditional statistical methods taught in sports science programs are limited.Communicating complex statistical concepts, such as Bayesian analysis, to coaches and players is crucial.Conveying uncertainties and limitations of the models is essential for effective utilization.Emerging trends in baseball science include the use of biomechanical information and computer vision algorithms.Improving player performance and injury prevention are key goals for the future of baseball science. Chapters: 00:00 The Role of Nutrition and Conditioning 05:46 Analyzing Player Performance and Managing Injury Risks 12:13 Educating Athletes on Dietary Choices 18:02 Emerging Trends in Baseball Science 29:49 Hierarchical Models and Player Analysis 36:03 Challenges of Working with Limited Data 39:49 Effective Communication of Statistical Concepts 47:59 Future Trends: Biomechanical Data Analysis and Computer Vision Algorithms Thank 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,...

    1h 2m
  7. A Deep Dive into Bayesian Stats, with Alex Andorra, ft. the Super Data Science Podcast

    AUG 22

    A Deep Dive into Bayesian Stats, with Alex Andorra, ft. the Super Data Science Podcast

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! My Intuitive Bayes Online Courses1:1 Mentorship with me 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 ;) Takeaways: Bayesian statistics is a powerful framework for handling complex problems, making use of prior knowledge, and excelling with limited data.Bayesian statistics provides a framework for updating beliefs and making predictions based on prior knowledge and observed data.Bayesian methods allow for the explicit incorporation of prior assumptions, which can provide structure and improve the reliability of the analysis.There are several Bayesian frameworks available, such as PyMC, Stan, and Bambi, each with its own strengths and features.PyMC is a powerful library for Bayesian modeling that allows for flexible and efficient computation.For beginners, it is recommended to start with introductory courses or resources that provide a step-by-step approach to learning Bayesian statistics.PyTensor leverages GPU acceleration and complex graph optimizations to improve the performance and scalability of Bayesian models.ArviZ is a library for post-modeling workflows in Bayesian statistics, providing tools for model diagnostics and result visualization.Gaussian processes are versatile non-parametric models that can be used for spatial and temporal data analysis in Bayesian statistics. Chapters: 00:00 Introduction to Bayesian Statistics 07:32 Advantages of Bayesian Methods 16:22 Incorporating Priors in Models 23:26 Modeling Causal Relationships 30:03 Introduction to PyMC, Stan, and Bambi 34:30 Choosing the Right Bayesian Framework 39:20 Getting Started with Bayesian Statistics 44:39 Understanding Bayesian Statistics and PyMC 49:01 Leveraging PyTensor for Improved Performance and Scalability 01:02:37 Exploring Post-Modeling Workflows with ArviZ 01:08:30 The Power of Gaussian Processes in Bayesian Modeling Thank 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,...

    1h 31m
  8. Advanced Bayesian Regression, with Tomi Capretto

    AUG 7

    Advanced Bayesian Regression, with Tomi Capretto

    Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch! My Intuitive Bayes Online Courses1:1 Mentorship with me 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 ;) Takeaways: Teaching Bayesian Concepts Using M&Ms: Tomi Capretto uses an engaging classroom exercise involving M&Ms to teach Bayesian statistics, making abstract concepts tangible and intuitive for students.Practical Applications of Bayesian Methods: Discussion on the real-world application of Bayesian methods in projects at PyMC Labs and in university settings, emphasizing the practical impact and accessibility of Bayesian statistics.Contributions to Open-Source Software: Tomi’s involvement in developing Bambi and other open-source tools demonstrates the importance of community contributions to advancing statistical software.Challenges in Statistical Education: Tomi talks about the challenges and rewards of teaching complex statistical concepts to students who are accustomed to frequentist approaches, highlighting the shift to thinking probabilistically in Bayesian frameworks.Future of Bayesian Tools: The discussion also touches on the future enhancements for Bambi and PyMC, aiming to make these tools more robust and user-friendly for a wider audience, including those who are not professional statisticians.  Chapters: 05:36 Tomi's Work and Teaching 10:28 Teaching Complex Statistical Concepts with Practical Exercises 23:17 Making Bayesian Modeling Accessible in Python 38:46 Advanced Regression with Bambi 41:14 The Power of Linear Regression 42:45 Exploring Advanced Regression Techniques 44:11 Regression Models and Dot Products 45:37 Advanced Concepts in Regression 46:36 Diagnosing and Handling Overdispersion 47:35 Parameter Identifiability and Overparameterization 50:29 Visualizations and Course Highlights 51:30 Exploring Niche and Advanced Concepts 56:56 The Power of Zero-Sum Normal 59:59 The Value of Exercises and Community 01:01:56 Optimizing Computation with Sparse Matrices 01:13:37 Avoiding MCMC and Exploring Alternatives 01:18:27 Making Connections Between Different Models Thank you to my Patrons for making this episode...

    1h 27m

Trailers

Ratings & Reviews

4.7
out of 5
61 Ratings

About

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!

You Might Also Like

To listen to explicit episodes, sign in.

Stay up to date with this show

Sign in or sign up to follow shows, save episodes, and get the latest updates.

Select a country or region

Africa, Middle East, and India

Asia Pacific

Europe

Latin America and the Caribbean

The United States and Canada