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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, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire and Mike Loncaric.

Takeaways:

• The evolution of sports modeling is tied to the availability of high-frequency data.
• Bayesian methods are valuable in handling messy, hierarchical data.
• Communication between data scientists and decision-makers is crucial for effective model use.
• Models are often wrong, and learning from mistakes is part of the process.
• Simplicity in models can sometimes yield better results than complexity.
• The integration of analytics in sports is still developing, with opportunities in various sports.
• Transparency in research and development teams enhances decision-making.
• Understanding uncertainty in models is essential for informed decisions.
• The balance between point estimates and full distributions is a...

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 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 offers a robust framework for econometric modeling.
• State space models provide a comprehensive way to understand time series data.
• Gaussian random walks serve as a foundational model in time series analysis.
• Innovations represent external shocks that can significantly impact forecasts.
• Understanding the assumptions behind models is key to effective forecasting.
• Complex models are not always better; simplicity can be powerful.
• Forecasting requires careful consideration of potential disruptions. Understanding observed and hidden states is crucial in modeling.
• Latent abilities can be modeled as Gaussian random walks.
• State space models can be highly flexible and diverse.
• Composability allows for the integration of different model components.
• Trends in time series should reflect real-world dynamics.
• Seasonality can be captured through Fourier bases.
• AR components help model residuals in time series data.
• Exogenous regression components can enhance state space models.
• Causal analysis in time series often involves interventions and counterfactuals.
• Time-varying regression allows for dynamic relationships between variables.
• Kalman filters were originally developed for tracking rockets in space.
• The Kalman filter iteratively updates beliefs based on new data.
• Missing data can be treated as hidden states in the Kalman filter framework.
• The Kalman filter is a practical application of Bayes' theorem in a sequential context.
• Understanding the dynamics of systems is crucial for effective modeling.
• The state space module in PyMC simplifies complex time series modeling tasks.

Chapters:

00:00 Introduction to Jesse Krabowski and Time Series Analysis

04:33 Jesse's Journey into Bayesian Statistics

10:51 Exploring State Space Models

18:28 Understanding State Space Models and Their Components

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 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:

• BART models are non-parametric Bayesian models that approximate functions by summing trees.
• BART is recommended for quick modeling without extensive domain knowledge.
• PyMC-BART allows mixing BART models with various likelihoods and other models.
• Variable importance can be easily interpreted using BART models.
• PreliZ aims to provide better tools for prior elicitation in Bayesian statistics.
• The integration of BART with Bambi could enhance exploratory modeling.
• Teaching Bayesian statistics involves practical problem-solving approaches.
• Future developments in PyMC-BART include significant speed improvements.
• Prior predictive distributions can aid in understanding model behavior.
• Interactive learning tools can enhance understanding of statistical concepts.
• Integrating PreliZ with PyMC improves workflow transparency.
• Arviz 1.0 is being completely rewritten for better usability.
• Prior elicitation is crucial in Bayesian modeling.
• Point intervals and forest plots are effective for visualizing complex data.

Chapters:

00:00 Introduction to Osvaldo Martin and Bayesian Statistics

08:12 Exploring Bayesian Additive Regression Trees (BART)

18:45 Prior Elicitation and the PreliZ Package

29:56 Teaching Bayesian Statistics and Future Directions

45:59 Exploring Prior Predictive Distributions

52:08 Interactive Modeling with PreliZ

54:06 The Evolution of ArviZ

01:01:23 Advancements in ArviZ 1.0

01:06:20 Educational Initiatives in Bayesian Statistics

01:12:33 The Future of Bayesian Methods

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...

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 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:

• Effective data science education requires feedback and rapid iteration.
• Building LLM applications presents unique challenges and opportunities.
• The software development lifecycle for AI differs from traditional methods.
• Collaboration between data scientists and software engineers is crucial.
• Hugo's new course focuses on practical applications of LLMs.
• Continuous learning is essential in the fast-evolving tech landscape.
• Engaging learners through practical exercises enhances education.
• POC purgatory refers to the challenges faced in deploying LLM-powered software.
• Focusing on first principles can help overcome integration issues in AI.
• Aspiring data scientists should prioritize problem-solving over specific tools.
• Engagement with different parts of an organization is crucial for data scientists.
• Quick paths to value generation can help gain buy-in for data projects.
• Multimodal models are an exciting trend in AI development.
• Probabilistic programming has potential for future growth in data science.
• Continuous learning and curiosity are vital in the evolving field of data science.

Chapters:

09:13 Hugo's Journey in Data Science and Education

14:57 The Appeal of Bayesian Statistics

19:36 Learning and Teaching in Data Science

24:53 Key Ingredients for Effective Data Science Education

28:44 Podcasting Journey and Insights

36:10 Building LLM Applications: Course Overview

42:08 Navigating the Software Development Lifecycle

48:06 Overcoming Proof of Concept Purgatory

55:35 Guidance for Aspiring Data Scientists

01:03:25 Exciting Trends in Data Science and AI

01:10:51 Balancing Multiple Roles in Data Science

01:15:23 Envisioning Accessible Data Science for All

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 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:

• 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 Analytics

30:10 Challenges and Advantages of Bayesian Approaches in SEM and CFA

33:58 Evaluating Bayesian Models

39:50 Challenges in Model Building

44:15 Causal Relationships in SEM and CFA

49:01 Practical Applications of SEM and CFA

51:47 Influence of Philosophy on Data Science

54:51 Designing Models with Confounding in Mind

57:39 Future Trends in Causal Inference

01:00:03 Advice for Aspiring Data Scientists

01:02:48 Future Research Directions

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,

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 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 ;)

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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 Epidemiology

03:35 Guest Backgrounds and Their Journey

10:04 Understanding Computational Biology vs. Epidemiology

16:11 The Role of Bayesian Statistics in Epidemiology

21:40 Recent Projects and Applications in Epidemiology

31:30...

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 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,...

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

• My Intuitive Bayes Online Courses
• 1:1 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...