Learning Bayesian Statistics

著者： Alexandre Andorra
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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 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)!
    Copyright Alexandre Andorra
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あらすじ・解説

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)!
Copyright Alexandre Andorra
科学
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エピソード
  • #121 Exploring Bayesian Structural Equation Modeling, with Nathaniel Forde

    #121 Exploring Bayesian Structural Equation Modeling, with Nathaniel Forde
    2024/12/11

    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,
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    1 時間 8 分
  • #120 Innovations in Infectious Disease Modeling, with Liza Semenova & Chris Wymant

    #120 Innovations in Infectious Disease Modeling, with Liza Semenova & Chris Wymant
    2024/11/27

    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...
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    1 時間 2 分
  • #119 Causal Inference, Fiction Writing and Career Changes, with Robert Kubinec

    #119 Causal Inference, Fiction Writing and Career Changes, with Robert Kubinec
    2024/11/13

    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,...
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    1 時間 25 分

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