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Learning Bayesian Statistics

Learning Bayesian Statistics

Alexandre Andorra
Scienza e Medicina, Tecnologia

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)!

134 - #119 Causal Inference, Fiction Writing and Career Changes, with Robert Kubinec
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  • 134 - #119 Causal Inference, Fiction Writing and Career Changes, with Robert Kubinec

    Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget 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 hisawesome 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, 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 and Stefan.

    Links from the show:

    Robert’s website (includes blog posts): https://www.robertkubinec.com/Robert on GitHub: https://github.com/saudiwinRobert on Linkedin: https://www.linkedin.com/in/robert-kubinec-9191a9a/Robert on Google Scholar: https://scholar.google.com/citations?user=bhOaXR4AAAAJ&hl=enRobert on Twitter: https://x.com/rmkubinecRobert on Bluesky: https://bsky.app/profile/rmkubinec.bsky.socialThe Bayesian Hitman: https://www.amazon.com/Bayesian-Hitman-Robert-M-Kubinec/dp/B0D6M4WNRZ/Ordbetareg overview: https://www.robertkubinec.com/ordbetaregIdealstan – this isn’t out yet, but you can access an older working paper here: https://osf.io/preprints/osf/8j2btOrdinal Regression tutorial, Michael Betancourt: https://betanalpha.github.io/assets/case_studies/ordinal_regression.htmlAndrew Heiss blog: https://www.andrewheiss.com/blog/

    Transcript

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    Wed, 13 Nov 2024 - 1h 25min
  • 133 - #118 Exploring the Future of Stan, with Charles Margossian & Brian Ward

    Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget 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 hisawesome 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 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 and Robert Flannery.

    Links from the show:

    Come see the show live at PyData NYC: https://pydata.org/nyc2024/LBS #90, Demystifying MCMC & Variational Inference, with Charles Margossian: https://learnbayesstats.com/episode/90-demystifying-mcmc-variational-inference-charles-margossian/Charles' website: https://charlesm93.github.io/Charles on GitHub: https://github.com/charlesm93Charles on LinkedIn: https://www.linkedin.com/in/charles-margossian-3428935b/Charles on Google Scholar: https://scholar.google.com/citations?user=nPtLsvIAAAAJ&hl=enCharles on Twitter: https://x.com/charlesm993Brian's website: https://brianward.dev/Brian on GitHub: https://github.com/WardBrianBrian on LinkedIn: https://www.linkedin.com/in/ward-brianm/Brian on Google Scholar: https://scholar.google.com/citations?user=bzosqW0AAAAJ&hl=enBrian on Twitter: https://x.com/ward_brianmBob Carpenter's reflections on StanCon: https://statmodeling.stat.columbia.edu/category/bayesian-statistics/

    Transcript

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    Wed, 30 Oct 2024 - 58min
  • 132 - #117 Unveiling the Power of Bayesian Experimental Design, with Desi Ivanova

    Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget 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 hisawesome 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, 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 and Gary Clarke.

    Links from the show:

    Come see the show live at PyData NYC: https://pydata.org/nyc2024/Desi’s website: https://desirivanova.com/Desi on GitHub: https://github.com/desi-ivanovaDesi on Google Scholar: https://scholar.google.com/citations?user=AmX6sMIAAAAJ&hl=enDesi on Linkedin: https://www.linkedin.com/in/dr-ivanova/Desi on Twitter: https://x.com/desirivanovaLBS #34, Multilevel Regression, Post-stratification & Missing Data, with Lauren Kennedy: https://learnbayesstats.com/episode/34-multilevel-regression-post-stratification-missing-data-lauren-kennedy/LBS #35, The Past, Present & Future of BRMS, with Paul Bürkner: https://learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner/LBS #45, Biostats & Clinical Trial Design, with Frank Harrell:https://learnbayesstats.com/episode/45-biostats-clinical-trial-design-frank-harrell/LBS #107, Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt: https://learnbayesstats.com/episode/107-amortized-bayesian-inference-deep-neural-networks-marvin-schmitt/Bayesian Experimental Design (BED) with BayesFlow and PyTorch: https://github.com/stefanradev93/BayesFlow/blob/dev/examples/michaelis_menten_BED_tutorial.ipynbPaper – Modern Bayesian Experimental Design: https://arxiv.org/abs/2302.14545Paper – Optimal experimental design; Formulations and computations: https://arxiv.org/pdf/2407.16212Information theory, inference and learning algorithms, by the great late Sir David MacKay:  https://www.inference.org.uk/itprnn/book.pdfPatterns, Predictions and Actions, Moritz Hard and Ben Recht  https://mlstory.org/index.html

    Transcript

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    Tue, 15 Oct 2024 - 1h 13min
  • 131 - #116 Mastering Soccer Analytics, with Ravi Ramineni

    Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget 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 hisawesome 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, 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 and Gary Clarke.

    Links from the show:

    LBS Sports Analytics playlist: https://www.youtube.com/playlist?list=PL7RjIaSLWh5kDiPVMUSyhvFaXL3NoXOe4Ravi on Linkedin: https://www.linkedin.com/in/ravi-ramineni-3798374/Ravi on Twitter: https://x.com/analyseFootyDecisions in Football - The Power of Compounding | StatsBomb Conference 2023: https://www.youtube.com/watch?v=D7CXtwDg9lMThe Signal and the Noise: https://www.amazon.com/Signal-Noise-Many-Predictions-Fail-but/dp/0143125087PreliZ – A tool-box for prior elicitation: https://preliz.readthedocs.io/en/latest/Ravi talking on Ted Knutson's podcast: https://open.spotify.com/episode/1exLBfyFf0d1dm2IaXkd2vMore about Ravi's work at the Seattle Sounders: https://www.trumedianetworks.com/expected-value-podcast/ravi-ramineni

    Transcript

    This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

    Wed, 02 Oct 2024 - 1h 32min
  • 130 - #115 Using Time Series to Estimate Uncertainty, with Nate Haines

    Proudly sponsored byPyMC Labs, the Bayesian Consultancy.Book a call, orget 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 hisawesome 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.

    Chapters:

    00:00 Introduction to Bayesian Modeling in Insurance

    13:00 Time Series Models and Their Applications

    30:51 Bayesian Model Averaging Explained

    56:20 Impact of External Factors on Forecasting

    01:25:03 Future of Bayesian Modeling and AI

    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 and Francesco Madrisotti.

    Links from the show:

    Nate’s website: http://haines-lab.com/Nate on GitHub: https://github.com/Nathaniel-HainesNate on Linkedin: https://www.linkedin.com/in/nathaniel-haines-216049101/Nate on Twitter: https://x.com/nate__hainesNate on Google Scholar: https://scholar.google.com/citations?user=lg741SgAAAAJLBS #14 Hidden Markov Models & Statistical Ecology, with Vianey Leos-Barajas: https://learnbayesstats.com/episode/14-hidden-markov-models-statistical-ecology-with-vianey-leos-barajas/LBS #107 Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt: https://learnbayesstats.com/episode/107-amortized-bayesian-inference-deep-neural-networks-marvin-schmitt/LBS #109 Prior Sensitivity Analysis, Overfitting & Model Selection, with Sonja Winter: https://learnbayesstats.com/episode/109-prior-sensitivity-analysis-overfitting-model-selection-sonja-winter/BayesBlend – Easy Model Blending: https://arxiv.org/abs/2405.00158BayesBlend documentation: https://ledger-investing-bayesblend.readthedocs-hosted.com/en/latest/SBC paper: https://arxiv.org/abs/1804.06788Isaac Asimov’s Foundation (Hari Seldon): https://en.wikipedia.org/wiki/Hari_SeldonStancon 2023 talk on Ledger’s Bayesian modeling workflow: https://github.com/stan-dev/stancon2023/blob/main/Nathaniel-Haines/slides.pdfLedger’s Bayesian modeling workflow: https://arxiv.org/abs/2407.14666v1More on Ledger Investing: https://www.ledgerinvesting.com/about-us

    Transcript

    This is an automatic transcript and may therefore contain errors. Pleaseget in touchif you're willing to correct them.

    Tue, 17 Sep 2024 - 1h 39min
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