64 episodes

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 Paris. 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!

This podcast uses the following third-party services for analysis:

Podcorn - https://podcorn.com/privacy

Learning Bayesian Statistics Learn Bayes Stats

    • Technology
    • 4.7 • 47 Ratings

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 Paris. 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!

This podcast uses the following third-party services for analysis:

Podcorn - https://podcorn.com/privacy

    Why we still use non-Bayesian methods, with EJ Wagenmakers

    Why we still use non-Bayesian methods, with EJ Wagenmakers

    The big problems with classic hypothesis testing are well-known. And yet, a huge majority of statistical analyses are still conducted this way. Why is it? Why are things so hard to change? Can you even do (and should you do) hypothesis testing in the Bayesian framework?

    • 1 hr 16 min
    Modeling Dialogues & Languages, with J.P. de Ruiter

    Modeling Dialogues & Languages, with J.P. de Ruiter

    Using Bayesian statistics to improve our understanding of how humans and artificial agents use language, gesture and other types of signals to effectively communicate with each other.

    • 1 hr 12 min
    Bayesian Modeling in Civil Engineering, with Michael Faber

    Bayesian Modeling in Civil Engineering, with Michael Faber

    How to govern and manage risks, resilience and sustainability in the built environment... with Bayesian statistics!

    • 59 min
    Bayesian Modeling and Computation, with Osvaldo Martin, Ravin Kumar and Junpeng Lao

    Bayesian Modeling and Computation, with Osvaldo Martin, Ravin Kumar and Junpeng Lao

    Get a hands-on approach, focusing on the practice of applied statistics. And you'll see how to use diverse libraries, like PyMC, Tensorflow Probability, ArviZ, Bambi, and so on!

    • 1 hr 9 min
    Forecasting French Elections, with... Mystery Guest

    Forecasting French Elections, with... Mystery Guest

    Alex made us discover new methods, new ideas, and mostly new people. But what do we really know about him? Does he even really exist? To find this out I put on my Frenchest beret, a baguette under my arm, and went undercover to try to find him.

    • 1 hr 21 min
    Causal & Probabilistic Machine Learning, with Robert Osazuwa Ness

    Causal & Probabilistic Machine Learning, with Robert Osazuwa Ness

    How do you make sure your computer doesn’t just happily (and mistakenly) report correlations as causations? That’s when causal and probabilistic machine learning enter the stage, as Robert Ness will tell us...

    • 1 hr 8 min

Customer Reviews

4.7 out of 5
47 Ratings

47 Ratings

Iameteore ,

Coolest show around

Super inspiring discussions with awesome tips and real life experience !
Cant wait for the next episode to come out 🔥

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