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Artwork for Learning Bayesian Statistics

Learning Bayesian Statistics

Alexandre Andorra
Bayesian Statistics
Bayesian Inference
Quantum Physics
Machine Learning
Causal Inference
Low Birth Weight Paradox
MIT
Reading Groups
Cambridge
Pymc Labs
David Spiegelhalter
Bob Carpenter
Sansa Kazadi
Andrew Gelman
Simpson's Paradox
Reactive Message Passing
Rxinfer.jl
Base Rate Fallacy
Statistical Physics
Variational Inference

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 Ba... more

PublishesWeeklyEpisodes173Founded6 years ago
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Artwork for Learning Bayesian Statistics

Latest Episodes

• Sign up for Alex's first live cohort, about Hierarchical Model building!

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

• Intro to Bayes Course (first 2 lessons free)

• Advanced Regression Course (first 2 ... more

• Soccer Factor Model Dashboard

• Unveiling True Talent: The Soccer Factor Model for Skill Evaluation

• LBS #91, Exploring European Football Analytics, with Max Göbel

Get early access to Alex's next live-cohort courses!

Today’s clip is from episode... more

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

• Get early access to Alex's next live-cohort courses!

• Intro to Bayes Course (first 2 lessons free)

• Advanced Regression Course (first 2 lessons free)

Our th... more

Get early access to Alex's next live-cohort courses!

Today’s clip is from episode 141 of the podcast, with Sam Witty.

Alex and Sam discuss the ChiRho project, delving into the intricacies of causal inference, particularly focusing on Do-Calculus, r... more

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Recent Guests

Christoph Bamberg
Researcher in cognitive science and health psychology
University of Salzburg
Episode: #143 Transforming Nutrition Science with Bayesian Methods, with Christoph Bamberg
Gabriel Stechschulte
A software engineer passionate about probabilistic programming and optimization.
Lutian University of Applied Sciences and Arts
Episode: #142 Bayesian Trees & Deep Learning for Optimization & Big Data, with Gabriel Stechschulte
Sam Witty
Founder of Cerberus AI and principal consultant at Sorbus AI.
Cerberus AI, Sorbus AI
Episode: #141 AI Assisted Causal Inference, with Sam Witty
Ron Yurko
Assistant Teaching Professor and Director of Sports Analytics
Carnegie Mellon University
Episode: #140 NFL Analytics & Teaching Bayesian Stats, with Ron Yurko
Max Balandat
Research Scientist Manager at Meta, leads the modeling and optimization team and oversees the development of BoTorch.
Meta
Episode: #139 Efficient Bayesian Optimization in PyTorch, with Max Balandat
Melody Monod
Senior postdoc researcher at the University of Oxford.
University of Oxford
Episode: #138 Quantifying Uncertainty in Bayesian Deep Learning, Live from Imperial College London
Ying Zheng
Associate Professor at the Computing Department at Imperial College London, expert in machine learning.
Imperial College London
Episode: #138 Quantifying Uncertainty in Bayesian Deep Learning, Live from Imperial College London
François-Xavier Briol
Associate Professor in Stats at UCL, emphasizes the integration of machine learning with Bayesian statistics.
University College London
Episode: #138 Quantifying Uncertainty in Bayesian Deep Learning, Live from Imperial College London
Robert Ness
Expert in causal inference and machine learning
Episode: BITESIZE | Practical Applications of Causal AI with LLMs, with Robert Ness

Host

Alex Andorra
Host focusing on Bayesian Statistics and its applications. He is also a data scientist at PyMC Labs and actively contributes to open-source projects related to Bayesian methods.

Reviews

4.7 out of 5 stars from 230 ratings
  • Eric Trexler is the man

    I wanted to listen to this podcast but just can’t get past the annoying host .

    Apple Podcasts
    1
    ukfitness
    United Kingdom2 years ago
  • Amazing podcast

    Keep up the great work, I very much enjoy listening to this podcast :)

    Apple Podcasts
    5
    Funnymovie
    Germany2 years ago
  • Interesting Bayesian information

    Podcast Addict
    5
    bills
    3 years ago
  • The podcast for Bayesian statistics fans

    I first found this podcast searching for examples of applied Bayesian statistics. A year later, I still enjoy every show. It’s not only a great way to keep up to date with current developments in statistical programming but also a source of inspiration for how to apply statistical tools to any and all questions. A great add-on is that the podcast guests also talk about their failures, struggles and personal journeys, which makes statistics - a fearful and/or boring topic for many - more human ... more

    Apple Podcasts
    5
    Daniel Bern
    Germany3 years ago
  • Come for the great guests and their unique applications, stay for the novelty statistical rap music.

    Podchaser
    5
    Kristian Higgins
    4 years ago

Listeners Say

Key themes from listener reviews, highlighting what works and what could be improved about the show.

There are occasional mentions of audio quality issues that detract from the overall listening experience.
The show's emphasis on failures as a learning tool resonates well, making it relatable and engaging.
Feedback often centers on the selection of guests and the relevance of topics covered, with many finding them inspiring and applicable to their work.
Listeners appreciate the practical insights and educational value, highlighting how the podcast makes complex statistical topics more approachable.

Chart Rankings

How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.

Apple Podcasts
#231
United States/Technology
Apple Podcasts
#128
Australia/Technology
Apple Podcasts
#95
Mexico/Technology
Apple Podcasts
#100
Russia/Technology
Apple Podcasts
#111
Norway/Technology
Apple Podcasts
#122
Denmark/Technology

Talking Points

Recent interactions between the hosts and their guests.

BITESIZE | How Bayesian Additive Regression Trees Work in Practice
Q: What are the strengths and drawbacks of BART compared to other tree methods?
BART provides uncertainty quantification but is slower compared to XGBoost or LightGBM, while also having regularization techniques to avoid overfitting.
BITESIZE | How Bayesian Additive Regression Trees Work in Practice
Q: Can you talk about your re-implementation of BART in Rust?
Gabriel discusses his motivation to improve the performance of BART by re-implementing it in Rust and explains the collaborative process with Osvaldo.
#143 Transforming Nutrition Science with Bayesian Methods, with Christoph Bamberg
Q: What would your dream study entail?
It would involve using continuous physiological measurements to assess real-time effects on participants' feelings.
#143 Transforming Nutrition Science with Bayesian Methods, with Christoph Bamberg
Q: What were the main obstacles you faced with co-authors regarding your methods?
Co-authors often viewed Bayesian methods as more work, and it was difficult to convey the benefits.
#143 Transforming Nutrition Science with Bayesian Methods, with Christoph Bamberg
Q: What drew you to Bayesian Statistics?
I was recommended Statistical Rethinking, and it formed my foundation for Bayesian Statistics.

Audience Metrics

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Frequently Asked Questions About Learning Bayesian Statistics

What is Learning Bayesian Statistics about and what kind of topics does it cover?

This podcast provides a comprehensive exploration of Bayesian statistics and its myriad applications. Through discussions with researchers and practitioners from diverse fields, listeners are introduced to the fundamental concepts of Bayesian inference, learning not only successful methodologies but also the challenges and failures encountered along the way. The show makes complex statistical concepts accessible, inviting a broad audience aimed at researchers, data scientists, and anyone eager to integrate Bayesian methods into their workflows. A unique aspect is the emphasis on real-world applications, ranging from astrophysics to public health, showcasing how Bayesian statistics assist in solving critical issues in various domains.

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Rephonic provides a wide range of podcast stats for Learning Bayesian Statistics. We scanned the web and collated all of the information that we could find in our comprehensive podcast database. See how many people listen to Learning Bayesian Statistics and access YouTube viewership numbers, download stats, audience demographics, chart rankings, ratings, reviews and more.

How many listeners does Learning Bayesian Statistics get?

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Which podcasts are similar to Learning Bayesian Statistics?

These podcasts share a similar audience with Learning Bayesian Statistics:

1. Super Data Science: ML & AI Podcast with Jon Krohn
2. Machine Learning Street Talk (MLST)
3. Talk Python To Me
4. Data Engineering Podcast
5. Practical AI

How many episodes of Learning Bayesian Statistics are there?

Learning Bayesian Statistics launched 6 years ago and published 173 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

How do I contact Learning Bayesian Statistics?

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What guests have appeared on Learning Bayesian Statistics?

Recent guests on Learning Bayesian Statistics include:

1. Christoph Bamberg
2. Gabriel Stechschulte
3. Sam Witty
4. Ron Yurko
5. Max Balandat
6. Melody Monod
7. Ying Zheng
8. François-Xavier Briol

To view more recent guests and their details, simply upgrade your Rephonic account. You'll also get access to a typical guest profile to help you decide if the show is worth pitching.

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