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PodcastsRank #7444
Artwork for Learning Bayesian Statistics

Learning Bayesian Statistics

Alexandre ANDORRA
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TechnologyPodcastsScienceENunited-statesDaily or near-daily
4.7 / 5
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 <a target="_blank" rel="noopener noreferrer nofollow" href="https://alexandorra.github.io/">Alex Andorra</a> by the way.
Top 14.9% by pitch volume (Rank #7444 of 50,000)Data updated Feb 10, 2026

Key Facts

Publishes
Daily or near-daily
Episodes
187
Founded
N/A
Category
Technology
Number of listeners
Private
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Public snapshot
Audience: 20K–40K / month
Canonical: https://podpitch.com/podcasts/learning-bayesian-statistics
Cadence: Active monthly
Reply rate: Under 2%

Latest Episodes

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#150 Fast Bayesian Deep Learning, with David Rügamer, Emanuel Sommer & Jakob Robnik

Wed Jan 28 2026

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• Support & get perks! • Proudly sponsored by PyMC Labs! Get in touch at alex.andorra@pymc-labs.com • Intro to Bayes and Advanced Regression courses (first 2 lessons free) Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Chapters:00:00 Scaling Bayesian Neural Networks04:26 Origin Stories of the Researchers09:46 Research Themes in Bayesian Neural Networks12:05 Making Bayesian Neural Networks Fast16:19 Microcanonical Langevin Sampler Explained22:57 Bottlenecks in Scaling Bayesian Neural Networks29:09 Practical Tools for Bayesian Neural Networks36:48 Trade-offs in Computational Efficiency and Posterior Fidelity40:13 Exploring High Dimensional Gaussians43:03 Practical Applications of Bayesian Deep Ensembles45:20 Comparing Bayesian Neural Networks with Standard Approaches50:03 Identifying Real-World Applications for Bayesian Methods57:44 Future of Bayesian Deep Learning at Scale01:05:56 The Evolution of Bayesian Inference Packages01:10:39 Vision for the Future of Bayesian Statistics Thank you to my Patrons for making this episode possible! Come meet Alex at the Field of Play Conference in Manchester, UK, March 27, 2026! Links from the show: David Rügamer:* Website* Google Scholar* GitHubEmanuel Sommer:* Website* GitHub* Google ScholarJakob Robnik:* Google Scholar* GitHub* Microcanonical Langevin paper* LinkedIn

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• Support & get perks! • Proudly sponsored by PyMC Labs! Get in touch at alex.andorra@pymc-labs.com • Intro to Bayes and Advanced Regression courses (first 2 lessons free) Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Chapters:00:00 Scaling Bayesian Neural Networks04:26 Origin Stories of the Researchers09:46 Research Themes in Bayesian Neural Networks12:05 Making Bayesian Neural Networks Fast16:19 Microcanonical Langevin Sampler Explained22:57 Bottlenecks in Scaling Bayesian Neural Networks29:09 Practical Tools for Bayesian Neural Networks36:48 Trade-offs in Computational Efficiency and Posterior Fidelity40:13 Exploring High Dimensional Gaussians43:03 Practical Applications of Bayesian Deep Ensembles45:20 Comparing Bayesian Neural Networks with Standard Approaches50:03 Identifying Real-World Applications for Bayesian Methods57:44 Future of Bayesian Deep Learning at Scale01:05:56 The Evolution of Bayesian Inference Packages01:10:39 Vision for the Future of Bayesian Statistics Thank you to my Patrons for making this episode possible! Come meet Alex at the Field of Play Conference in Manchester, UK, March 27, 2026! Links from the show: David Rügamer:* Website* Google Scholar* GitHubEmanuel Sommer:* Website* GitHub* Google ScholarJakob Robnik:* Google Scholar* GitHub* Microcanonical Langevin paper* LinkedIn

Key Metrics

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Pitches sent
37
From PodPitch users
Rank
#7444
Top 14.9% by pitch volume (Rank #7444 of 50,000)
Average rating
4.7
Ratings count may be unavailable
Reviews
1
Written reviews (when available)
Publish cadence
Daily or near-daily
Active monthly
Episode count
187
Data updated
Feb 10, 2026
Social followers
6.5K

Public Snapshot

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TechnologyPodcastsScience
Country
United States
Language
English
Language (ISO)
Release cadence
Daily or near-daily
Latest episode date
Wed Jan 28 2026

Audience & Outreach (Public)

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Audience range
20K–40K / month
Public band
Reply rate band
Under 2%
Public band
Response time band
1–2 weeks
Public band
Replies received
1–5
Public band

Public ranges are rounded for privacy. Unlock the full report for exact values.

Presence & Signals

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Social followers
6.5K
Contact available
Yes
Masked on public pages
Sponsors detected
Private
Hidden on public pages
Guest format
Private
Hidden on public pages

Social links

No public profiles listed.

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Audience & Growth
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Monthly listeners49,360
Reply rate18.2%
Avg response4.1 days
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Reviews

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4.7 / 5
RatingsN/A
Written reviews1

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Frequently Asked Questions About Learning Bayesian Statistics

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What is Learning Bayesian Statistics about?

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 <a target="_blank" rel="noopener noreferrer nofollow" href="https://alexandorra.github.io/">Alex Andorra</a> by the way.

How often does Learning Bayesian Statistics publish new episodes?

Daily or near-daily

How many listeners does Learning Bayesian Statistics get?

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