Learning Bayesian Statistics

About Learning Bayesian Statistics

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 (https://alexandorra.github.io/) by the way. By day, I'm a Senior data scientist. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages PyMC (https://docs.pymc.io/) and ArviZ (https://arviz-devs.github.io/arviz/). I also love 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 (https://www.patreon.com/learnbayesstats)!

Publisher

Alexandre Andorra

Category

science · technology

Language

en

Explicit

No

First episode

20 Sept 2019

Latest episode

7 Oct 2026

Latest episodes

122 of 227 episodes have a transcript.

1. 

   7 Oct 2026TRANSCRIPT

   What Do Kernels, Length Scale & Amplitude Mean in Gaussian Processes?

   Host Bill Engels interviews Jesse Grabowski, explaining how kernels, length scale, and amplitude define Gaussian process similarity and function representation for modeling nonlinear relationships.

2. 

   1 Oct 2026TRANSCRIPT

   #166 PTGP: A New Gaussian Process Library, with Bill Engels & Jesse Grabowski

   Host Alex Andorra interviews Bill Engels and Jesse Grabowski about PTGP, a new Gaussian process library designed for practitioners, offering battle-tested algorithms and debugging knowledge.

3. 

   28 Sept 2026TRANSCRIPT

   How AI Can Evaluate Bayesian Workflows with Bayesify

   Host Alex Fengler explains how the AI tool Bayesify evaluates Bayesian statistical workflows in research papers, offering insights for improvement and educational use in this interview.

Inside a recent episode

What Do Kernels, Length Scale & Amplitude Mean in Gaussian Processes?

Published 7 Oct 2026 · Transcript excerpt

[…] But working in just sort of regular data where If x is 5 and another x is 10, the length scale parameter says, okay, is 5 and 10, they're 5 apart? Are we going to think of that as similar or not? And the length scale, what it basically does is scale the inputs. That's the simplest way to think about it. And you can do a little algebra on a kernel function like the exponential quadratic, and you can see that the length scale just divides each x by a scale. And, you know, you can divide X equals 5 and X equals 10, you know, divide them both by 5, and now they're 1 and 2. So now they're only one unit apart. […]

Who has been a guest on Learning Bayesian Statistics

Names identified in recent episode analyses. Showing up to five guests.

• Bill Engels
• Jesse Grabowski
• Alex Fengler
• Andrew Gelman
• Aki Vehtari

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

YouTube views

115,545

Score snapshot 15 Sept 2025

Apple Podcasts (US)

4.7 / 5

69 ratings

Spotify

4.7 / 5

85 ratings

Podcast Authority Score: 81 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

88

Social presence

0

YouTube

86

Engagement

99

Contact Learning Bayesian Statistics

Guest appearances

Books guests

Based on episode analysis; this does not confirm that the show is currently accepting guests.

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Questions about Learning Bayesian Statistics