Learning Bayesian Statistics

Alexandre Andorra

Booking Overview

An interview-led statistics and data-science show focused on Bayesian inference, statistical workflows, research applications, and open-source modeling tools, typically featuring academic researchers, authors, practitioners, and software developers. A PR agency could credibly pitch Bayesian statisticians, quantitative researchers, data scientists, probabilistic-programming developers, and authors of relevant technical books; booking difficulty is medium because deep subject-matter expertise is essential, although mass-market fame is not.

Metrics

Episodes: 222

Frequency: Several times a week

Rating: 4.7/5.0

Estimated listeners: 1,000 - 10,000

Listener location: USA

YouTube: 5.7k subscribers

Contact Information

Publicly listed emails

learnbayesstats@gmail.com

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Host

Alexandre Andorra - Senior data scientist, open-source enthusiast, and core contributor to the PyMC and ArviZ Python packages.

Booking Intelligence

Booking Requirements

medium

Typical Credentials:

Researchers, statisticians, data scientists, authors, and open-source developers with demonstrated expertise in Bayesian inference, statistical modeling, probabilistic programming, or applied quantitative research.

Required Achievements:

Published research, Development of open-source statistical software, Authorship of technical or academic books, Leadership of specialized research or modeling projects

Recent Guest Discussions

Alex Fengler - Hierarchical Sequential Sampling Models, Drift Diffusion Models, Likelihood Approximation Networks, Amortized Inference, And Hssm.

Richard McElreath - Bayesian Workflow, Generative Models, Causal Inference, Principal Stratification, And Practical Statistical Analysis.

Aki Vehtari - Bayesian Workflow, Model Checking, Prior Sensitivity, Generative Simulation, And Communicating Posterior Uncertainty.

Andrew Gelman - Bayesian Workflow, Generative Modeling, Model Checking, Causal Inference, Principal Stratification, And Communicating Uncertainty.

Thomas Pinder - Gaussian Processes, Gpjax Development, Software Usability, Documentation, And Real-world Modeling Applications.

Recent Topics

Science, Statistics, Bayesian Inference, Probabilistic Programming, Statistical Modeling

Episodes

Here's the recent few episodes on

Learning Bayesian Statistics

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

October 07, 2026

Today's clip is from Episode 166, featuring Bill Engels & Jesse Grabowski. In this conversation, Bill explains what a kernel means in a Gaussian process and how it defines the idea of similarity between data points. He then breaks down the role of the length scale, showing how it controls how quickly the relationship between two points changes, and how amplitude controls the overall scale of the functions a GP can represent. The discussion also connects these ideas to the practical use of...

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

October 01, 2026

Support & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Takeaways:Q: What is PTGP and why did Bill and Jesse build it?A: PTGP is a new Gaussian process library built on PyTensor and PyMC, aimed squarely at practitioners rather than researchers assembling their own GP methods from papers. Bill Engels, who wrote PyMC's original GP submodule ...

How AI Can Evaluate Bayesian Workflows with Bayesify

September 28, 2026

Today's clip is from Episode 165, featuring Alex Fengler. In this conversation, Alex introduces Bayesify , a tool that uses AI to analyze research papers and assess how well they follow a Bayesian workflow.He explains how the tool breaks an analysis down step by step, identifies strengths and weaknesses, and provides suggestions for improving the paper. They also discuss how BasiFi can be used as an educational resource, a review engine for researchers, and potentially as a way to study how t...

Publicly listed emails

learnbayesstats@gmail.com

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