Learning Bayesian Statistics

Why Bayesian Statistics Is More Computational Than Ever


Listen Later

Today's clip is from Episode 158 featuring Stefan Radev. In this conversation, Alex Andorra and Stefan break down a core argument from their paper: Bayesian statistics has never been more computational than it is now, and simulation is the thread that ties the whole workflow together.

Stefan parcellates the Bayesian workflow into four stages, and this clip covers the first two. Stage one is model specification, where the workflow community has long recommended prior predictive checks. You can do this informally, just running simulations from your model and eyeballing whether the output meets your expectations, or formally, à la Michael Betancourt, by pushing your model's high-dimensional output through a transformation into a low-dimensional, interpretable space and checking it against reality.

The punchline: a surprising number of models can be discarded before you've even seen real data, yet Stefan notes these checks remain underused in practice.

Stage two is model verification, where the question shifts to whether your inferences are well calibrated. This is the territory of simulation-based calibration and parameter recovery studies, classic tools that have always carried a steep computational price. You simulate thousands of synthetic datasets and run inference on every single one, which is exactly why these checks are so often skipped in papers, even though doing one well can be a contribution in its own right.

Here's where amortized simulation-based inference changes the math entirely. Checks that used to take days now take seconds, and instead of laboriously running inference dataset by dataset, you get millions of posterior samples essentially for free. The calibration checks that the field has always known it should be doing finally become cheap enough to actually do.

Get the full discussion here

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

...more
View all episodesView all episodes
Download on the App Store

Learning Bayesian StatisticsBy Alexandre Andorra

  • 4.7
  • 4.7
  • 4.7
  • 4.7
  • 4.7

4.7

66 ratings


More shows like Learning Bayesian Statistics

View all
Odd Lots by Bloomberg

Odd Lots

1,978 Listeners

Conversations with Tyler by Mercatus Center at George Mason University

Conversations with Tyler

2,457 Listeners

Talk Python To Me by Michael Kennedy

Talk Python To Me

583 Listeners

The Quanta Podcast by Quanta Magazine

The Quanta Podcast

548 Listeners

Super Data Science: ML & AI Podcast with Jon Krohn by Jon Krohn

Super Data Science: ML & AI Podcast with Jon Krohn

301 Listeners

Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas by Sean Carroll

Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas

4,176 Listeners

Practical AI by Practical AI LLC

Practical AI

213 Listeners

Last Week in AI by Skynet Today

Last Week in AI

318 Listeners

Machine Learning Street Talk (MLST) by Machine Learning Street Talk (MLST)

Machine Learning Street Talk (MLST)

97 Listeners

Dwarkesh Podcast by Dwarkesh Patel

Dwarkesh Podcast

561 Listeners

Hard Fork by The New York Times

Hard Fork

5,544 Listeners

Latent Space: The AI Engineer Podcast by Latent.Space

Latent Space: The AI Engineer Podcast

100 Listeners

Risky Business with Nate Silver and Maria Konnikova by Pushkin Industries

Risky Business with Nate Silver and Maria Konnikova

261 Listeners

Prof G Markets by Vox Media Podcast Network

Prof G Markets

1,486 Listeners

The Opinions by The New York Times Opinion

The Opinions

633 Listeners