PyData NYC 2024

Saving Sharks... with Python, Causal Inference and Bayesian Stats!
11-08, 10:55–12:20 (US/Eastern), Radio City

Live recording of the Learning Bayesian Statistics Podcast


What you're in for

Join Alexandre Andorra and Aaron MacNeil for a live recording of Learning Bayesian Statistics Podcast.

Every two weeks Alex Andorra interviews researchers and practitioners about their projects, walk of life, and why and how they use Bayesian methods.

In this special live episode, Alex talks to Aaron MacNeil about his latest project, inferring trade of shark meat between countries. So prepare for a lot of missing data, causal inference and, of course, Bayesian statistics!

Curious? Here is a teaser of this fantastic project!
Come join the Q&A, connect with the speakers, and collect some exclusive Bayesian stickers 😏

More about the guest

Originally from Wolfville, NS Canada, Aaron heads the Integrated Fisheries lab at Dalhousie University and is a marine ecologist working to solve small-scale fisheries problems in the context of climate change, using Bayesian models to integrate across disciplines and make inferences about how the world works.

He has worked extensively on interdisciplinary projects in coral reef ecosystems throughout the Indo-Pacific and is applying these approaches to a wide range of stakeholder-relevant projects globally.

Aaron contributes extensively to the fisheries and ecology literature in areas relating to environmental disturbance, human impacts, fisheries livelihoods, and food web structure.

More about the show

Here is the latest live episode, recorded at StanCon 2024, about the future of Stan and the latest computing algorithms.

With more than 5 years of back-catalogue, you'll definitely find an episode to listen to -- whether with Andrew Gelman, Matt Hoffman, Allen Downey, and many others!


Prior Knowledge Expected

No previous knowledge expected

See also:

⚾ Senior Applied Scientist @ Miami Marlins
🎙️ Creator @ LearnBayesStats Podcast
📊 Cofounder @ PyMC Labs
👨‍🏫 Teacher @ Intuitive Bayes

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