Vanishing Gradients cover image

Episode 16: Data Science and Decision Making Under Uncertainty

Vanishing Gradients

CHAPTER

Understanding Causality and Probabilistic Thinking in Decision-Making

The chapter explores the significance of domain expertise in communicating decisions, focusing on causality and probabilistic thinking. Insights from Edwin James and Bayes are discussed, with examples like urn ball drawings and the Monty Hall problem to illustrate conditional probabilities. Emphasis is placed on explicit modeling, Bayesian analysis, and the importance of ensuring the superiority of complex models over simpler ones in decision-making processes.

00:00
Transcript
Play full episode

Remember Everything You Learn from Podcasts

Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.
App store bannerPlay store banner