Bayesian Thinking
ProbabilityDecision MakingData Analysis
Bayesian Thinking uses probability to update beliefs based on new evidence, emphasizing continuous learning.
Introduction
Bayesian Thinking uses probability to update beliefs based on new evidence, emphasizing continuous learning.
Core Concepts
- Prior Probability: Initial belief about an event.
- Posterior Probability: Updated belief after considering evidence.
- Bayes' Theorem: A mathematical formula for calculating conditional probabilities.
Applications
- Medical diagnostics for refining probabilities of conditions.
- Data science for predictive modeling.
Related Resources
- Tools: Bayesian inference tools like PyMC3.
- Book: "The Signal and the Noise" by Nate Silver.