Bayesian thinking
A way to update beliefs with new evidence, treating probabilities as degrees of certainty.
Bayesian thinking is a method for reasoning under uncertainty. It's about starting with a belief about something, and then adjusting that belief as you get new information.
Imagine you think there's a 50% chance it will rain tomorrow. If you see dark clouds forming, you'd update your belief to a higher chance of rain. Bayesian thinking provides a mathematical framework to do this updating systematically.
Bayesian thinking is a probabilistic approach to reasoning where beliefs are represented as probability distributions. These beliefs are updated using Bayes' theorem when new evidence is observed. The process involves a prior belief, a likelihood function describing the evidence given a hypothesis, and a posterior belief that combines the prior with the evidence.
This framework is fundamental in areas like probabilistic machine learning, allowing models to quantify uncertainty and adapt their internal states based on incoming data. Tradeoffs include computational cost for complex models and the subjective nature of prior selection.
Frontier AI labs are exploring Bayesian thinking to build more robust and reliable AI systems. This approach helps models understand and express their own uncertainty, which is crucial for safe and trustworthy decision-making.
- The mathematics of AI uncertaintyGoogle · Google DeepMind: The Podcast ·
- Google Gemini
- Google Gemini's research often touches on probabilistic methods and uncertainty quantification, aligning with Bayesian principles for building more reliable AI.
- Frequentist statistics
- This approach defines probability as the long-run freque
The concept is named after Reverend Thomas Bayes, an 18th-century statistician and theologian. His work on probability was published posthumously. Source ↗
When using a generative AI like ChatGPT or Gemini, ask it to explain a concept and then ask it to explain it again, but assuming you know nothing about the topic. Notice how it adjusts its explanation based on your implied feedback.
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Questions people ask
- What is a prior belief?
- A prior belief is your initial assessment of probability before considering any new evidence.
- How does new information change beliefs?
- New information is used through Bayes' theorem to update the prior belief into a posterior belief, reflecting a revised degree of certainty.
First seen on Subvolts Aug 26, 2026. Updated Sep 16, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.