How Probabilistic Reasoning Works — From Evidence to Better Beliefs
Probabilistic reasoning is a method used in AI to make decisions under uncertainty by updating beliefs based on new evidence. It relies on Bayes' theorem to revise prior beliefs as new information is introduced. This approach allows AI systems to adapt and improve their decision-making processes in real-world scenarios where complete information is often unavailable.
- ▪Probabilistic reasoning assigns probabilities to answers instead of treating them as simply true or false.
- ▪The process involves defining hypotheses, assigning initial probabilities, observing new evidence, and updating those probabilities.
- ▪Bayes' theorem is the central rule that explains how prior beliefs change after new evidence is observed.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3872570) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } zeromathai Posted on May 18 • Originally published at zeromathai.com How Probabilistic Reasoning Works — From Evidence to Better Beliefs #ai #machinelearning #probability #datascience AI often has to decide without complete information. The question is not always “What is true?” It is often: “What should we believe now that new evidence has arrived?” That is the core of probabilistic reasoning. Core Idea Probabilistic reasoning is a way to make decisions under uncertainty.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
Discussion
0 commentsMore from DEV.to (Top)
