Bayes' theorem explained: revising predictions as new evidence arrives
mdancho84 · x · 2026-09-19
Part 2 of mdancho84's Bayesian machine learning series explains Bayes' theorem: the mathematical formula for updating the probability of a hypothesis as more evidence becomes available — the process of revising existing predictions in light of new data, known as Bayesian inference.
Related event: Bayesian ML Series Explores Theorem and Parameter Distributions(2 posts)→
More from Research
- MiMo-V2.6 livestreams its RL run at ~2B tokens/step as Stanford's Marin pretrains in public — stanfordnlp · 2026-09-20
- Schmidhuber's AI Blog Surveys Recursive Self-Improvement from 1987 and the 1991 Roots of Today's AI Boom — SchmidhuberAI · 2026-09-20
- Running a Jev-inspired agent on a laptop: Qwen 3 1.7B plays VIZDOOM locally on 4GB VRAM — Inside_Ad_6240 · 2026-09-20
- Skeptical deep dive confirms Humanity's Last Exam errors; official o3-mini grader marked right answers wrong every time — paul_cal · 2026-09-20
- Sarah Hooker shares a Colab notebook that 'invents' a dataset in a few lines of code — sarahookr · 2026-09-20
- A 0.62-AUC classifier helped discover 6 new altermagnets: AI's job is making one loop step cheap — bravo_abad · 2026-09-20