Variational Inference Explained: Turning Intractable Posteriors into Optimization
goyal__pramod · x · 2026-10-12
Matthew N. Bernstein, Principal ML Scientist at Cellular Intelligence, published a concise explainer on variational inference.
- The problem: computing the posterior P(Z|X) via Bayes' theorem is usually intractable because the denominator p(x) requires integrating over hidden variables and lacks a closed form.
- The idea: instead of exact computation, cast posterior estimation as an optimization problem — find a distribution q(z) close to the true posterior by minimizing their divergence.
- The technique underpins tools like VAEs and much of modern probabilistic machine learning, and the post offers an intuitive, beginner-friendly walkthrough.
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