Approximate Inference: Sampling the Posterior vs Optimizing an Approximation
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In a simple probabilistic model, posterior inference sounds straightforward: observe some data, compute the posterior distribution over hidden variables, and use it to answer the question you care about. In complex models, that computation is often the hard part. The latent space may be high-dimens…
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- 2026-09-10 04:54 · DEV Community — Machine Learning
Approximate Inference: Sampling the Posterior vs Optimizing an Approximation