Probabilistic Programming
Probabilistic programming is a way to specify statistical models as programs and let inference machinery estimate the unknown quantities.
The reason it matters for machine learning practitioners is not just flexibility. It changes the modeling posture. Instead of only asking for a prediction function, we can describe a partial theory of how the data could have been produced, encode uncertainty, and update beliefs as evidence arrives.
This is attractive for public infrastructure problems because the analyst often has a mixture of:
- observed data
- missing or biased records
- expert knowledge
- physical constraints
- uncertainty about latent conditions
- decisions that need defensible explanations
Probabilistic programming can support interpretable statistical models, but it is not magic. The model still depends on assumptions, data quality, and domain review.
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