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By Godfried T. Toussaint

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And, although you can update a simulation with known current information as you go along, it’s hard to include unknown information that must be inferred. As a result, the ability to learn from past experience to improve future predictions and analyses is limited. You can’t use simulations for machine learning. A probabilistic program is like a simulation that you can analyze, not just run. The key insight in developing probabilistic programming is that many of the inference algorithms that can be used for simpler modeling frameworks can also be used on simulations.

Why should my boss care? ■ How does it work? ■ Figaro—a system for probabilistic programming ■ A comparison between writing a probabilistic application with and without probabilistic programming In this chapter, you’ll learn how to make everyday decisions by using a probabilistic model and an inference algorithm—the two main components of a probabilistic reasoning system. You’ll also see how modern probabilistic programming languages make creating such reasoning systems far easier than a general-purpose language such as Java or Python would.

Figaro model Atomic elements Evidence Observations Compound elements Apply Chain Conditions Atomic elements are the basic building blocks. Compound elements connect simpler elements together. Apply and Chain are two important kinds of compound elements. Constraints Figaro inference algorithms Queries Instantiate Run Clean up Target elements Answers Probabilities of values Queries specify which target elements you’re interested in. Most likely values Figaro’s algorithms compute information about the target elements.

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