Probabilistic programming language

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• It will then proceed to reading and discussion of primary sources about probabilistic programming languages.
• Any project related to probabilistic programming language is acceptable.
• This book explains how to implement PPLs by lightweight embedding into a host language.
• We illustrate this by designing and implementing WebPPL, a small PPL embedded in Javascript.
• Probabilistic programming languages are designed to describe probabilistic models and then perform inference in those models.
• Probabilistic programming languages can accommodate the uncertain and incomplete information that is so common in the business domain.
• We demonstrate the ideas with a new probabilistic programming language called Birch, with a multiple object tracking example.
• BUGS is a probabilistic programming language originally developed by statisticians more than 20 years ago.
• Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend.
• This chapter also introduces Figaro, the probabilistic programming language based on Scala that’s used throughout the book.
• Stan is a probabilistic programming language for specifying statistical models.
• We extend the expressivity of an existing probabilistic programming language, Infer.
• To illustrate the simplicity of PPLs, let’s use one of the most famous problems of modern statistics: a biased coin toss.
• Pyro is a universal PPL — it can represent any computable probability distribution.
• As stated above, probabilistic programming languages and tools allow us to automate Bayesian inference.
• PPLs often extend from a basic language.
• This is actually really cool because graph problems are something that PPLs really shine at.
• The first thing that makes a language a probabilistic programming language (PPL) is a set of primitives for drawing random numbers.
• At this point, a PPL looks like any old imperative language with a rand call.
• Forest is a dizzying resource of generative models written in PPLs for domains from cognition and NLP to document retrieval.
• The development of Turing-complete probabilistic programming languages remains a highly specialized discipline.

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• ID : Q7246865

Spacy 패턴 목록

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• [{'LOWER': 'probabilistic'}, {'LOWER': 'relational'}, {'LOWER': 'programming'}, {'LEMMA': 'language'}]
• [{'LEMMA': 'PPL'}]