"Probabilistic programming language"의 두 판 사이의 차이

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* ID :  [https://www.wikidata.org/wiki/Q7246865 Q7246865]

2020년 12월 26일 (토) 06:01 판

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  • It will then proceed to reading and discussion of primary sources about probabilistic programming languages.[1]
  • Any project related to probabilistic programming language is acceptable.[1]
  • This book explains how to implement PPLs by lightweight embedding into a host language.[2]
  • We illustrate this by designing and implementing WebPPL, a small PPL embedded in Javascript.[2]
  • Probabilistic programming languages are designed to describe probabilistic models and then perform inference in those models.[3]
  • Probabilistic programming languages can accommodate the uncertain and incomplete information that is so common in the business domain.[4]
  • We demonstrate the ideas with a new probabilistic programming language called Birch, with a multiple object tracking example.[5]
  • BUGS is a probabilistic programming language originally developed by statisticians more than 20 years ago.[6]
  • Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend.[7]
  • This chapter also introduces Figaro, the probabilistic programming language based on Scala that’s used throughout the book.[8]
  • Stan is a probabilistic programming language for specifying statistical models.[9]
  • We extend the expressivity of an existing probabilistic programming language, Infer.[10]
  • To illustrate the simplicity of PPLs, let’s use one of the most famous problems of modern statistics: a biased coin toss.[11]
  • Pyro is a universal PPL — it can represent any computable probability distribution.[11]
  • As stated above, probabilistic programming languages and tools allow us to automate Bayesian inference.[12]
  • PPLs often extend from a basic language.[13]
  • This is actually really cool because graph problems are something that PPLs really shine at.[14]
  • The first thing that makes a language a probabilistic programming language (PPL) is a set of primitives for drawing random numbers.[15]
  • At this point, a PPL looks like any old imperative language with a rand call.[15]
  • Forest is a dizzying resource of generative models written in PPLs for domains from cognition and NLP to document retrieval.[15]
  • The development of Turing-complete probabilistic programming languages remains a highly specialized discipline.[16]

소스

  1. ↑ 1.0 1.1 Probabilistic Programming Languages Fall 2016
  2. ↑ 2.0 2.1 The Design and Implementation of Probabilistic Programming Languages
  3. ↑ Probabilistic Programming
  4. ↑ Applications of Probabilistic Programming
  5. ↑ Automated learning with a probabilistic programming language: Birch
  6. ↑ What is probabilistic programming?
  7. ↑ Pyro
  8. ↑ Chapter 1. Probabilistic programming in a nutshell · Practical Probabilistic Programming
  9. ↑ Stan: A Probabilistic Programming Language, Grantee Submission, 2017-Jan
  10. ↑ ABC–Fun: A Probabilistic Programming Language for Biology
  11. ↑ 11.0 11.1 A Gentle Introduction to Probabilistic Programming Languages
  12. ↑ The Future of Artificial Intelligence Part 1 – Probabilistic Programming Languages
  13. ↑ Probabilistic programming
  14. ↑ Probabilistic Programming for Software Engineers
  15. ↑ 15.0 15.1 15.2 Probabilistic Programming
  16. ↑ The Anglican Probabilistic Programming System

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