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Foundations of probabilistic programming /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Foundations of probabilistic programming // edited by Gilles Barthe, Joost-Pieter Katoen, Alexandra Silva.
其他作者:	Silva, Alexandra,
面頁冊數:	1 online resource (xiv, 568 pages) :digital, PDF file(s). :
附註:	Title from publisher's bibliographic system (viewed on 20 Nov 2020).
標題:	Probabilities - Data processing. -
電子資源:	https://doi.org/10.1017/9781108770750
ISBN:	9781108770750 (ebook)

Foundations of probabilistic programming /
Foundations of probabilistic programming /edited by Gilles Barthe, Joost-Pieter Katoen, Alexandra Silva. - 1 online resource (xiv, 568 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 20 Nov 2020).

Open Access title.

What does a probabilistic program actually compute? How can one formally reason about such probabilistic programs? This valuable guide covers such elementary questions and more. It provides a state-of-the-art overview of the theoretical underpinnings of modern probabilistic programming and their applications in machine learning, security, and other domains, at a level suitable for graduate students and non-experts in the field. In addition, the book treats the connection between probabilistic programs and mathematical logic, security (what is the probability that software leaks confidential information?), and presents three programming languages for different applications: Excel tables, program testing, and approximate computing. This title is also available as Open Access on Cambridge Core.

ISBN: 9781108770750 (ebook)Subjects--Topical Terms:

678601
Probabilities
--Data processing.

LC Class. No.: QA76.6 / .F69 2021

Dewey Class. No.: 001.642

Foundations of probabilistic programming /
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520 $a What does a probabilistic program actually compute? How can one formally reason about such probabilistic programs? This valuable guide covers such elementary questions and more. It provides a state-of-the-art overview of the theoretical underpinnings of modern probabilistic programming and their applications in machine learning, security, and other domains, at a level suitable for graduate students and non-experts in the field. In addition, the book treats the connection between probabilistic programs and mathematical logic, security (what is the probability that software leaks confidential information?), and presents three programming languages for different applications: Excel tables, program testing, and approximate computing. This title is also available as Open Access on Cambridge Core.
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