Foundations Of Probabilistic Logic Programming
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Foundations of Probabilistic Programming
Author | : Gilles Barthe,Joost-Pieter Katoen,Alexandra Silva |
Publsiher | : Cambridge University Press |
Total Pages | : 583 |
Release | : 2020-12-03 |
Genre | : Computers |
ISBN | : 9781108488518 |
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This book provides an overview of the theoretical underpinnings of modern probabilistic programming and presents applications in e.g., machine learning, security, and approximate computing. Comprehensive survey chapters make the material accessible to graduate students and non-experts. This title is also available as Open Access on Cambridge Core.
Foundations of Probabilistic Logic Programming
Author | : Fabrizio Riguzzi |
Publsiher | : CRC Press |
Total Pages | : 422 |
Release | : 2022-09-01 |
Genre | : Computers |
ISBN | : 9781000795875 |
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Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertain information by means of probability theory. Probabilistic Logic Programming is at the intersection of two wider research fields: the integration of logic and probability and Probabilistic Programming.Logic enables the representation of complex relations among entities while probability theory is useful for model uncertainty over attributes and relations. Combining the two is a very active field of study.Probabilistic Programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for the inference and learning tasks are then provided automatically by the system.Probabilistic Logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds.Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. Foundations of Probabilistic Logic Programming aims at providing an overview of the field with a special emphasis on languages under the Distribution Semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods.Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online.
Foundations of Probabilistic Logic Programming
Author | : Fabrizio Riguzzi |
Publsiher | : River Publishers |
Total Pages | : 422 |
Release | : 2018-09-01 |
Genre | : Computers |
ISBN | : 9788770220187 |
Download Foundations of Probabilistic Logic Programming Book in PDF, Epub and Kindle
Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertain information. Probabilistic Logic Programming is at the intersection of two wider research fields: the integration of logic and probability and Probabilistic Programming. Logic enables the representation of complex relations among entities while probability theory is useful for model uncertainty over attributes and relations. Combining the two is a very active field of study. Probabilistic Programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for the inference and learning tasks are then provided automatically by the system. Probabilistic Logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds. Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. Foundations of Probabilistic Logic Programming aims at providing an overview of the field with a special emphasis on languages under the Distribution Semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online.
Probabilistic Inductive Logic Programming
Author | : Luc De Raedt,Paolo Frasconi,Kristian Kersting,Stephen H. Muggleton |
Publsiher | : Springer |
Total Pages | : 341 |
Release | : 2008-02-26 |
Genre | : Computers |
ISBN | : 9783540786528 |
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This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory.
Abstraction Refinement and Proof for Probabilistic Systems
Author | : Annabelle McIver,Carroll Morgan |
Publsiher | : Springer Science & Business Media |
Total Pages | : 412 |
Release | : 2005 |
Genre | : Computers |
ISBN | : 0387401156 |
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Provides an integrated coverage of random/probabilistic algorithms, assertion-based program reasoning, and refinement programming models, providing a focused survey on probabilistic program semantics. This book illustrates, by examples, the typical steps necessary to build a mathematical model of any programming paradigm.
Logical Foundations of Probability
Author | : Rudolf Carnap |
Publsiher | : Unknown |
Total Pages | : 0 |
Release | : 1962 |
Genre | : Electronic Book |
ISBN | : OCLC:1067867022 |
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Practical Foundations for Programming Languages
Author | : Robert Harper |
Publsiher | : Cambridge University Press |
Total Pages | : 513 |
Release | : 2016-04-04 |
Genre | : Computers |
ISBN | : 9781107150300 |
Download Practical Foundations for Programming Languages Book in PDF, Epub and Kindle
This book unifies a broad range of programming language concepts under the framework of type systems and structural operational semantics.
Foundations of Data Science
Author | : Avrim Blum,John Hopcroft,Ravindran Kannan |
Publsiher | : Cambridge University Press |
Total Pages | : 433 |
Release | : 2020-01-23 |
Genre | : Computers |
ISBN | : 9781108485067 |
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Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.