Learning And Modeling With Probabilistic Conditional Logic
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Introduction To Statistical Relational Learning
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Author : Lise Getoor
language : en
Publisher: MIT Press
Release Date : 2007
Introduction To Statistical Relational Learning written by Lise Getoor and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computer algorithms categories.
In 'Introduction to Statistical Relational Learning', leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data.
Hybrid Random Fields
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Author : Antonino Freno
language : en
Publisher: Springer
Release Date : 2013-07-15
Hybrid Random Fields written by Antonino Freno and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-15 with Computers categories.
This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives. -- Manfred Jaeger, Aalborg Universitet The book not only marks an effective direction of investigation with significant experimental advances, but it is also---and perhaps primarily---a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, with full of stimulating connections. [...] Everyone specifically interested in Bayesian networks and Markov random fields should not miss it. -- Marco Gori, Università degli Studi di Siena Graphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they only work well for low-dimensional applications and discrete-valued domains. While guiding the reader through the major achievements of this research area in a technically detailed yet accessible way, the book is concerned with the presentation and thorough (mathematical and experimental) investigation of a novel paradigm for probabilistic graphical modeling, the hybrid random field. This model subsumes and extends both Bayesian networks and Markov random fields. Moreover, it comes with well-defined learning algorithms, both for discrete and continuous-valued domains, which fit the needs of real-world applications involving large-scale, high-dimensional data.
Learning And Modeling With Probabilistic Conditional Logic
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Author : Jens Fisseler
language : en
Publisher: IOS Press
Release Date : 2010
Learning And Modeling With Probabilistic Conditional Logic written by Jens Fisseler and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Artificial intelligence categories.
Conditionals, also called if-then-rules, are a popular concept for knowledge representation. They have intuitive semantics and can also be annotated with probabilities, which, when combined with the principle of maximum entropy, yields a powerful formalism for representing uncertain knowledge. This dissertation discusses several issues pertaining to probabilistic conditionals: learning them from data and using them for modeling. The first part of this thesis presents the implementation of a method for learning probabilistic conditionals from data. In the second part, this learning technique is applied to the problem of fusing data originating from different sources. The third part is the focal point of the thesis. Here, an extension of a propositional probabilistic conditional logic to a first-order probabilistic conditional logic is developed and an approach to reduce the complexity of computing the maximum entropy model of a set of first-order probabilistic conditionals is devised. IOS Press is an international science, technical and medical publisher of high-quality books for academics, scientists, and professionals in all fields. Some of the areas we publish in: -Biomedicine -Oncology -Artificial intelligence -Databases and information systems -Maritime engineering -Nanotechnology -Geoengineering -All aspects of physics -E-governance -E-commerce -The knowledge economy -Urban studies -Arms control -Understanding and responding to terrorism -Medical informatics -Computer Sciences
Probabilistic Graphical Models
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Author : Luis Enrique Sucar
language : en
Publisher: Springer Nature
Release Date : 2020-12-23
Probabilistic Graphical Models written by Luis Enrique Sucar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-23 with Computers categories.
This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference. Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.
Foundations Of Probabilistic Logic Programming
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Author : Fabrizio Riguzzi
language : en
Publisher: River Publishers
Release Date : 2018-09-01
Foundations Of Probabilistic Logic Programming written by Fabrizio Riguzzi and has been published by River Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-01 with Computers categories.
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.
The Probabilistic Foundations Of Rational Learning
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Author : Simon M. Huttegger
language : en
Publisher: Cambridge University Press
Release Date : 2017-10-19
The Probabilistic Foundations Of Rational Learning written by Simon M. Huttegger and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-19 with Education categories.
This book extends Bayesian epistemology to develop new approaches to general rational learning within the framework of probability theory.
Probabilistic Inductive Logic Programming
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Author : Luc De Raedt
language : en
Publisher: Springer
Release Date : 2008-02-26
Probabilistic Inductive Logic Programming written by Luc De Raedt and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-02-26 with Computers categories.
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.
Modelling And Reasoning With Vague Concepts
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Author : Jonathan Lawry
language : en
Publisher: Springer
Release Date : 2006-06-17
Modelling And Reasoning With Vague Concepts written by Jonathan Lawry and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-06-17 with Computers categories.
This volume introduces a formal representation framework for modelling and reasoning, that allows us to quantify the uncertainty inherent in the use of vague descriptions to convey information between intelligent agents. This can then be applied across a range of applications areas in automated reasoning and learning. The utility of the framework is demonstrated by applying it to problems in data analysis where the aim is to infer effective and informative models expressed as logical rules and relations involving vague concept descriptions. The author also introduces a number of learning algorithms within the framework that can be used for both classification and prediction (regression) problems. It is shown how models of this kind can be fused with qualitative background knowledge such as that provided by domain experts. The proposed algorithms will be compared with existing learning methods on a range of benchmark databases such as those from the UCI repository.
Research On Teaching And Learning Probability
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Author : Carmen Batanero
language : en
Publisher: Springer
Release Date : 2016-07-12
Research On Teaching And Learning Probability written by Carmen Batanero and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-12 with Education categories.
This book summarizes the vast amount of research related to teaching and learning probability that has been conducted for more than 50 years in a variety of disciplines. It begins with a synthesis of the most important probability interpretations throughout history: intuitive, classical, frequentist, subjective, logical propensity and axiomatic views. It discusses their possible applications, philosophical problems, as well as their potential and the level of interest they enjoy at different educational levels. Next, the book describes the main features of probabilistic thinking and reasoning, including the contrast to classical logic, probability language features, the role of intuitions, as well as paradoxes and the relevance of modeling. It presents an analysis of the differences between conditioning and causation, the variability expression in data as a sum of random and causal variations, as well as those of probabilistic versus statistical thinking. This is followed by an analysis of probability’s role and main presence in school curricula and an outline of the central expectations in recent curricular guidelines at the primary, secondary and high school level in several countries. This book classifies and discusses in detail the three different research periods on students’ and people’s intuitions and difficulties concerning probability: early research focused on cognitive development, a period of heuristics and biases programs, and the current period marked by a multitude of foci, approaches and theoretical frameworks.
Statistical Relational Artificial Intelligence
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Author : Luc De Raedt
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2016-03-24
Statistical Relational Artificial Intelligence written by Luc De Raedt and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-24 with Computers categories.
An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.