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Probabilistic Inductive Logic Programming

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Cover of 'Probabilistic Inductive Logic Programming'

Table of Contents

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    Book Overview
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    Chapter 1 Probabilistic Inductive Logic Programming
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    Chapter 2 Relational Sequence Learning
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    Chapter 3 Learning with Kernels and Logical Representations
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    Chapter 4 Markov Logic
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    Chapter 5 New Advances in Logic-Based Probabilistic Modeling by PRISM
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    Chapter 6 CLP( $\cal{BN}$ ): Constraint Logic Programming for Probabilistic Knowledge
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    Chapter 7 Basic Principles of Learning Bayesian Logic Programs
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    Chapter 8 The Independent Choice Logic and Beyond
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    Chapter 9 Protein Fold Discovery Using Stochastic Logic Programs
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    Chapter 10 Probabilistic Logic Learning from Haplotype Data
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    Chapter 11 Model Revision from Temporal Logic Properties in Computational Systems Biology
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    Chapter 12 A Behavioral Comparison of Some Probabilistic Logic Models
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    Chapter 13 Model-Theoretic Expressivity Analysis
Attention for Chapter 6: CLP( $\cal{BN}$ ): Constraint Logic Programming for Probabilistic Knowledge
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