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Practical Probabilistic Programming

trends, computer system failures, experimental outcomes, and other critical concerns. Practical Probabilistic Programming explains;

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Foundations of Probabilistic Logic Programming

thus represent an excellent candidate for the integration. Since its birth, the field of Probabilistic Logic Programming has seen a steady;

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Foundations of Probabilistic Programming

What does a probabilistic program actually compute? How can one formally reason about such probabilistic programs? This valuable guide;

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Redundancy in Semidefinite Programming

inequality constraints. It discusses both probabilistic and deterministic methods. The first probabilistic method, called the semidefinite-stand-hit;

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Knowledge Representation Reasoning & The

-intensive software systems capable of performing intelligent tasks. Using logical and probabilistic formalisms based on answer set programming (ASP;

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Bayesian Programming

probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications;

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Bayesian Programming

probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications;

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Stochastic Optimization

transportation. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications;

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Bayesian Analysis with Python

data analyses using PyMC3 and ArviZ A modern, practical and computational approach to Bayesian statistical modeling A tutorial for Bayesian;

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Programming Languages and Systems

, synthesis and transformation; program analysis, verification, and model-checking; logic, constraint, probabilistic and quantum programming; software;

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Engineering Design Under Uncertainty and Health Prognostics

seeks to facilitate practical application of probabilistic analysis and design by providing an authoritative, in-depth, and practical;

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Inductive Logic Programming

. The papers cover the following topics: propositionalization, logical foundations, implementations, probabilistic ILP, applications in robotics;

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Programming Languages and Systems

design; probabilistic programming; types and effects; concurrency; security; program verification; program analysis and automated verification;

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Probabilistic Graphical Models

This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective;

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Inductive Logic Programming

; logical foundations; statistical relational learning; probabilistic ILP; implementation and scalability; applications in robotics, cyber security;

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Probabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems

Probabilistic Power System Expansion Planning with Renewable Energy Resources and Energy Storage Systems Discover how modern techniques;

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Handbook of Probabilistic Models

approach to probabilistic models and their applications, thus solving a wide range of practical problems;

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Probabilistic Design for Optimization and Robustness for Engineers

Probabilistic Design for Optimization and Robustness: * Presents the theory of modeling with variation using physical models and methods;

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Optimisation in Signal and Image Processing

approach; wavelets and fractals; information criteria; training and quadratic programming; Bayesian formalism; probabilistic modeling; Markovian;

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Crowdsourcing and Probabilistic Decision-Making in Software Engineering

Probabilistic Decision-Making in Software Engineering: Emerging Research and Opportunities is a pivotal reference source that provides vital research on;

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Bayesian Inference for Probabilistic Risk Assessment

Bayesian Inference for Probabilistic Risk Assessment provides a Bayesian foundation for framing probabilistic problems and performing;

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Data Structures and Algorithms Using C#

.NET Framework library, as well as those developed by the programmer. The approach is very practical, using timing tests rather than Big O;

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Design and Analysis of Algorithms

of various algorithms in a step by step approach followed by their pseudo-codes, to build an understanding of their practical applications. After an;

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Data Structures and Algorithms Using C#

.NET Framework library, as well as those developed by the programmer. The approach is very practical, using timing tests rather than Big O;

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LogiQL

, analytical, graph, probabilistic, and mathematical programming. LogiQL makes it possible to build hybrid applications that previously required;

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LogiQL

, analytical, graph, probabilistic, and mathematical programming. LogiQL makes it possible to build hybrid applications that previously required;

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Bayesian Analysis with Python

* Understand the essentials Bayesian concepts from a practical point of view * Learn how to build probabilistic models using the Python library PyMC3;

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