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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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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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Oil and Gas Processing Equipment

, the application of Bayesian Networks (BNs) to risk assessment offers a different probabilistic version of causal reasoning. Introducing;

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Bayesian Networks for Reliability Engineering

This book presents a bibliographical review of the use of Bayesian networks in reliability over the last decade. Bayesian network (BN;

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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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Bayesian Analysis with Python

-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models. The main concepts of Bayesian;

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Probabilistic Graphical Models

with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides;

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Probabilistic Graphical Models

with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides;

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Bayesian and grAphical Models for Biomedical Imaging

This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging;

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Probabilistic Databases

integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database;

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Handbook of Probabilistic Models

that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil;

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Bayesian Methods For Hackers

. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely;

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Probabilistic Risk and Hazard Assessment: Proceedings of the Conference, Newcastle, Nsw, Australia, 22-23 September 1993

Highlights the multi-disciplinary nature of probabilistic risk and hazard assessment procedures. Topics covered include: Hazard scenario;

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Bayesian Inference and Maximum Entropy Methods in Science and Engineering

relevant foundations, the scientific community can avoid applying Bayesian methods on a merely ad hoc basis. For over 35 years, the MaxEnt;

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Probabilistic Databases

integration, and financial risk assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database;

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An Introduction to Bayesian Inference in Econometrics

volume is an early introduction to Bayesian inference and methodology which still has lasting value for today's statistician and student. The;

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Statistical Inference

alternatives, based on Bayesian analyses, to current frequentist nonparametric methods. The text concludes with new goodness-of-fit methods for;

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Statistical Inference

alternatives, based on Bayesian analyses, to current frequentist nonparametric methods. The text concludes with new goodness-of-fit methods for;

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Bayesian Disease Mapping

Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the;

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Bayesian Disease Mapping

Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the;

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Extreme Value Modeling and Risk Analysis

critical decision and risk assessment/management applications to stimulate more collaboration between these statisticians and specialists.;

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Bayesian Modeling and Computation in Python

methods for Exploratory Analysis of Bayesian Models. With an understanding of these two fundamentals the subsequent chapters talk through various;

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Probabilistic Methods for Bioinformatics

> Rather than getting bogged down in proofs and algorithms, probabilistic methods used for biological information and Bayesian networks are;

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Learning Probabilistic Graphical Models in R

* Predict and use a probabilistic graphical models (PGM) as an expert system * Comprehend how your computer can learn Bayesian modeling to solve;

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Practical Bayesian Inference

concepts of probability and statistics, and the computational tools for analysing and interpreting data. It describes the Bayesian approach, and;

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Practical Bayesian Inference

concepts of probability and statistics, and the computational tools for analysing and interpreting data. It describes the Bayesian approach, and;

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