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Modelling Operational Risk Using Bayesian Inference

risk and introduced corresponding capital requirements. Many banks are undertaking quantitative modelling of operational risk using the Loss;

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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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An Introduction to Bayesian Inference, Methods and Computation

These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy;

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Statistics for Astrophysics

This book contains statistics courses on basic to advanced methods with practical exercises using the R environment, by leading experts;

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Modeling, Measuring And Hedging Operational Risk

Worldwide banks are keen to find ways of effectively measuring and managing operational risk , yet many find themselves poorly equipped to;

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Operational Risk Modeling in Financial Services

Transform your approach to oprisk modelling with a proven, non-statistical methodology Operational Risk Modeling in Financial Services;

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Bayesian Methods For Hackers

introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power;

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Bayesian Methods for Nonlinear Classification and Regression

methods. * Focuses on the problems of classification and regression using flexible, data-driven approaches.* Demonstrates how Bayesian ideas can;

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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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Case Studies in Bayesian Statistical Modelling and Analysis

Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian;

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Bayesian Model Selection and Statistical Modeling

Ando shows how to implement a variety of Bayesian inference using R and sampling methods, such as Markov chain Monte Carlo. He covers the;

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Bayesian Analysis of Stochastic Process Models

Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified;

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Bayesian Modeling Using WinBUGS

A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible;

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Introduction to Bayesian Statistics

inference using independent Jeffreys priors and join conjugate prior * The cutting-edge topic of computational Bayesian Statistics in a new chapter;

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An Introduction to Bayesian Inference in Econometrics

This is a classical reprint edition of the original 1971 edition of An Introduction to Bayesian Inference in Economics. This historical;

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Bayesian Thinking, Modeling and Computation

summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety;

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Statistical Inference

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian;

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Statistical Inference

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian;

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An Introduction to Bayesian Inference and Decision

An Introduction to Bayesian Inference and Decision is een boek van Robert L Winkler;

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Mastering Operational Risk

Operational risk is a constant concern for all businesses. It goes far beyond operations and process to encompass all aspects of business;

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Bayesian Model Selection and Statistical Modeling

, and provides R code for implementation. The author shows how to implement a variety of Bayesian inference using R and sampling methods;

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Modeling and Reasoning with Bayesian Networks

This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive;

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Modeling and Reasoning with Bayesian Networks

This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive;

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The Advanced Measurement Approach to Operational Risk

This is a technical guide on how to model operational risk, with practical advice on how to set up an AMA programme that supports the;

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Statistics of Extremes

of models and application areas, including risk and insurance: a major area of interest and relevance to extreme value theory. Case studies are;

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Likelihood & Bayesian Inference

likelihood ratio and the Wald statistic. In the second part of the book, likelihood is combined with prior information to perform Bayesian inference;

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Likelihood and Bayesian Inference

likelihood ratio and the Wald statistic. In the second part of the book, likelihood is combined with prior information to perform Bayesian inference;

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