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Bayesian networks : a practical guide to applications / / edited by Olivier Pourret , Patrick Naim, Bruce Marcot
| Bayesian networks : a practical guide to applications / / edited by Olivier Pourret , Patrick Naim, Bruce Marcot |
| Autore | Pourret Olivier |
| Pubbl/distr/stampa | Chichester, West Sussex, Eng. ; ; Hoboken, NJ, : John Wiley, c2008 |
| Descrizione fisica | 1 online resource (448 p.) |
| Disciplina | 519.5/42 |
| Altri autori (Persone) |
NaimPatrick
MarcotBruce |
| Collana | Statistics in practice |
| Soggetto topico |
Bayesian statistical decision theory
Mathematical models |
| ISBN |
9786612349652
9781282349650 1282349651 9780470994559 047099455X 9780470994542 0470994541 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Bayesian Networks; Contents; Foreword; Preface; 1 Introduction to Bayesian networks; 1.1 Models; 1.2 Probabilistic vs. deterministic models; 1.3 Unconditional and conditional independence; 1.4 Bayesian networks; 2 Medical diagnosis; 2.1 Bayesian networks in medicine; 2.2 Context and history; 2.3 Model construction; 2.4 Inference; 2.5 Model validation; 2.6 Model use; 2.7 Comparison to other approaches; 2.8 Conclusions and perspectives; 3 Clinical decision support; 3.1 Introduction; 3.2 Models and methodology; 3.3 The Busselton network; 3.4 The PROCAM network; 3.5 The PROCAM Busselton network
3.6 Evaluation3.7 The clinical support tool: TakeHeartII; 3.8 Conclusion; 4 Complex genetic models; 4.1 Introduction; 4.2 Historical perspectives; 4.3 Complex traits; 4.4 Bayesian networks to dissect complex traits; 4.5 Applications; 4.6 Future challenges; 5 Crime risk factors analysis; 5.1 Introduction; 5.2 Analysis of the factors affecting crime risk; 5.3 Expert probabilities elicitation; 5.4 Data preprocessing; 5.5 A Bayesian network model; 5.6 Results; 5.7 Accuracy assessment; 5.8 Conclusions; 6 Spatial dynamics in France; 6.1 Introduction; 6.2 An indicator-based analysis 6.3 The Bayesian network model6.4 Conclusions; 7 Inference problems in forensic science; 7.1 Introduction; 7.2 Building Bayesian networks for inference; 7.3 Applications of Bayesian networks in forensic science; 7.4 Conclusions; 8 Conservation of marbled murrelets in British Columbia; 8.1 Context/history; 8.2 Model construction; 8.3 Model calibration, validation and use; 8.4 Conclusions/perspectives; 9 Classifiers for modeling of mineral potential; 9.1 Mineral potential mapping; 9.2 Classifiers for mineral potential mapping; 9.3 Bayesian network mapping of base metal deposit; 9.4 Discussion 9.5 Conclusions10 Student modeling; 10.1 Introduction; 10.2 Probabilistic relational models; 10.3 Probabilistic relational student model; 10.4 Case study; 10.5 Experimental evaluation; 10.6 Conclusions and future directions; 11 Sensor validation; 11.1 Introduction; 11.2 The problem of sensor validation; 11.3 Sensor validation algorithm; 11.4 Gas turbines; 11.5 Models learned and experimentation; 11.6 Discussion and conclusion; 12 An information retrieval system; 12.1 Introduction; 12.2 Overview; 12.3 Bayesian networks and information retrieval; 12.4 Theoretical foundations 12.5 Building the information retrieval system12.6 Conclusion; 13 Reliability analysis of systems; 13.1 Introduction; 13.2 Dynamic fault trees; 13.3 Dynamic Bayesian networks; 13.4 A case study: The Hypothetical Sprinkler System; 13.5 Conclusions; 14 Terrorism risk management; 14.1 Introduction; 14.2 The Risk Influence Network; 14.3 Software implementation; 14.4 Site Profiler deployment; 14.5 Conclusion; 15 Credit-rating of companies; 15.1 Introduction; 15.2 Naive Bayesian classifiers; 15.3 Example of actual credit-ratings systems; 15.4 Credit-rating data of Japanese companies 15.5 Numerical experiments |
| Record Nr. | UNINA-9911019149503321 |
Pourret Olivier
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| Chichester, West Sussex, Eng. ; ; Hoboken, NJ, : John Wiley, c2008 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Risk quantification : management, diagnosis and hedging / / Laurent Condamin, Jean-Paul Louisot, Patrick Naim
| Risk quantification : management, diagnosis and hedging / / Laurent Condamin, Jean-Paul Louisot, Patrick Naim |
| Autore | Condamin Laurent |
| Pubbl/distr/stampa | Chichester, West Sussex, England ; ; Hoboken, NJ, : John Wiley, c2006 |
| Descrizione fisica | 1 online resource (287 p.) |
| Disciplina | 658.15/5 |
| Altri autori (Persone) |
LouisotJean-Paul
NaimPatrick |
| Collana | Wiley finance series |
| Soggetto topico | Risk management - Mathematical models |
| ISBN |
9786610740031
9781119209331 1119209331 9781280740039 1280740035 9780470060438 0470060433 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Risk Quantification; Contents; Foreword; Introduction; 1 Foundations; Risk management: Principles and Practice; Definitions; Systematic and Unsystematic Risk; Insurable Risks; Exposure; Management; Risk Management; Risk Management Objectives; Organizational Objectives; Other Significant Objectives; Risk Management Decision Process; Step 1-Diagnosis of Exposures; Step 2-Risk Treatment; Step 3-Audit and Corrective Actions; State of the Art and the Trends in risk Management; Risk Profile, Risk Map or Risk Matrix; Frequency x Severity; Risk Financing and Strategic Financing
From Risk Management to Strategic Risk ManagementFrom Managing Physical Assets to Managing Reputation; From Risk Manager to Chief Risk Officer; Why is Risk Quantification Needed?; Risk Quantification - A Knowledge-Based Approach; Introduction; Causal Structure of Risk; Building a Quantitative Causal Model of Risk; Exposure, Frequency, and Probability; Exposure, Occurrence, and Impact Drivers; Controlling Exposure, Occurrence, and Impact; Controllable, Predictable, Observable, and Hidden Drivers; Cost of Decisions; Risk Financing; Risk Management Programme as an Influence Diagram Modelling an Individual Risk or the Risk Management ProgrammeSummary; 2 Tool Box; Probability Basics; Introduction to Probability Theory; Conditional Probabilities; Independence; Bayes' Theorem; Random Variables; Moments of a Random Variable; Continuous Random Variables; Main Probability Distributions; Introduction-the Binomial Distribution; Overview of Usual Distributions; Fundamental Theorems of Probability Theory; Empirical Estimation; Estimating Probabilities from Data; Fitting a Distribution from Data; Expert Estimation; From Data to Knowledge Estimating Probabilities from Expert KnowledgeEstimating a Distribution from Expert Knowledge; Identifying the Causal Structure of a Domain; Conclusion; Bayesian Networks and Influence Diagrams; Introduction to the Case; Introduction to Bayesian Networks; Nodes and Variables; Probabilities; Dependencies; Inference; Learning; Extension to Influence Diagrams; Introduction to Monte Carlo Simulation; Introduction; Introductory Example: Structured Funds; Risk Management Example 1 - Hedging Weather Risk; Description; Collecting Information; Model; Manual Scenario; Monte Carlo Simulation; Summary Risk Management Example 2- Potential Earthquake in Cement IndustryAnalysis; Model; Monte Carlo Simulation; Conclusion; A Bit of Theory; Introduction; Definition; Estimation According to Monte Carlo Simulation; Random Variable Generation; Variance Reduction; Software Tools; 3 Quantitative Risk Assessment: A Knowledge Modelling Process; Introduction; Increasing Awareness of Exposures and Stakes; Objectives of Risk Assessment; Issues in Risk Quantification; Risk Quantification: A Knowledge Management Process; The Basel II Framework for Operational Risk; Introduction; The Three Pillars Operational Risk |
| Record Nr. | UNINA-9911018926303321 |
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Condamin Laurent
|
||
| Chichester, West Sussex, England ; ; Hoboken, NJ, : John Wiley, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||