Bayesian Networks A Practical Guide to Applications 1st Edition by Olivier Pourret, Patrick Naïm, Bruce Marcot 0470060301 9780470060308

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ISBN 10: 0470060301 

ISBN 13: 9780470060308

Author: Olivier Pourret, Patrick Naïm, Bruce Marcot

Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book provides a general introduction to Bayesian networks, defining and illustrating the basic concepts with pedagogical examples and twenty real-life case studies drawn from a range of fields including medicine, computing, natural sciences and engineering. Designed to help analysts, engineers, scientists and professionals taking part in complex decision processes to successfully implement Bayesian networks, this book equips readers with proven methods to generate, calibrate, evaluate and validate Bayesian networks. The book: Provides the tools to overcome common practical challenges such as the treatment of missing input data, interaction with experts and decision makers, determination of the optimal granularity and size of the model.  Highlights the strengths of Bayesian networks whilst also presenting a discussion of their limitations. Compares Bayesian networks with other modelling techniques such as neural networks, fuzzy logic and fault trees. Describes, for ease of comparison, the main features of the major Bayesian network software packages: Netica, Hugin, Elvira and Discoverer, from the point of view of the user. Offers a historical perspective on the subject and analyses future directions for research. Written by leading experts with practical experience of applying Bayesian networks in finance, banking, medicine, robotics, civil engineering, geology, geography, genetics, forensic science, ecology, and industry, the book has much to offer both practitioners and researchers involved in statistical analysis or modelling in any of these fields.

Table of contents:

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 Evaluation
3.7 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 model
6.4 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 Conclusions

10 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 system
12.6 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
15.6 Performance comparison of classifiers
15.7 Conclusion

16 Classification of Chilean wines
16.1 Introduction
16.2 Experimental setup
16.3 Feature extraction methods
16.4 Classification results
16.5 Conclusions

17 Pavement and bridge management
17.1 Introduction
17.2 Pavement management decisions
17.3 Bridge management
17.4 Bridge approach embankment – case study
17.5 Conclusion

18 Complex industrial process operation
18.1 Introduction
18.2 A methodology for Root Cause Analysis
18.3 Pulp and paper application
18.4 The ABB Industrial IT platform
18.5 Conclusion

19 Probability of default for large corporates
19.1 Introduction
19.2 Model construction
19.3 BayesCredit
19.4 Model benchmarking
19.5 Benefits from technology and software
19.6 Conclusion

20 Risk management in robotics
20.1 Introduction
20.2 DeepC
20.3 The ADVOCATE II architecture
20.4 Model development
20.5 Model usage and examples
20.6 Benefits from using probabilistic graphical models
20.7 Conclusion

21 Enhancing Human Cognition
21.1 Introduction
21.2 Human foreknowledge in everyday settings
21.3 Machine foreknowledge
21.4 Current application and future research needs
21.5 Conclusion

22 Conclusion
22.1 An artificial intelligence perspective
22.2 A rational approach of knowledge
22.3 Future challenges

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Tags: Olivier Pourret, Patrick Naïm, Bruce Marcot, Bayesian