Bayesian Networks A Practical Guide to Applications

摘要: Foreword. Preface. 1 Introduction to Bayesian networks. 1.1 Models. 1.2 Probabilistic vs. deterministic models. 1.3 Unconditional and conditional independence. 1.4 2 Medical diagnosis. 2.1 networks in medicine. 2.2 Context history. 2.3 Model construction. 2.4 Inference. 2.5 validation. 2.6 use. 2.7 Comparison other approaches. 2.8 Conclusions perspectives. 3 Clinical decision support. 3.1 Introduction. 3.2 Models methodology. 3.3 The Busselton network. 3.4 PROCAMnetwork. 3.5 PROCAMBusselton 3.6 Evaluation. 3.7 clinical support tool: TakeHeartII. 3.8 Conclusion. 4 Complex genetic 4.1 4.2 Historical 4.3 traits. 4.4 dissect complex 4.5 Applications. 4.6 Future challenges. 5 Crime risk factors analysis. 5.1 5.2 Analysis of the affecting crime risk. 5.3 Expert probabilities elicitation. 5.4 Data preprocessing. 5.5 A network model. 5.6 Results. 5.7 Accuracy assessment. 5.8 Conclusions. 6 Spatial dynamics coastal region. 6.1 6.2 An indicator-based 6.3 6.4 7 Inference problems forensic science. 7.1 7.2 Building for inference. 7.3 Applications 7.4 8 Conservation marbled murrelets British Columbia. 8.1 Context/history. 8.2 8.3 calibration, validation 8.4 Conclusions/perspectives. 9 Classifiers modeling mineral potential. 9.1 Mineral potential mapping. 9.2 9.3 mapping base metal deposit. 9.4 Discussion. 9.5 10 Student modeling. 10.1 10.2 relational 10.3 student 10.4 Case study. 10.5 Experimental evaluation. 10.6 future directions. 11 Sensor 11.1 11.2 problem sensor 11.3 algorithm. 11.4 Gas turbines. 11.5 learned experimentation. 11.6 Discussion conclusion. 12 information retrieval system. 12.1 12.2 Overview. 12.3 retrieval. 12.4 Theoretical foundations. 12.5 12.6 13 Reliability analysis systems. 13.1 13.2 Dynamic fault trees. 13.3 13.4 case study: Hypothetical Sprinkler System. 13.5 14 Terrorism management. 14.1 14.2 Risk Influence Network. 14.3 Software implementation. 14.4 Site Profiler deployment. 14.5 15 Credit-rating companies. 15.1 15.2 Naive classifiers. 15.3 Example actual credit-ratings 15.4 data Japanese 15.5 Numerical experiments. 15.6 Performance comparison 15.7 16 Classification Chilean wines. 16.1 16.2 setup. 16.3 Feature extraction methods. 16.4 results. 16.5 17 Pavement bridge 17.1 17.2 management decisions. 17.3 Bridge 17.4 approach embankment - 17.5 18 industrial process operation. 18.1 18.2 methodology Root Cause Analysis. 18.3 Pulp paper application. 18.4 ABB Industrial IT platform. 18.5 19 Probability default large corporates. 19.1 19.2 19.3 BayesCredit. 19.4 benchmarking. 19.5 Benefits from technology software. 19.6 20 robotics. 20.1 20.2 DeepC. 20.3 ADVOCATE II architecture. 20.4 development. 20.5 usage examples. 20.6 using probabilistic graphical 20.7 21 Enhancing Human Cognition. 21.1 21.2 foreknowledge everyday settings. 21.3 Machine foreknowledge. 21.4 Current application research needs. 21.5 22 22.1 artificial intelligence perspective. 22.2 rational knowledge. 22.3 Bibliography. Index.