Chapter
2.4 Savage's theory of rational decision
2.5 Measurement of subjective probabilities
2.6 Different types of uncertainty
2.7 Uncertainty about probabilities
3.1 Review of elementary probability theory
3.3 The exponential life distribution
3.4 The Poisson distribution
3.5 The gamma distribution
3.6 The beta distribution
3.7 The lognormal distribution
3.9 Approximating distributions
4.3 Classical statistical inference
5.2 Graphical methods for parameter fitting
5.3 Maximum likelihood methods for parameter estimation
System analysis and quantification
6.1 Fault and event trees
6.2 The aim of a fault-tree analysis
6.3 The definition of a system and of a top event
6.4 What classes of faults can occur?
6.5 Symbols for fault trees
6.6 Fault tree construction
6.8 Minimal path and cut sets for coherent systems
6.9 Set theoretic description of cut and path sets
6.10 Estimating the probability of the top event
7.1 The MOCUS algorithm for finding minimal cut sets
7.2 Binary decision diagrams and new algorithms
8.2 Component failure data versus incident reporting
8.4 Inter-system dependencies
8.5 Inter-component dependencies – common cause failure
8.6 The square root bounding model
8.7 The Marshall–Olkin model
8.8 The beta-factor model
8.9 The binomial failure rate model
9.2 Maintenance and failure taxonomies
9.4 Data analysis without competing risks
9.5 Competing risk concepts and methods
9.6 Competing risk models
9.8 Examples of dependent competing risk models
10.2 Generic issues in the use of expert opinion
10.3 Bayesian combinations of expert assessments
10.4 Non-Bayesian combinations of expert distributions
10.5 Linear opinion pools
10.6 Performance based weighting – the classical model
10.7 Case study – uncertainty in dispersion modeling
11.2 Generic aspects of a human reliability analysis
11.3 THERP – technique for human error rate prediction
11.4 The Success Likelihood Index Methodology
11.5 Time reliability correlations
11.6 Absolute Probability Judgement
12.1 Qualitative assessment – ways to find errors
12.2 Software quality assurance
12.3 Software reliability prediction
12.4 Calibration and weighting
Uncertainty modeling and risk measurement
13.1 Preferences over actions
13.2 Decision tree example
13.3 The value of information
13.5 Multi-attribute decision theory and value models
13.6 Other popular models
Influence diagrams and belief nets
14.2 Conditional independence
14.3 Directed acyclic graphs
14.4 Construction of influence diagrams
14.5 Operations on influence diagrams
14.6 Evaluation of influence diagrams
14.7 The relation with decision trees
14.8 An example of a Bayesian net application
15.1 Risk management methods
15.2 The Critical Path Method (CPM)
15.3 Expert judgement for quantifying uncertainties
15.4 Building in correlations
15.5 Simulation of completion times
Probabilistic inversion techniques for uncertainty analysis
16.1 Elicitation variables and target variables
16.2 Mathematical formulation of probabilistic inversion
16.4 Infeasibility problems and PARFUM
17.2 Monte Carlo simulation
17.3 Examples: uncertainty analysis for system failure
17.5 Appendix: bivariate minimally informative distributions
Risk measurement and regulation
18.1 Single statistics representing risk
18.2 Frequency vs consequence lines
18.4 Perceiving and accepting risks
18.5 Beyond risk regulation: compensation, trading and ethics
Probabilistic Risk Analysis
：Foundations and Methods
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