| Basic Probability | p. 1 |
| Sample Space, Events, and Probabilities | p. 4 |
| Independence | p. 8 |
| Counting variables | p. 10 |
| Conditional Probabilities and the Law of Total Probability | p. 12 |
| Event-tree Analysis | p. 15 |
| Probabilities in Risk Analysis | p. 21 |
| Bayes' Formula | p. 22 |
| Odds and Subjective Probabilities | p. 23 |
| Recursive Updating of Odds | p. 27 |
| Probabilities as Long-term Frequencies | p. 30 |
| Streams of Events | p. 33 |
| Intensities of Streams | p. 37 |
| Poisson streams of events | p. 40 |
| Non-stationary streams | p. 43 |
| Distributions and Random Variables | p. 49 |
| Random Numbers | p. 51 |
| Uniformly distributed random numbers | p. 51 |
| Non-uniformly distributed random numbers | p. 52 |
| Examples of random numbers | p. 54 |
| Some Properties of Distribution Functions | p. 55 |
| Scale and Location Parameters - Standard Distributions | p. 59 |
| Some classes of distributions | p. 60 |
| Independent Random Variables | p. 62 |
| Averages - Law of Large Numbers | p. 63 |
| Expectations of functions of random variables | p. 65 |
| Fitting Distributions to Data - Classical Inference | p. 69 |
| Estimates of F[subscript x] | p. 72 |
| Choosing a Model for F[subscript x] | p. 74 |
| A graphical method: probability paper | p. 75 |
| Introduction to [chi superscript 2]-method for goodness-of-fit tests | p. 77 |
| Maximum Likelihood Estimates | p. 80 |
| Introductory example | p. 80 |
| Derivation of ML estimates for some common models | p. 82 |
| Analysis of Estimation Error | p. 85 |
| Mean and variance of the estimation error [epsilon] | p. 86 |
| Distribution of error, large number of observations | p. 89 |
| Confidence Intervals | p. 92 |
| Introduction. Calculation of bounds | p. 92 |
| Asymptotic intervals | p. 94 |
| Bootstrap confidence intervals | p. 95 |
| Examples | p. 95 |
| Uncertainties of Quantiles | p. 98 |
| Asymptotic normality | p. 98 |
| Statistical bootstrap | p. 100 |
| Conditional Distributions with Applications | p. 105 |
| Dependent Observations | p. 105 |
| Some Properties of Two-dimensional Distributions | p. 107 |
| Covariance and correlation | p. 113 |
| Conditional Distributions and Densities | p. 115 |
| Discrete random variables | p. 115 |
| Continuous random variables | p. 116 |
| Application of Conditional Probabilities | p. 117 |
| Law of total probability | p. 117 |
| Bayes' formula | p. 118 |
| Example: Reliability of a system | p. 119 |
| Introduction to Bayesian Inference | p. 125 |
| Introductory Examples | p. 126 |
| Compromising Between Data and Prior Knowledge | p. 130 |
| Bayesian credibility intervals | p. 132 |
| Bayesian Inference | p. 132 |
| Choice of a model for the data - conditional independence | p. 133 |
| Bayesian updating and likelihood functions | p. 134 |
| Conjugated Priors | p. 135 |
| Unknown probability | p. 137 |
| Probabilities for multiple scenarios | p. 139 |
| Priors for intensity of a stream A | p. 141 |
| Remarks on Choice of Priors | p. 143 |
| Nothing is known about the parameter [theta] | p. 143 |
| Moments of [Theta] are known | p. 144 |
| Large number of observations: Likelihood dominates prior density | p. 147 |
| Predicting Frequency of Rare Accidents | p. 151 |
| Intensities and Poisson Models | p. 157 |
| Time to the First Accident - Failure Intensity | p. 157 |
| Failure intensity | p. 157 |
| Estimation procedures | p. 162 |
| Absolute Risks | p. 166 |
| Poisson Models for Counts | p. 170 |
| Test for Poisson distribution - constant mean | p. 171 |
| Test for constant mean - Poisson variables | p. 173 |
| Formulation of Poisson regression model | p. 174 |
| ML estimates of [Beta subscript 0],...,[Beta subscript p] | p. 180 |
| The Poisson Point process | p. 182 |
| More General Poisson Processes | p. 185 |
| Decomposition and Superposition of Poisson Processes | p. 187 |
| Failure Probabilities and Safety Indexes | p. 193 |
| Functions Often Met in Applications | p. 194 |
| Linear function | p. 194 |
| Often used non-linear function | p. 198 |
| Minimum of variables | p. 201 |
| Safety Index | p. 202 |
| Cornell's index | p. 202 |
| Hasofer-Lind index | p. 204 |
| Use of safety indexes in risk analysis | p. 204 |
| Return periods and safety index | p. 205 |
| Computation of Cornell's index | p. 206 |
| Gauss' Approximations | p. 207 |
| The delta method | p. 209 |
| Estimation of Quantiles | p. 217 |
| Analysis of Characteristic Strength | p. 217 |
| Parametric modelling | p. 218 |
| The Peaks Over Threshold (POT) Method | p. 220 |
| The POT method and estimation of [kappav][alpha] quantiles | p. 222 |
| Example: Strength of glass fibres | p. 223 |
| Example: Accidents in mines | p. 224 |
| Quality of Components | p. 226 |
| Binomial distribution | p. 227 |
| Bayesian approach | p. 228 |
| Design Loads and Extreme Values | p. 231 |
| Safety Factors, Design Loads, Characteristic Strength | p. 232 |
| Extreme Values | p. 233 |
| Extreme-value distributions | p. 234 |
| Fitting a model to data: An example | p. 240 |
| Finding the 100-year Load: Method of Yearly Maxima | p. 241 |
| Uncertainty analysis of S[subscript T]: Gumbel case | p. 242 |
| Uncertainty analysis of S[subscript T]: GEV case | p. 244 |
| Warning example of model error | p. 245 |
| Discussion on uncertainty in design-load estimates | p. 247 |
| Some Useful Tables | p. 251 |
| Short Solutions to Problems | p. 257 |
| References | p. 275 |
| Index | p. 279 |
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