This book is a collective work made by various authors well-recognised owing to their appreciable contributions to the theory and applications of probability. Both mathematical and engineering aspects of probability outline its framework. Readers can find here several timely topics such as risk theory and applications, Laplace distributions which describe the heavy-tailed noise, Poisson sums having applications in business and engineering, Markov chains investigations and approximations, Berstein-Hoeffding-type exponential inequalities useful for proving limiting theorems, Bayesian computational methods, as well as a modern view on sampling and reconstruction of Gaussian and non-Gaussian random processes.
Preface; Risk Theory in Actuarial Science; Multivariate Models Connected with Poisson Sums & Maxima of Exponentials; Laplace Probability Distributions & Related Stochastic Processes; Markov Chain Approximation for Stochastic Control & Games; A Random Time Horizon Optimal Stopping Problem; Dendrogram Representation of Stochastic Clustering; Berstein-Hoeffding Type Exponential Inequalities & Strong Laws of Large Numbers; Bayesian Computational Methods for Meta-Analysis of Gene Expression Studies; Sampling: Reconstruction Procedures of Gaussian Process Realizations; Sampling: Reconstruction Procedures of Non-Gaussian Process Realizations; Statistical Inference for Elliptically Contoured Distributions; Index.