Stochastic Processes: Theory and Applications
The aim of this special issue is to publish original research papers that cover recent advances in the theory and application of stochastic processes. There is especial focus on applications of stochastic processes as models of dynamic phenomena in various research areas, such as queuing theory, phy...
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| Year of Publication: | 2019 |
| Language: | English |
| Physical Description: | 1 electronic resource (216 p.) |
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| 245 | 1 | 0 | |a Stochastic Processes: Theory and Applications |
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| 520 | |a The aim of this special issue is to publish original research papers that cover recent advances in the theory and application of stochastic processes. There is especial focus on applications of stochastic processes as models of dynamic phenomena in various research areas, such as queuing theory, physics, biology, economics, medicine, reliability theory, and financial mathematics. Potential topics include, but are not limited to: Markov chains and processes; large deviations and limit theorems; random motions; stochastic biological model; reliability, availability, maintenance, inspection; queueing models; queueing network models; computational methods for stochastic models; applications to risk theory, insurance and mathematical finance. | ||
| 546 | |a English | ||
| 653 | |a recursive formula | ||
| 653 | |a rate of convergence | ||
| 653 | |a asymptotic approximation | ||
| 653 | |a parabolic equation | ||
| 653 | |a processor heating and cooling | ||
| 653 | |a compound poisson insurance risk model | ||
| 653 | |a Koksma-Hlawka inequality | ||
| 653 | |a phase-type service time distribution | ||
| 653 | |a discrete-time Geo/D/1 queue | ||
| 653 | |a lower record values | ||
| 653 | |a Fourier-cosine series | ||
| 653 | |a retrials | ||
| 653 | |a state-dependent marked Markovian arrival process | ||
| 653 | |a queuing network | ||
| 653 | |a stochastic processes | ||
| 653 | |a Laplace transform | ||
| 653 | |a von-Neumann–Ulam scheme | ||
| 653 | |a Monte Carlo method | ||
| 653 | |a Lévy process | ||
| 653 | |a Wiener–Poisson risk model | ||
| 653 | |a queueing systems | ||
| 653 | |a quasi-random sequences | ||
| 653 | |a closed-form solution | ||
| 653 | |a Cauchy problem | ||
| 653 | |a product form | ||
| 653 | |a estimation | ||
| 653 | |a extreme order statistics | ||
| 653 | |a guaranteed minimum death benefit | ||
| 653 | |a valuation | ||
| 653 | |a multidimensional birth-death process | ||
| 653 | |a Markovian queueing models | ||
| 653 | |a survival probability | ||
| 653 | |a truncated distribution | ||
| 653 | |a Markovian arrival process | ||
| 653 | |a inhomogeneous continuous-time Markov chain | ||
| 653 | |a measure of information | ||
| 653 | |a option | ||
| 653 | |a unbiased estimator | ||
| 653 | |a matrix-geometric solution | ||
| 653 | |a Dickson–Hipp operator | ||
| 653 | |a Fourier transform | ||
| 653 | |a multi-class arrival processes | ||
| 653 | |a total precipitation volume | ||
| 653 | |a one dimensional projection | ||
| 653 | |a random sample size | ||
| 653 | |a markovian arrival process | ||
| 653 | |a cumulative inaccuracy | ||
| 653 | |a mutual information | ||
| 653 | |a Quasi-Birth-and-Death process | ||
| 653 | |a limiting characteristics | ||
| 653 | |a testing statistical hypotheses | ||
| 653 | |a wet periods | ||
| 653 | |a compound Poisson risk model | ||
| 653 | |a time-dependent queue-length probability | ||
| 653 | |a non-stationary | ||
| 653 | |a equity-linked death benefits | ||
| 653 | |a wireless telecommunication networks | ||
| 653 | |a Fourier cosine series expansion | ||
| 653 | |a impatience | ||
| 653 | |a generalized Gerber–Shiu discounted penalty function | ||
| 653 | |a quasi-Monte Carlo method | ||
| 653 | |a expected discounted penalty function | ||
| 653 | |a Nonparametric threshold estimation | ||
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