Stochastic Processes: Modeling and Simulation, 21. sējumsD N Shanbhag, Calyampudi Radhakrishna Rao Gulf Professional Publishing, 2003. gada 24. febr. - 1000 lappuses This sequel to volume 19 of Handbook on Statistics on Stochastic Processes: Modelling and Simulation is concerned mainly with the theme of reviewing and, in some cases, unifying with new ideas the different lines of research and developments in stochastic processes of applied flavour. This volume consists of 23 chapters addressing various topics in stochastic processes. These include, among others, those on manufacturing systems, random graphs, reliability, epidemic modelling, self-similar processes, empirical processes, time series models, extreme value therapy, applications of Markov chains, modelling with Monte Carlo techniques, and stochastic processes in subjects such as engineering, telecommunications, biology, astronomy and chemistry. particular with modelling, simulation techniques and numerical methods concerned with stochastic processes. The scope of the project involving this volume as well as volume 19 is already clarified in the preface of volume 19. The present volume completes the aim of the project and should serve as an aid to students, teachers, researchers and practitioners interested in applied stochastic processes. |
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Saturs
Modelling and Numerical Methods in Manufacturing System Using Control | 1 |
W Wefelmeyer Fachbereich 6 Mathematik Universität Siegen WalterFlexStr | 3 |
N Gautam Department of Industrial and Manufacturing Engineering The Pennsyl | 7 |
S Z Li Microsoft Research China 1 Beijing Sigma Center Beijing 100080 China | 13 |
Continuous flow model for production control | 15 |
Ch | 16 |
G F Yeo Mathematics and Statistics DSE Murdoch University Murdoch 6150 | 23 |
Maintenance model without considering the machine aging | 32 |
Applications of Markov Chains to the Distribution Theory of Runs | 431 |
Markov Chain imbeddable variables of binomial type | 443 |
Multivariate MVB distributions | 454 |
Multivariate success runs distributions | 457 |
Alternative methods for exact distribution evaluation | 466 |
Modelling Image Analysis Problems Using Markov Random Fields | 473 |
Markov random fields and Gibbs distributions | 480 |
Useful MRF models | 489 |
Conclusion | 46 |
Overview of the ErdősRényi model | 57 |
Applications of the ErdősRényi model | 63 |
Random cluster models | 69 |
Other models of random graphs | 78 |
Locally SelfSimilar Processes and their Wavelet Analysis | 93 |
Locally selfsimilar processes | 95 |
Generalized fractional Brownian motion | 97 |
Estimating the scaling function | 102 |
Implementation of the estimation procedure | 104 |
Simulations | 106 |
Applications | 117 |
Conclusion | 124 |
References | 133 |
Stochastic Models for DNA Replication | 137 |
Stochastic chemistry | 138 |
Exponentially distributed waiting times | 141 |
The biological cell | 142 |
A glib mathematical abstraction | 143 |
The spatial pattern of replication origins | 144 |
The time to separation of a long DNA molecule | 146 |
The proportion of origins initiated | 150 |
Mean eye lengths and eyetoeye distances | 151 |
What is happening inside the eyes? | 153 |
The CowanChiu model of fragment formation | 155 |
the quasirenewal equation | 157 |
Relationship between fragment length and primersite spacing | 159 |
Estimated spacing between primer sites | 160 |
Notations for the competing theory | 161 |
Analysis of the lagging strand for Model B | 162 |
Analysis of the leading strand for Model B | 163 |
Concluding remarks | 164 |
References | 165 |
An Empirical Process with Applications to Testing the Exponential and Geometric Models | 167 |
The empirical integrated lackofmemory process | 170 |
Connection with certain test statistics and empirical processes | 172 |
Asymptotic behaviour of the process | 181 |
Statement of results examples and comparisons | 190 |
Integral statistics Applications to testing | 196 |
Asymptotic efficiency in the continuous case | 207 |
Patterns in Sequences of Random Events | 227 |
Further classical and martingale methods | 235 |
Stochastic Models in Telecommunications for Optimal Design Control | 243 |
Network performance using traffic models | 250 |
LAN multiaccess communication models | 268 |
Stochastic Processes in Epidemic Modelling and Simulation | 285 |
Spatial models | 301 |
Stochastic models for control of epidemics | 308 |
Stochastic processes in parameter estimation and hypothesis testing | 321 |
Summary and conclusions | 328 |
Empirical Estimators Based on MCMC Data | 337 |
Efficient estimation for Markov chain models | 345 |
Asymptotic variance of empirical estimators for Gibbs samplers | 351 |
Improving empirical estimators for random fields with local interactions | 359 |
Fractals and the Modelling of SelfSimilarity | 371 |
Fractals and stochastic processes | 388 |
Further applications and conclusion | 403 |
Numerical inversion of Laplace transforms | 410 |
Infinite Markov chains | 418 |
The quasi birthanddeath process | 425 |
The MAPMRF framework | 499 |
An Introduction to SemiMarkov Processes with Application | 515 |
SemiMarkov processes with an arbitrary state space | 525 |
The countable case | 532 |
Asymptotic behavior | 540 |
Reliability modeling and estimation | 551 |
Departures and Related Characteristics in Queueing Models | 557 |
Characterizationidentifiability via infinite divisibility property | 564 |
Discrete Variate Time Series | 573 |
Markov chains | 575 |
The DARMA models | 576 |
Models based on thinning | 578 |
Regression models | 594 |
State space and Bayesian models | 597 |
The future | 602 |
Extreme Value Theory Models and Simulation | 607 |
Limit laws in univariate extremes and characterizations | 608 |
Rates of convergence | 613 |
Generalized extreme value GEV distribution | 618 |
Generalized Pareto GP distribution | 620 |
Joint distribution of the rlargest order statistics | 623 |
A point process characterization | 624 |
Extremes of stochastic processes | 625 |
Limit laws for multivariate extremes | 635 |
Characterizations of the domain of attraction | 637 |
Characterizations of multivariate extreme value distributions | 649 |
Rates of convergence | 652 |
Parametric families for bivariate extreme value distributions | 654 |
Parametric families for multivariate extreme value distributions | 663 |
Extremes of multivariate stochastic processes | 677 |
Acknowledgements | 679 |
References | 680 |
Biological Applications of Branching Processes | 693 |
History surnames and sex | 695 |
Genetics and evolution | 703 |
Epidemic modelling | 728 |
Ecology and conservation modelling | 738 |
References | 762 |
Markov Chain Approaches to Damage Models | 775 |
Characterizations based on modified RaoRubin conditions | 782 |
Characterization via conditional expectations | 788 |
Exciting Events in the Universe | 795 |
The mystery of Gamma Ray bursts | 801 |
Other examples of astronomical point processes | 815 |
Conclusion | 822 |
Linear time series models and cumulant spectra | 833 |
Multivariate nonlinear time series and higherorder cumulants of random vectors | 843 |
Time dependent nonlinear models | 850 |
Stationary bilinear process in continuous time | 861 |
Nonlinear and NonGaussian StateSpace Modeling with Monte Carlo | 871 |
Nonlinear and nonGaussian statespace modeling | 887 |
Monte Carlo studies | 906 |
Summary and concluding remarks | 917 |
Markov Modelling of Burst Behaviour in Ion Channels | 931 |
Theoretical bursts | 939 |
Empirical bursts | 945 |
A linear sequential model with drug blockade | 951 |
A model for a supergated doublebarrelled chloride channel | 958 |
969 | |
Contents of Previous Volumes | 979 |