Stochastic Modeling: Analysis and Simulation

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Choose your country's store to see books available for purchase. See if you have enough points for this item. Sign in. A coherent introduction to the techniques for modeling dynamic stochastic systems, this volume also offers a guide to the mathematical, numerical, and simulation tools of systems analysis. Suitable for advanced undergraduates and graduate-level industrial engineers and management science majors, it proposes modeling systems in terms of their simulation, regardless of whether simulation is employed for analysis.

Beginning with a view of the conditions that permit a mathematical-numerical analysis, the text explores Poisson and renewal processes, Markov chains in discrete and continuous time, semi-Markov processes, and queuing processes. Each chapter opens with an illustrative case study, and comprehensive presentations include formulation of models, determination of parameters, analysis, and interpretation of results. Programming language—independent algorithms appear for all simulation and numerical procedures. Data Analysis with Open Source Tools.

Philipp K. Probability, Markov Chains, Queues, and Simulation. William J. Arshad Iqbal. Schaum's Outline of Operations Research. Richard Bronson. Probability and Random Processes. Scott Miller.

Stochastic simulation

Financial Modelling. Daniel Wetterau. Modeling and Dimensioning of Mobile Wireless Networks. Maciej Stasiak.

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Elements of Information Theory. Thomas M. Sheldon M. Linear Programming and Network Flows. Mokhtar S. Mathematical Foundations of Computer Networking. Srinivasan Keshav.

Stochastic modeling and simulation of biological systems

Optimization Theory for Large Systems. Leon S. Fundamentals of Queueing Theory. John F. Introduction to Probability Models. Performance Modeling and Design of Computer Systems. Mor Harchol-Balter. Handbook of Monte Carlo Methods. Dirk P. Fourier Transform Methods in Finance.

Umberto Cherubini. Numerical Methods in Finance and Economics. Paolo Brandimarte. Stefano M. Essentials of Monte Carlo Simulation. Nick T. Dynamic Programming: Models and Applications. Eric V. Uncertainty and Optimization in Structural Mechanics. Abdelkhalak El Hami.


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Stochastic Modeling : Analysis & Simulation

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    Stochastic Modelling, Analysis, and Simulations of the Solar Cycle Dynamic Process - IOPscience

    Ronald A. Linear Models. William R. Numerical Analysis. Ridgway Scott. A female mosquito requires a certain number of blood meals for reproduction and it is assumed that a single mosquito takes k bites per unit time to fulfill this blood requirement. Another important assumption is that the total number of bites by the mosquito population is dependent on the total number of mosquitoes but it is not dependent on the number of human hosts only the proportion of human hosts.

    The probability per bite that an infectious mosquito transmits malaria is p and the probability per bite that a healthy mosquito acquires infection is q. The birth rate and death rate of the mosquito population are equal.

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    The natural birth and death rates of humans are negligible with respect to the modeling time frame and are assumed to be zero. With the preceding assumptions, the malaria model takes the form:. An equivalent form in terms of the threshold of one, is defined as the product of the transmission from vector to host and from host to vector:.

    source This latter expression is used in the following discussion of the basic reproduction number. There are six events corresponding to transmission from host to vector and vector to host, recovery of humans, death of healthy and infectious mosquitoes, and birth of mosquitoes.

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