Stochastic Simulation and Monte Carlo Methods Mathematical Foundations of Stochastic Simulation
In various scientific and industrial fields, stochastic simulations are taking on a new importance. This is due to the increasing power of computers and practitioners’ aim to simulate more and more complex systems, and thus use random parameters as well as random noises to model the parametric uncer...
| Main Authors: | , |
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| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2013, 2013
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| Edition: | 1st ed. 2013 |
| Series: | Stochastic Modelling and Applied Probability
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Part I:Principles of Monte Carlo Methods
- 1.Introduction
- 2.Strong Law of Large Numbers and Monte Carlo Methods
- 3.Non Asymptotic Error Estimates for Monte Carlo Methods
- Part II:Exact and Approximate Simulation of Markov Processes
- 4.Poisson Processes
- 5.Discrete-Space Markov Processes
- 6.Continuous-Space Markov Processes with Jumps
- 7.Discretization of Stochastic Differential Equations
- Part III:Variance Reduction, Girsanov’s Theorem, and Stochastic Algorithms
- 8.Variance Reduction and Stochastic Differential Equations
- 9.Stochastic Algorithms
- References
- Index