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Projection Methods for the Numerical Solution of Markov Chain Models
| Content Provider | Scilit |
|---|---|
| Author | Saad, Youcef |
| Copyright Year | 2021 |
| Description | This paper gives an overview of projection methods for computing stationary probability distributions for Markov chain models. A general projection method is a method that seeks an approximation from a subspace of small dimension to the original problem. Thus, the original matrix problem of size N is approximated by one of dimension m, typically much smaller than N. A particularly successful class of methods based on this principle is that of Krylov subspace methods that utilize subspaces of the form {v,Av,…,Am−1v} These methods are effective in solving linear systems (i.e., conjugate gradients, GMRES) and eigenvalue problems (i.e., Lanczos, Arnoldi) as well as nonlinear equations. They can be combined with more traditional iterative methods such as SOR or SSOR, or with incomplete factorization methods to enhance convergence. Book Name: Numerical Solution of Markov Chains |
| Related Links | https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781003210160-24&type=chapterpdf |
| Ending Page | 471 |
| Page Count | 17 |
| Starting Page | 455 |
| DOI | 10.1201/9781003210160-24 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2021-06-16 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Numerical Solution of Markov Chains Statistics and Probability Projection Methods Subspace Approximated Markov Chain Chain Models |
| Content Type | Text |
| Resource Type | Chapter |
