Introduction to the Numerical Solution of Markov Chains /

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A cornerstone of applied probability, Markov chains can be used to help model how plants grow, chemicals react, and atoms diffuse--and applications are increasingly being found in such areas as engineering, computer science, economics, and education. To apply the techniques to real problems, however...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Stewart, William J. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Princeton, NJ : Princeton University Press, [2021]
Temas:
Markov processes > Numerical solutions.
MATHEMATICS / Probability & Statistics / Stochastic Processes.
Absorbing state.
Arnoldi method.
Arnoldi process.
Asymptotic convergence rate.
Block Gauss-Seidel.
Block splitting.
Block triangular matrix.
Chebyshev approximation.
Coefficient matrix.
Conditional probabilities.
Conditioning.
Connected vertices.
Consistently ordered matrix.
Convergence factors.
Convergence properties.
Convergence.
Courtois problem.
Cyclic tendencies.
Decompositional methods.
Diagonal blocks.
Direct methods.
Directed graph.
Discretization parameter.
Eigenvalues.
Eigenvectors.
Ephemeral states.
Erlang law.
Euler methods.
Euler polygon.
Explicit method.
Family of solution curves.
Full pivoting.
Galerkin condition.
Gauss transformation.
Gaussian elimination.
Gerschgorin theorem.
Guard vectors.
Hermitian matrices.
Hessenberg Matrices.
Indicator function.
Iterative process.
Jacobi method.
Krylov subspace methods.
Lanczos methods.
Load-dependent.
Low-stepping transitions.
Lower Hessenberg matrix.
Machine epsilon.
Marca ordering.
Markov chains.
Memoryless property.
Near-breakdown.
Optimality property.
Orthogonal complement.
Overrelaxation.
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