ACM Home Page
Please provide us with feedback. Feedback
Rounding errors in certain algorithms involving Markov chains
Full text PdfPdf (723 KB)
Source ACM Transactions on Mathematical Software (TOMS) archive
Volume 19 ,  Issue 4  (December 1993) table of contents
Pages: 496 - 508  
Year of Publication: 1993
ISSN:0098-3500
Author
Winfried K. Grassmann  Univ. of Saskatchewan
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 6,   Downloads (12 Months): 29,   Citation Count: 2
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues   peer to peer  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/168173.168416
What is a DOI?

ABSTRACT

A number of algorithms involving Markov chains contain no subtractions. This property makes the analysis of rounding errors particularly simple. To show this, some principles for analyzing the propagation and generation of rounding errors in algorithms containing no subtraction are discussed first. These principles are then applied in the context of a simple recursive algorithm involving the transient solution of discrete-time Markov chains, Jensen's algorithm, and state reduction. Jensen's algorithm, also known as randomization or uniformization, is an algorithm for finding transient solutions of continuous-time Markov chains. State reduction is a method for finding equilibrium probabilities for discrete-time or continuous-time Markov chains.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
 
2
BUNCH, J.F. 1987. The weak and strong stability of algorithms in numerical linear algebra. Linear Algebra Appl. 88-89, 46-66.
 
3
DEBOOR, C., AND PINKUS, A. 1977. Backward error analysis for totally positive linear systems. Numer. Math. 27, 4, 485-490.
4
 
5
GRASSMANN, W.K. 1989. A probabilistic analysis of rounding errors of floating point numbers. Congr. Numeratlum 6g, 171-182_
 
6
GRASSMANN, W.K. 1985. The factorization of queueing equations and their interpretation. J. Opl. Res. Soc. 36, 11 (Nov.) 1041 1051.
 
7
GRASSMANN, W.K. 1977. Transient solutions in Markovian queueing systems. Comput Oper. Res. 4, 47-53.
 
8
GRASSMANN, W. K., AND HEYMAN, D. P. 1993. Computation of steady-state probabilities for infinite-state Markov chains with repeating rows ORSA J. Comput. 5, 3 (Summer).
 
9
GRASSMANN, W. K., TAKSAR, M. I., AND HEYMAN, D.P. 1985. Regenerative analysis and steady state dmtributions for Markov chains. Oper. Res. 33, 5 (Sept.-Oct.), 1107-1116.
 
10
GRoss, D., AND MmLER, D.R. 1984. The randomization technique as a modelling tool and solution procedure for transient Markov processes. Oper Res. 32, 2 (Mar.-Apr.), 343-361
 
11
 
12
HEYMAN, D. P., AND REEVES, A. 1989. Numerical solutions arising in Markov chain models ORSA J. Comput 1, t (Winter), 52-60.
 
13
 
14
JENSEN, A. 1953. Markov chains as an aid in the study of Markoff processes. Scand. Actuar. 36, 87-91.
 
15
KOHLAS, J. 1986 Numerical computation for mean passage times and absorption probabilities in Markov and semi-Markov models. Z. Oper. Res. 30, A197-A207.
 
16
 
17
KEMENY, J. G., SNELL, J. L., AND KNAPP, A. W 1966. Denumerable Markov Chains. Van Nostrand, Princeton, N.J.
 
18
O'CINNEIDE, 1993. Entrywise perturbation theory and error analysis for Markov chains. Numer. Math. 5, i (May).
 
19
STEWART, G. W. 1993. Gaussian elimination, perturbation theory and Markov chains. In Linear Algebra, Markov Chains, and Queuemg Models. C. Meyer and R. J. Ptemmons, Eds. Springer-Verlag, New York, 59 69
 
20
STEWART, G. W, AND ZHANG, G. 1990. On a direct method for the solution of nearly uncoupled Markov chains. Numer. Math. 59, 1 (Apr.), 1 11.
 
21


Collaborative Colleagues:
Winfried K. Grassmann: colleagues

Peer to Peer - Readers of this Article have also read: