Balance equation
In probability theory, a balance equation is an equation that describes the probability flux associated with a Markov chain in and out of states or set of states.[1]
Global balance
The global balance equations (also known as full balance equations[2]) are a set of equations that characterize the equilibrium distribution (or any stationary distribution) of a Markov chain, when such a distribution exists.
For a continuous time Markov chain with state space
or equivalently
for all
Detailed balance
For a continuous time Markov chain (CTMC) with transition rate matrix
holds, then by summing over
A CTMC is reversible if and only if the detailed balance conditions are satisfied for every pair of states
A discrete time Markov chain (DTMC) with transition matrix
When a solution can be found, as in the case of a CTMC, the computation is usually much quicker than directly solving the global balance equations.
Local balance
In some situations, terms on either side of the global balance equations cancel. The global balance equations can then be partitioned to give a set of local balance equations (also known as partial balance equations,[2] independent balance equations[7] or individual balance equations[8]).[1] These balance equations were first considered by Peter Whittle.[8][9] The resulting equations are somewhere between detailed balance and global balance equations. Any solution
During the 1980s it was thought local balance was a requirement for a product-form equilibrium distribution,[10][11] but Gelenbe's G-network model showed this not to be the case.[12]
Notes
- ↑ Jump up to: 1.0 1.1 1.2 Harrison, Peter G.; Patel, Naresh M. (1992). Performance Modelling of Communication Networks and Computer Architectures. Addison-Wesley. ISBN 0-201-54419-9. https://archive.org/details/performancemodel0000harr.
- ↑ Jump up to: 2.0 2.1 2.2 Kelly, F. P. (1979). Reversibility and stochastic networks. J. Wiley. ISBN 0-471-27601-4. http://www.statslab.cam.ac.uk/~frank/BOOKS/kelly_book.html.
- ↑ Chandy, K.M. (March 1972). "The analysis and solutions for general queueing networks". Princeton, N.J.. pp. 224–228.
- ↑ Jump up to: 4.0 4.1 Grassman, Winfried K. (2000). Computational probability. Springer. ISBN 0-7923-8617-5.
- ↑ Bocharov, Pavel Petrovich; D'Apice, C.; Pechinkin, A.V.; Salerno, S. (2004). Queueing theory. Walter de Gruyter. p. 37. ISBN 90-6764-398-X.
- ↑ Norris, James R. (1998). Markov Chains. Cambridge University Press. ISBN 0-521-63396-6. http://www.statslab.cam.ac.uk/~james/Markov/. Retrieved 2010-09-11.
- ↑ Baskett, F.; Chandy, K. Mani; Muntz, R.R.; Palacios, F.G. (1975). "Open, closed and mixed networks of queues with different classes of customers". Journal of the ACM 22 (2): 248–260. doi:10.1145/321879.321887.
- ↑ Jump up to: 8.0 8.1 Whittle, P. (1968). "Equilibrium Distributions for an Open Migration Process". Journal of Applied Probability 5 (3): 567–571. doi:10.2307/3211921.
- ↑ Chao, X.; Miyazawa, M. (1998). "On Quasi-Reversibility and Local Balance: An Alternative Derivation of the Product-Form Results". Operations Research 46 (6): 927–933. doi:10.1287/opre.46.6.927.
- ↑ Boucherie, Richard J.; van Dijk, N.M. (1994). "Local balance in queueing networks with positive & negative customers". Annals of Operations Research 48 (5): 463–492. doi:10.1007/bf02033315. https://www.researchgate.net/publication/225825899_Local_balance_in_queueing_networks_with_positive_and_negative_customers.
- ↑ Chandy, K. Mani; Howard, J.H. Jr; Towsley, D.F. (1977). "Product form and local balance in queueing networks". Journal of the ACM 24 (2): 250–263. doi:10.1145/322003.322009. http://portal.acm.org/citation.cfm?id=322009.
- ↑ Gelenbe, Erol (Sep 1993). "G-Networks with Triggered Customer Movement". Journal of Applied Probability 30 (3): 742–748. doi:10.2307/3214781.
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