Abstract
We present a new fundamental intuition forwhy the Kemeny feature of a Markov chain is a constant. This new perspective has interesting further implications.
References
[1] J.J Hunter, The Role of Kemeny’s constant in properties ofMarkov chains, Communications in Statistics -Theory andMethods, 43(2014), 1309-1321. 10.1080/03610926.2012.741742Search in Google Scholar
[2] I. Gialampoukidis, K. Gustafson, and I. Antoniou, Time operator ofMarkov chains and mixing times. Applications to financial data, Physica A 415(2014), 141-155. 10.1016/j.physa.2014.07.084Search in Google Scholar
[3] J.G Kemeny and J.L. Snell, Finite Markov Chains, Van Nostrand, Princeton, NJ, 1960. Search in Google Scholar
[4] D. Lay, Linear Algebra and its Applications, 4th Ed., Addison Wesley, Boston, MA, 2012. Search in Google Scholar
[5] J.J Hunter, Mathematical Techniques of Applied Probability, Volume 2, Discrete Time Models: Techniques and Application, Academic Press, New York, NY, 1983. Search in Google Scholar
[6] R.A Horn, and C.R Johnson, Matrix Analysis, Cambridge University Press, Cambridge, UK, 1985. 10.1017/CBO9780511810817Search in Google Scholar
[7] K. Gustafson, Antieigenvalue Analysis, with Applications to Numerical Analysis, Wavelets, Statistics, Quantum Mechanics, Finance and Optimization, World-Scientific, Singapore, 2012. 10.1142/8247Search in Google Scholar
[8] I. Antoniou, Th. Christidis, and K.Gustafson, Probability from chaos, International J. of Quantum Chemistry 98 (2004) pp 150-159. 10.1002/qua.10869Search in Google Scholar
©2016 Karl Gustafson and Jeffrey J. Hunter
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