Source: Ann. Appl. Probab. Volume 16, Number 4
(2006), 2098-2122.
This paper gives a necessary and sufficient condition for a sequence of birth and death chains to converge abruptly to stationarity, that is, to present a cut-off. The condition involves the notions of spectral gap and mixing time. Y. Peres has observed that for many families of Markov chains, there is a cut-off if and only if the product of spectral gap and mixing time tends to infinity. We establish this for arbitrary birth and death chains in continuous time when the convergence is measured in separation and the chains all start at 0.
References
Aldous, D. (1983). Random walks on finite groups and rapidly mixing Markov chains. Seminar on Probability XVII. Lecture Notes in Math. 986 243--297. Springer, Berlin.
Aldous, D. and Diaconis, P. (1986). Shuffling cards and stopping times. Amer. Math. Monthly 93 333--348.
Aldous, D. and Diaconis, P. (1986). Strong uniform times and finite random walks. Adv. in Appl. Math. 8 69--97.
Aldous, D. and Fill, J. Reversible Markov Chains and Random Walks on Graphs. Book project. Available at http://www.stat.berkeley.edu/users/aldous/RWG/book.html.
Bannai, E. and Ito, T. (1987). Algebraic Combinatorics. I. Association Schemes. Benjamin, Menlo Park, CA.
Belsley, E. (1993). Rates of convergence of Markov chains related to association schemes. Ph.D. dissertation, Harvard Univ.
Belsley, E. (1998). Rates of convergences of random walk on distance regular graphs. Probab. Theory Related Fields 112 493--533.
Brown, M. and Shao, Y.-S. (1987). Identifying coefficients in the spectral representation for first passage time distributions. Probab. Engrg. Inform. Sci. 1 69--74.
Brouwer, A. E., Cohen, A. M. and Neumaier, A. (1989). Distance-Regular Graphs. Springer, Berlin.
Chen, G.-Y. (2006). The cut-off phenomenon for finite Markov chains. Ph.D. dissertation, Cornell Univ.
Chen, G.-Y. and Saloff-Coste, L. (2006). $L^p$ cut-offs for finite Markov chains. To appear.
Curtiss, J. H. (1942). A note on the theory of moment generating functions. Ann. Math. Statist. 13 430--433.
D'Aristotle, A. (1993). The nearest neighbor random walk on subspaces of a vector space and rate of convergence. J. Theoret. Probab. 8 321--346.
Diaconis, P. (1988). Group Representations in Probability and Statistics. IMS, Hayward, CA.
Diaconis, P. (1996). The cutoff phenomenon in finite Markov chains. Proc. Natl. Acad. Sci. USA 93 1659--1664.
Diaconis, P. and Fill, J. (1990). Strong stationary times via a new form of duality. Ann. Probab. 18 1483--1522.
Diaconis, P. and Hanlon, P. (1992). Eigen analysis for some examples of the Metropolis algorithm. In Hypergeometric Functions on Domains of Positivity, Jack Polynomials, and Applications 99--117. Amer. Math. Soc., Providence RI.
Diaconis, P. and Ram, A. (2000). Analysis of systematic scan Metropolis algorithms using Iwahori--Hecke algebra techniques. Michigan Math. J. 48 157--189.
Diaconis, P. and Saloff-Coste, L. (1993). Comparison techniques for random walk on finite groups. Ann. Probab. 21 2131--2156.
Diaconis, P. and Saloff-Coste, L. (1998). What do we know about the Metropolis algorithm? In 27th Annual ACM Symposium on the Theory of Computing (STOC'95) (Las Vegas, NV). J. Comput. System Sci. 57 20--36.
Diaconis, P. and Saloff-Coste, L. (1996). Logarithmic Sobolev inequalities for finite Markov chains. Ann. Appl. Probab. 6 695--750.
Diaconis, P. and Shahshahani, M. (1981). Generating a random permutation with random transpositions. Z. Wahrsch. Verw. Gebiete 57 159--179.
Diaconis, P. and Shahshahani, M. (1987). Time to reach stationarity in the Bernoulli--Laplace diffusion model. SIAM J. Math. Anal. 18 208--218.
Feller, W. (1971). An Introduction to Probability Theory and Its Applications. II, 2nd ed. Wiley, New York.
Fill, J. (1992). Strong stationary duality for continuous-time Markov chains. I. Theory. J. Theoret. Probab. 5 45--70.
Ismail, M., Masson, D., Letessier, J. and Valent, G. (1990). Birth and death processes and orthogonal polynomials. In Orthogonal Polynomials (Columbus, OH, 1989) 229--255. NATO Adv. Sci. Inst. Ser. C Math. Phys. Sci. 294. Kluwer Acad. Publ., Dordrecht.
Karlin, S. and McGregor, J. (1961). The Hahn polynomials, formulas and and application. Scripta Math. 26 33--46.
Karlin, S. and McGregor, J. (1965). Ehrenfest urn models. J. Appl. Probab. 2 352--376.
Keilson, J. (1979). Markov Chain Models---Rarity and Exponentiality. Springer, New York.
Lovász, L. and Winkler, P. (1998). Mixing times. In Microsurveys in Discrete Probability 85--133. Amer. Math. Soc., Providence RI.
Pak, I. and Van Vu, H. (2001). On mixing of certain random walks, cutoff phenomenon and sharp threshold of random matroid processes. Discrete Appl. Math. 110 251--272.
Parthasarathy, P. R. and Lenin, R. B. (1997). On the exact transient solution of finite birth and death processes with specific quadratic rates. Math. Sci. 22 92--105.
Parthasarathy, P. R. and Lenin, R. B. (1999). An inverse problem in birth and death processes. Comput. Math. Appl. 38 33--40.
Saloff-Coste, L. (1997). Lectures on finite Markov chains. Lectures on Probability Theory and Statistics 301--413. Lecture Notes in Math. 1665 301--413. Springer, Berlin.
Steutel, F. W. and van Harn, K. (2000). Infinite Divisibility of Probability Distributions on the Real Line. Dekker, New York.
Van Assche, W., Parthasarathy, P. R. and Lenin, R. B. (1999). Spectral representation of four finite birth and death processes. Math. Sci. 24 105--112.