Abstract
We consider the coupling from the past implementation of the random–cluster heat-bath process, and study its random running time, or coupling time. We focus on hypercubic lattices embedded on tori, in dimensions one to three, with cluster fugacity at least one. We make a number of conjectures regarding the asymptotic behaviour of the coupling time, motivated by rigorous results in one dimension and Monte Carlo simulations in dimensions two and three. Amongst our findings, we observe that, for generic parameter values, the distribution of the appropriately standardized coupling time converges to a Gumbel distribution, and that the standard deviation of the coupling time is asymptotic to an explicit universal constant multiple of the relaxation time. Perhaps surprisingly, we observe these results to hold both off criticality, where the coupling time closely mimics the coupon collector’s problem, and also at the critical point, provided the cluster fugacity is below the value at which the transition becomes discontinuous. Finally, we consider analogous questions for the single-spin Ising heat-bath process.
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Notes
For concreteness, in the present discussion we refer to the measure corresponding to wired boundary conditions [24, Sect. 4.2].
We adopt the convention that \(\mathbb {N}\,:=\{0,1,2,\ldots \}\) and \(\mathbb {N}^+:=\{1,2,\ldots \}\).
The notation \(a_L\asymp b_L\) means that there exist constants \(c,C>0\) such that \(c b_L \le a_L \le C b_L\) for all sufficiently large L.
Assuming the relevant exponents exist.
I.e. \(M_n\mapsto (M_n-b_n)/a_n\) for some deterministic sequences \(a_n>0\) and \(b_n\).
Since the time \(W_k\) of the first arrival of \(D_k\) is not geometrically distributed, \(I_k\) is not itself a sum of geometric random variables.
References
Aizenman, M., Duminil-Copin, H., Sidoravicius, V.: Random currents and continuity of ising model’s spontaneous magnetization. Commun. Math. Phys. 334, 719–742 (2015)
Baxter, R.J.: Solvable eight-vertex model on an arbitrary planar lattice. Philos. Trans. R. Soc. A 289, 315–346 (1978)
Beffara, V., Duminil-Copin, H.: The self-dual point of the two-dimensional random-cluster model is critical for q \(\ge \) 1. Probab. Theory Relat. Fields 153, 511–542 (2012)
Billingsley, P.: Probability and Measure. (Wiley Series in Probability and Statistics), 3rd edn. Wiley, New York (1994)
Cesi, F., Guadagni, G., Martinelli, F., Schonmann, R.H.: On the two-dimensional stochastic Ising model in the phase coexistence region near the critical point. J. Stat. Phys. 85, 55–102 (1996)
Chayes, L., Machta, J.: Graphical representations and cluster algorithms II. Phys. A 254, 477–516 (1998)
Chow, Y.S., Teicher, H.: Probability Theory: Independence, Interchangeability, Martingales. Springer, New York (1978)
Deng, Y., Blöte, H.: Simultaneous analysis of several models in the three-dimensional Ising universality class. Phys. Rev. E 68, 036125 (2003)
Deng, Y., Garoni, T., Machta, J., Ossola, G., Polin, M., Sokal, A.: Critical behavior of the Chayes–Machta–Swendsen–Wang dynamics. Phys. Rev. Lett. 99, 055701 (2007)
Deng, Y., Garoni, T.M., Sokal, A.D.: Critical speeding-up in the local dynamics of the random-cluster model. Phys. Rev. Lett. 98, 230602 (2007)
Duminil-Copin, H., Gagnebin, M., Harel, M., Manolescu, I., Tassion, V.: Discontinuity of the phase transition for the planar random-cluster and Potts models with \(q > 4\). arXiv:1611.09877 (2016)
Duminil-Copin, H., Sidoravicius, V., Tassion, V.: Continuity of the phase transition for planar random-cluster and Potts models with \(1 \le q \le 4\). arXiv:1505.04159 (2015)
Dyer, M., Greenhill, C., Ullrich, M.: Structure and eigenvalues of heat-bath Markov chains. Linear Algebra Appl. 454, 57–71 (2014)
Elci, E.: Algorithmic and geometric aspects of the random-cluster model. Ph.D. thesis (2015)
Elçi, E.M., Weigel, M.: Efficient simulation of the random-cluster model. Phys. Rev. E 88, 033303 (2013)
Elçi, E.M., Weigel, M.: Dynamic connectivity algorithms for Monte Carlo simulations of the random-cluster model. J. Phys. 510(1), 012013 (2014)
Erdos, P., Renyi, A.: On a classical problem of probability theory. Publ. Math. Inst. Hung. Acad. Sci. Ser. A 6, 215–219 (1961)
Feller, W.: An Introduction to Probability Theory and Its Applications, vol. 1, 3rd edn. Wiley, New York (1968)
Friedli, S., Velenik, Y.: Statistical Mechanics of Lattice Systems. A Concrete Mathematical Introduction. Cambridge University Press, Cambridge (2016)
Gheissari, R., Lubetzky, E.: Mixing Times Of Critical 2D Potts Models. arXiv:1607.02182 (2016)
Gliozzi, F.: Simulation of Potts models with real q and no critical slowing down. Phys. Rev. E 66, 016115 (2002)
Graham, R.L., Knuth, D.E., Patashnik, O.: Concrete Mathematics. A Foundation for Computer Science, 2nd edn. Addisen-Wesley Publishing Company, Boston (1994)
Grassberger, P.: Damage spreading and critical exponents for “model A” Ising dynamics. Phys. A 214, 547–559 (1995)
Grimmett, G.: The Random-Cluster Model. Springer, New York (2006)
Grimmett, G.: Probability on Graphs. Cambridge University Press, Cambridge (2010)
Guo, H., Jerrum, M.: Random cluster dynamics for the Ising model is rapidly mixing. arXiv:1605.00139 pp. 1–15 (2016)
Häggström, O.: Finite Markov Chains and Algorithmic Applications. Cambridge University Press, Cambridge (2003)
Hartmann, A.: Calculation of partition functions by measuring component distributions. Phys. Rev. Lett. 94, 050601 (2005)
Holm, J., de Lichtenberg, K., Thorup, M.: Poly-logarithmic deterministic fully-dynamic algorithms for connectivity, minimum spanning tree, 2-edge, and biconnectivity. J. ACM (JACM) 48, 723–760 (2001)
Jaeger, F., Vertigan, D.L., Welsh, D.J.A.: On the computational complexity of the Jones and Tutte polynomials. Math. Proc. Camb. Philos. Soc. 108, 35–53 (1990)
Janson, S.: Tail bounds for sums of geometric and exponential random variables (2014). http://www2.math.uu.se/~svante/papers/sjN14.pdf
Jerrum, M.: Mathematical Foundations of the Markov Chain Monte Carlo Method. In: Probabilistic Methods for Algorithmic Discrete Mathematics, pp. 116–165. Springer, New York (1998)
Laanait, L., Messager, A., Miracle-Solé, S., Ruiz, J., Shlosman, S.: Interfaces in the Potts model I: Pirogov–Sinai theory of the Fortuin–Kasteleyn representation. Commun. Math. Phys. 140(1), 81–91 (1991)
Leadbetter, M.R., Lindgren, G., Rootzen, H.: Extremes and related properties of random sequences and processes. Springer, New York (1983)
Levin, D.A., Peres, Y., Wilmer, E.L.: Markov Chains and Mixing Times. American Mathematical Society, Providence (2009)
Madras, N., Slade, G.: The Self-Avoiding Walk. Birkhauser, Boston (1996)
McCoy, B.M., Wu, T.T.: The Two-Dimensional Ising Model. Harvard University Press, Cambridge (1973)
Mitzenmacher, M., Upfal, E.: Probability and Computing. Randomized Algorithms and Probabilistic Analysis. Cambridge University Press, Cambridge (2005)
Nacu, S.: Glauber dynamics on the cycle is monotone. Probab. Theory Relat. Fields 127, 177–185 (2003)
Nienhuis, B.: Critical behavior of two-dimensional spin models and charge asymmetry in the Coulomb gas. J. Stat. Phys. 34, 731–761 (1984)
Nightingale, M.P., Bloete, H.W.J.: Dynamic exponent of the two-dimensional Ising model and Monte Carlo computation of the subdominant eigenvalue of the stochastic matrix. Phys. Rev. Lett. 76, 4548–4551 (1996)
Posfai, A.: Approximation Theorems Related to the Coupon Collector’s Problem . Ph.D. thesis (2010)
Propp, J., Wilson, D.: Exact sampling with coupled Markov chains and applications to statistical mechanics. Random Struct. Algorithms 9, 223–252 (1996)
Sinclair, A.B., Alistair, Sinclair, A.: Random-Cluster Dynamics in \(\mathbb{Z}^2\). In: Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 498–513 (2016)
Sokal, A.D.: Monte Carlo methods in statistical mechanics: foundations and new algorithms. In: DeWitt-Morette, C., Cartier, P., Folacci, A. (eds.) Functional Integration: Basics and Applications (1996 Cargèse summer school), pp. 131–192. Plenum, New York (1997)
Sweeny, M.: Monte Carlo study of weighted percolation clusters relevant to the Potts models. Phys. Rev. B 27, 4445–4455 (1983)
Swendsen, R.H., Wang, J.S.: Nonuniversal critical dynamics in Monte Carlo simulations. Phys. Rev. Lett. 58, 86–88 (1987)
Wang, J.S., Kozan, O., Swendsen, R.: Sweeny and Gliozzi dynamics for simulations of Potts models in the Fortuin-Kasteleyn representation. Phys. Rev. E 66(5), 057101 (2002)
Welsh, D.J.A.: Complexity: Knots, Colourings and Counting, London Mathematical Society Lecture Note Series, vol. 186. Cambridge University Press, Cambridge (1993)
Deng, Youjin, Garoni, Timothy, M., Sokal, Alan, Zhou, Zongzheng: Dynamic critical behavior of the Chayes-Machta random-cluster algorithm II: Three-dimensions. In preparation
Young, P.: Everything You Wanted to Know about Data Analysis and Fitting but were Afraid to ask. Springer, New York (2015)
Acknowledgements
The authors thank Youjin Deng, Alan Sokal, and Ulli Wolff for useful discussions, and an anonymous referee for helpful comments. This work was supported under the Australian Research Council’s Discovery Projects funding scheme (project numbers DP140100559 & DP110101141), and T.G. is the recipient of an Australian Research Council Future Fellowship (project number FT100100494). A.C. would like to thank STREP project MATHEMACS. The work of EE and MW was partially supported by the European Commission through the IRSES network DIONICOS (PIRSES-GA-2013-612707).
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Appendices
Appendix A: Autocorrelation Functions of Strictly Increasing Observables
Let P denote the transition matrix of the FK heat-bath process on a finite graph \(G=(V,E)\) with parameters \(p\in (0,1)\) and \(q\ge 1\), and let \(k=2^{|E|}\). To avoid trivialities, we assume \(|E|>1\). We regard elements of \(\mathbb {R}^k\) as functions from \(2^E\) to \(\mathbb {R}\), and we endow \(\mathbb {R}^k\) with the inner product \(\langle \cdot ,\cdot \rangle \) defined by
Denote the eigenvalues of P by \(1=\lambda _1 > \lambda _2\ge \ldots \ge \lambda _k\). As mentioned in Sect. 2.2, general results for heat-bath chains [13] imply that all \(\lambda _i\) are non-negative. Let \(\{\psi _i\}_{i=1}^k\) be an orthonormal basis for \(\mathbb {R}^k\) such that \(\psi _i\) is an eigenfunction of P corresponding to \(\lambda _i\). The Perron-Frobenius theorem implies that the eigenspace of \(\lambda _1\) is one-dimensional, and that we can take \(\psi _1(A)=1\) for all \(A\subseteq E\). Let W denote the eigenspace of \(\lambda _2\). For \(g\in \mathbb {R}^k\), we let \(g_W\) denote its projection onto W.
We say \(g\in \mathbb {R}^k\) is increasing if \(A\subset B\) implies \(g(A)\le g(B)\), and strictly increasing if \(A\subset B\) implies \(g(A) < g(B)\).
Proposition A.1
Let \((X_t)_{t\in \mathbb {N}}\) be a stationary FK heat-bath process, and for \(g\in \mathbb {R}^k\) define \((g_t)_{t\in \mathbb {N}}\) via \(g_t\,:=g(X_t)\). If g is strictly increasing, then its autocorrelation function satisfies
for constant \(C>0\).
Proof
Let \(\varPi \) denote the projection matrix onto the space of constant functions. General arguments (see e.g. [45] or [36, Chap. 9]) imply
Since g is strictly increasing, Lemma A.2 implies that \(\Vert g_W\Vert ^2>0\), and therefore
It follows that
\(\square \)
Lemma A.2
If g is strictly increasing, then its projection onto W is non-zero.
Proof
Lemma A.3 implies there exists \(\psi \in W\) which is non-zero and increasing. Positive association (see e.g. [24, Theorem 3.8 (b)]) then implies that for any other increasing g we have
since \(\mathbb {E}(\psi )=\langle \psi _1,\psi \rangle =0\). In particular, suppose that g is strictly increasing. Choosing \(\alpha >0\) so that
implies that \(g-\alpha \psi \) is also strictly increasing. Applying (A.1) to \(g-\alpha \psi \) then yields
Rearranging, and using the fact that \(\psi \) is non-zero then implies
Therefore, g has a non-zero projection onto \(\psi \in W\), and the stated result follows. \(\square \)
The following lemma is the natural analogue, in the FK setting, of the result [39, Lemma 3] established for the Ising heat-bath process.
Lemma A.3
There exists \(\psi \in W\) which is non-zero and increasing.
Proof
Let \(g=\psi _2+ C (\mathscr {N}-\mathbb {E}(\mathscr {N}))\), where \(\mathscr {N}\in \mathbb {R}^k\) is defined so that \(\mathscr {N}(A)=|A|\) for each \(A\subseteq E\), and \(C>0\) is a constant. We have
If \(\langle \mathscr {N},\psi _2\rangle =0\), then g has a non-zero projection onto \(\psi _2\), for any choice of \(C>0\). If \(\langle \mathscr {N},\psi _2\rangle \ne 0\), then choosing \(C>|\langle \mathscr {N},\psi _2\rangle |^{-1}\) suffices to guarantee that g again has a non-zero projection onto \(\psi _2\). In either case, assume C is so chosen. It follows that \(g_W\) is non-zero.
If \(A\subset B\), then
Therefore, by choosing \(C>\left| \min \limits _{A\subset B\subseteq E} [\psi _2(B) - \psi _2(A)]\right| \) we guarantee that g is increasing. Lemma A.4 then implies that \(g_W\) is increasing. Therefore, \(\psi =g_W\) is an increasing, non-zero element of W. \(\square \)
Lemma A.4
If g is increasing and has zero-mean, then its projection onto W is also increasing.
Proof
Let \(g\in \mathbb {R}^k\) be any increasing observable with mean zero, and let \(t\in \mathbb {N}^+\). Since Lemma A.6 implies \(\lambda _2>0\), we can write
It follows that
Now, for any given \(t\ge 1\), Lemma A.5 implies that \(P^t g(A)\) is an increasing function of A, and so \(\lambda _2^{-t}P^t g (A)\) is also an increasing function of A. It then follows, as an elementary consequence of (A.2), that \(g_W\) is also increasing. We have therefore established that if g is an increasing zero-mean function, then its projection \(g_W\) is also increasing. \(\square \)
Lemma A.5
If \(g\in \mathbb {R}^k\) is increasing, then \(P^tg\) is also increasing, for every \(t\ge 1\).
Proof
Let \((f,\mathscr {E},U)\) be the random mapping representation for P given in Sect. 2.1; see (2.3). Let \(A_1\subset A_2\subseteq E\), and let \(B_i=f(A_i,\mathscr {E},U)\) for \(i=1,2\). Clearly, \((B_1,B_2)\) is a coupling of the distributions \(P(A_1,\cdot )\) and \(P(A_2,\cdot )\), and the monotonicity of f implies \(B_1\subseteq B_2\). Strassen’s theorem (see e.g. [25, Theorem 4.2]) then implies that
for any increasing \(g\in \mathbb {R}^k\). It follows that
Since this holds for any \(A_1\subset A_2\subseteq E\), it follows that Pg is increasing. It then follows by a simple induction that \(P^tg\) is increasing for any \(t\ge 1\). \(\square \)
Lemma A.6
The second-largest eigenvalue of P is positive.
Proof
Since P is reversible and irreducible we have the spectral decomposition (see e.g. [35, Lemma 12.2])
Since \(\lambda _2\ge \lambda _j\ge 0\) for all \(j>2\), it follows that if \(\lambda _2=0\), then \(P(A,B)=\phi (B)\) for all \(A,B\subseteq E\). But since, by assumption, we have \(|E|>1\), we can choose \(A,B\subseteq E\) with \(|A\triangle B| > 1\), where \(\triangle \) denotes symmetric difference, and (2.2) then implies
We have therefore reached a contradiction, and we conclude that \(\lambda _2>0\). \(\square \)
Appendix B: Coupon Collecting
Let \(n\in \mathbb {N}^+\), and let \(C_1,C_2,\ldots \) be an iid sequence of uniformly random elements of \([n]\,:=\{1,2,\ldots ,n\}\). For \(t\in \mathbb {N}^+\), we think of \(C_t\) as the coupon collected at time t. For \(i\in [n]\), let \(D_i\in [n]\) denote the ith distinct type of coupon collected; i.e. the ith distinct element of the sequence \(C_1,C_2,\ldots \). Let \(S_i(t)\,:=\#\{s\le t : C_s = D_i\}\), the number of copies of \(D_i\) collected by time t. Define \(R_t\,:=\{c\in [n]: C_s = c \text { for some } s\le t\}\), the set of distinct coupon types collected up to time t. For any \(1\le k \le n\), let \(W_k=\inf \{t\in \mathbb {N}^+: |R_t| = k\}\), and note that \(W_k\) is simply the hitting time of \(D_{k}\). The coupon collector’s time is then defined as \(W\,:=W_n\).
For each \(c\in [n]\), define
We refer to the time \(H(c)\) as the last visit to c. Let \((H_i)_{i=1}^n\) denote the sequence of the \(H(c)\), arranged in increasing order. In particular, \(H_1\) is the first time that a last visit occurs.
Lemma B.1
There exists \(\varphi >0\) such that \(\mathbb {P}(|R_{H_{1}}| \le \left\lfloor \ln n \right\rfloor ) = O(n^{-\varphi })\).
Proof
Inserting \(a_n=\lfloor \ln (n)\rfloor \) and \(c_n=\lfloor \ln (n)/4\rfloor \) into Lemma B.2 and applying the union bound, implies
Therefore, for any \(0<\rho <1/4\), we have
It follows that,
Let \(I\,:=\inf \{t\in \mathbb {N}^+: S_i(t) = c_n \text { for some } i\in [n] \}\), the first time that there exists a coupon type for which exactly \(c_n\) copies have been collected, and define the random variable \(K\in [n]\) via \(C_{H_1}=D_K\). If \(|R_{H_1}|\le a_n\), then \(1\le K \le a_n\). Therefore, observing that \(S_{K}(W)=S_{K}(H_1)\), we find
since if \(|R_{H_1}|\le a_n\) and \(S_{K}(H_1) > c_n\) then \(|R_I|\le a_n\). Combining (B.1) and (B.2) then implies
However, Lemma B.3 implies that there exists \(\delta >0\) such that \(\mathbb {P}(|R_I|\le a_n) = O(n^{-\delta })\). We therefore conclude that, if \(\varphi =\min \{\rho ,\delta \}\), then
\(\square \)
Lemma B.2
Let \((a_n)_{n\in \mathbb {N}^+}\) and \((c_n)_{n\in \mathbb {N}^+}\) be any two sequences of natural numbers. For \(n\in \mathbb {N}^+\), if \(a_n<n\) then for each \(1\le i\le a_n\) we have
Proof
Fix \(n\in \mathbb {N}^+\) and \(1\le i \le a_n\), and assume \(a_n<n\). Adopting the convention \(W_0=0\), for \(0\le k \le n-1\) we define
Since \(Y_i(k)=0\) for all \(k<i\), and \(C_{W_k}=D_i\) iff \(k=i\), we then have
And since the random variables \(Y_i(k)\) are independent, for any \(\theta <0\), we have
where the final step follows from Markov’s inequality.
The moment generating function of \(Y_i(k)\) can be calculated explicitly. Let \(i \le k \le n-1\). Given \(W_{k}\) and \(W_{k+1}\), the random variable \(Y_i(k)\) has binomial distribution with \(W_{k+1} -W_{k}-1\) trials and success probability 1 / k, which implies
But since \(W_{k+1}-W_{k}\) has geometric distribution with parameter \(1-k/n\), this becomes
Therefore, setting \(\lambda =1-e^\theta \) and \(b_n=n-a_n\), it follows from the fact that \(\ln (1+\lambda /k)\) is a decreasing function of k that
where, in the penultimate step, we used the fact that \(\ln (1+x)\ge x/(1+x)\) holds for all \(x>-1\), and in the last step we used the fact that \((1+\lambda )-(1+\lambda )\ln (1+\lambda )>0\) for any \(\lambda \in (0,1)\). Combining (B.3) and (B.4), we conclude that for all \(\lambda \in (0,1)\) we have
Choosing \(\lambda =1/2\) yields the stated result. \(\square \)
Lemma B.3
Fix \(c\in (0,1)\), and define sequences \((a_n)_{n\in \mathbb {N}^+}\) and \((c_n)_{n\in \mathbb {N}^+}\) such that \(a_n=\lfloor \ln (n)\rfloor \) and \(c_n=\lfloor c\ln (n)\rfloor \). Let
the first time that there exists a coupon type for which exactly \(c_n\) copies have been collected. Then there exists \(\delta >0\) such that
Proof
We assume, in all that follows, that n is sufficiently large that \(c_n>1\). For \(k\in [n]\), let
be the first time that \(c_n\) copies of coupon type \(D_k\) have been collected. For any sequence of natural numbers \((b_n)_{n\in \mathbb {N}^+}\), we have
where the last inequality follows by observing that if \(|R_{I_k}|\le a_n\) and \(I_k>b_n\), then \(W_{a_n+1}>b_n\).
To find an upper bound for \(\mathbb {P}(I_k\le b_n)\), note that, for any \(s\ge 1\), the random time between the sth and \((s+1)\)th arrival of coupon type \(D_k\) is a geometric random variable with success probability 1 / n. It follows that \(\varDelta _k\,:=I_k-W_k\) is a sum of \(c_n-1\) independent geometric random variables,Footnote 6 each with success probability 1 / n. Lemma B.4 therefore implies that for any \(0<\lambda <1\),
where \(f(\lambda )>0\). But from the trivial lower bound \(W_k\ge 1\), it follows that \(\varDelta _k\le I_k -1\). Therefore, for any \(b_n\le \lambda n(c_n-1)+1\), we have
To find an upper bound for \(\mathbb {P}(W_{a_n+1}>b_n)\), we begin with the observation that, with the convention \(W_0=0\), we have
For \(0\le i \le a_n\), the random variables \(W_{i+1}-W_i\) are independent, and distributed according to a geometric distribution with success probability \(1-i/n\). Therefore, Lemma B.4 implies that for any \(\zeta >1\)
with \(f(\zeta )>0\). But explicit calculation shows that
where \(H_i\) is the ith harmonic number, and the asymptotic result follows from \(H_n\sim \ln (n)\) and the fact that \(a_n=o(n)\). It follows that for any choice of \(b_n \ge \zeta \, \mathbb {E}(W_{a_n+1})\) and \(\alpha \in (0,f(\zeta ))\), for sufficiently large n, we have
Any choice of \(b_n\) satisfying \(\zeta \,\mathbb {E}(W_{a_n+1}) \le b_n \le \lambda n(c_n-1)+1\), for sufficiently large n, suffices to ensure (B.6) and (B.7) hold simultaneously. It therefore suffices to set \(b_n=n\). For simplicity, \(\lambda \in (0,1)\) and \(\zeta >1\) can be chosen so that \(f(\lambda )=1=f(\zeta )\). Combining (B.5), (B.6) and (B.7) then implies that for any \(\alpha <1\) we have
for sufficiently large n.
Finally, since \(|R_I|\le a_n\) implies \(|R_{I_k}|\le a_n\) for some \(1\le k \le a_n\), it follows from the union bound that, for sufficiently large n,
Since \(c,\alpha >0\), we can choose \(0< \delta <\min \{c,\alpha \}\), and we obtain \(\mathbb {P}(|R_I|\le a_n) = O(n^{-\delta })\). \(\square \)
Lemma B.4
Let \(X_1,X_2,\ldots ,X_n\) be independent random variables, such that \(X_i\) has geometric distribution with success probability \(p_i\), and let \(X=\sum _{i=1}^n X_i\). Then
where \(\mu =\mathbb {E}(X) = \sum _{i=1}^n 1/p_i\), \(p_*= \min _{i\in [n]} p_i\) and \(f(x)=x -1 -\ln (x)\).
Proof
These results can be established, in the standard way, by applying Markov’s inequality to \(\mathbb {E}(e^{t X})\), and using the explicit form for \(\mathbb {E}(e^{t X_i})\); see e.g. [31]. \(\square \)
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Collevecchio, A., Elçi, E.M., Garoni, T.M. et al. On the Coupling Time of the Heat-Bath Process for the Fortuin–Kasteleyn Random–Cluster Model. J Stat Phys 170, 22–61 (2018). https://doi.org/10.1007/s10955-017-1912-x
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DOI: https://doi.org/10.1007/s10955-017-1912-x