Stochastic Monotonicity and Queueing Applications of by E. A. van Doorn (auth.) PDF

By E. A. van Doorn (auth.)

A stochastic technique {X(t): zero S t < =} with discrete nation area S c ~ is expounded to be stochastically expanding (decreasing) on an period T if the chances Pr{X(t) > i}, i E S, are expanding (decreasing) with t on T. Stochastic monotonicity is a simple structural estate for strategy behaviour. It supplies upward push to significant bounds for numerous amounts resembling the moments of the method, and gives the mathematical basis for approximation algorithms. evidently, stochastic monotonicity turns into a extra tractable topic for research if the techniques into consideration are such that stochastic mono tonicity on an inter­ val zero < t < E implies stochastic monotonicity at the complete time axis. DALEY (1968) was once the 1st to debate the same estate within the context of discrete time Markov chains. regrettably, he referred to as this estate "stochastic monotonicity", it really is extra acceptable, notwithstanding, to talk of tactics with monotone transition operators. KEILSON and KESTER (1977) have confirmed the superiority of this phenomenon in discrete and non-stop time Markov procedures. They (and others) have additionally given an important and adequate for a (temporally homogeneous) Markov technique to have monotone transition operators. even if such methods could be stochas­ tically monotone as outlined above, now depends upon the preliminary nation distribution. stipulations in this distribution for stochastic mono tonicity at the complete time axis to be successful got too by way of KEILSON and KESTER (1977).

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According to the above theorem. necessary and sufficient that ~*(O) ~O and ~*(O) ~ Q. 6) one sees that The next theore~ is now easily verified. * * n-O. I ••••••• ) be bounded. 4. Let the sequence (~/wn: natural. birth-death process {X* (t): 0 ~ t < ... ) iff (q*/w*) is non-decreasing and qo* ~ q*/w* for some n. 3 Properties of E(t) We are interested in the problem whether a natural birth-death process with an arbitrary· initial distribution is strictly stochastically monotone on some interval or not.

6. The fUnation Illp - Rs(bl(p),p) has at most s positive real zeros. PROOF. 2. 7) that for n,; s the function (/p)nQn(bl(p),P) is a polynomial of degree n in (/p) s-I Qs_I(bl(p),p) Illp. Hence the equation I s - (rp) Qs(bl(p),p) = ° has at most s positive roots. The result follows immediately. 8. 7. Rs(X'P) < -I/Ip for x ~bl(P) and p > 0 8uffiaientZy smaLz.. PROOF. Let 0 < P < 1/16s 2 and x ~ b l (p) .. A(I- l/lp)2. 12), Rn+1 (x,p) - 1- x/A + nIps - n/psRn(x,P) < < 1-(I-I/lp)2 + nIps + n/s/p- • (2 + n/s)/Ip - (I - n/s) /p < < -I/lp + (4 -(l-n/s)/Ip)/Ip ,;; -I/Ip , since for n < s, (1- n/s)/Ip ~ I/s/p ~ 4.

I) vn+IQn+l(x) - Q;+I(x) - Q:(x) ; (ii) -xQ*(x) - An vn (Qn +I(x) - Qn (x». n PROOF. (x) - (Ra(x), RI(x), ••••• ) . Then ~(x) - IT -I VTg * (x). (x). (x) - g(x}. (ii): Similarly. o 26 The polynomials ~(x) are orthogonal with respect to the spectral function ~, while the polynomials ~(x) are orthogonal with respect to the spectral function ~*. The relation between ~ and ~* was given by KARLIN and McGREGOR (1957 a ), lemmas 2 and 3, as follows (in their terminology ~(x) • Hn+l(x)/-x). 3. (i) (U) fx d~ * m x " o A~ o.

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