In a recent paper [1] in the American Mathematical Monthly, the authors look at
several distinct approaches to studying the convergence and limit L of a sequence (xn)
given by a linear recurrence. Our approach is to use probability to study the same
sequence. The nth term xn of the sequence is expressed as the average value of a
random process observed at time n. The convergence of (xn) then follows from the
asymptotic stability of the underlying random process, meaning intuitively that the
random process approaches the same equilibrium for any initial value.
We give two different probabilistic interpretations of (xn) with the asymptotic stability
deriving from, respectively, the ergodic theorem for Markov chains and the renewal
theorem for random walks.
To be more concrete, fix two sequences of real numbers (αk )
m
k=1 and (ak )
m
k=1. For
1 ≤ n ≤ m let xn = an, while for n > m let
xn = αm xn−1 + αm−1xn−2 +···+ α1xn−m.
In a recent paper [1] in the American Mathematical Monthly, the authors look atseveral distinct approaches to studying the convergence and limit L of a sequence (xn)given by a linear recurrence. Our approach is to use probability to study the samesequence. The nth term xn of the sequence is expressed as the average value of arandom process observed at time n. The convergence of (xn) then follows from theasymptotic stability of the underlying random process, meaning intuitively that therandom process approaches the same equilibrium for any initial value.We give two different probabilistic interpretations of (xn) with the asymptotic stabilityderiving from, respectively, the ergodic theorem for Markov chains and the renewaltheorem for random walks.To be more concrete, fix two sequences of real numbers (αk )mk=1 and (ak )mk=1. For1 ≤ n ≤ m let xn = an, while for n > m letxn = αm xn−1 + αm−1xn−2 +···+ α1xn−m.
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