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 sta- bility deriving from, respectively, the ergodic theorem for Markov chains and the re- newal 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