IEOR 6711 : Introduction to Renewal Theory II
نویسنده
چکیده
Here we will present some deeper results in renewal theory such as a central limit theorem for counting processes, stationary versions of renewal processes, renewal equations, the key renewal theorem, weak convergence. 1 Central limit theorem for counting processes Consider a renewal process {t n : n ≥ 1} with iid interarrival times X n = t n − t n−1 , n ≥ 0, such that 0 < E(X) = 1/λ < ∞ and 0 < σ 2 = V ar(X) < ∞. Let N (t) = max{n : t n ≤ t}, t ≥ 0, denote the counting process. =⇒ denotes convergence in distribution (weak convergence). Z denotes a rv with a N (0, 1) distribution (standard unit normal);
منابع مشابه
1 IEOR 6711 : Introduction to Renewal Theory
with tn−→∞ as n−→∞. With N(0) def = 0, N(t) max{n : tn ≤ t} denotes the number of points that fall in the interval (0, t], and {N(t) : t ≥ 0} is called the counting process for ψ. (If t1 > t, then N(t) def = 0.) If the tn are random variables then ψ is called a random point process. We sometimes allow a point at the origin and define t0 def = 0. Xn = tn − tn−1, n ≥ 1 is called the nth interarri...
متن کاملIEOR 3106: Introduction to Operations Research: Stochastic Models Fall 2012, Professor Whitt OPTIONAL EXTRA INFORMATION ABOUT RENEWAL THEORY The Renewal Function, The Renewal Equation and Renewal Theorems
These notes cover material beyond the topics covered in IEOR 3106. We refer to the books by [Asmussen (2003)] and [Ross (1996)] (a more advanced book than the one we use). First, renewal theory is about renewal processes. A key quantity is the renewal function. The renewal function is important because it is a key component of the solution of the renewal equation. In applications, we are often ...
متن کاملSigman 1 IEOR 6711 : Notes on the Poisson Process
We view t as time and view tn as the n th arrival time (although there are other kinds of applications in which the points tn denote locations in space as opposed to time). The word simple refers to the fact that we are not allowing more than one arrival to ocurr at the same time (as is stated precisely in (1)). In many applications there is a “system” to which customers are arriving over time ...
متن کامل1 IEOR 6711 : Continuous - Time Markov Chains
A Markov chain in discrete time, {Xn : n ≥ 0}, remains in any state for exactly one unit of time before making a transition (change of state). We proceed now to relax this restriction by allowing a chain to spend a continuous amount of time in any state, but in such a way as to retain the Markov property. As motivation, suppose we consider the rat in the open maze. Clearly it is more realistic ...
متن کاملIEOR 8100-001: Learning and Optimization for Sequential Decision Making 02/03/16 Lecture 5: Thomposon Sampling (part II): Regret bounds proofs
We describe the main technical difficulties in the proof for TS algorithm as compared to the UCB algorithm. In UCB algorithm, the suboptimal arm 2 will be played at time t, if its UCB value is higher, i.e. if UCB2,t−1 > UCB1,t−1. If we have pulled arm 2 for some amount of times Ω( log(T ) ∆2 ), then with a high probability this will not happen. This is because after n2,t ≥ Ω(log(T )/∆), using c...
متن کامل