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Arrival theorem

theorem

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Record originEnglish Wikipedia
Text licenseCC BY-SA 4.0
Source revisionDec 8, 2025
Entity authorityQ283690
Source-derived summary

In queueing theory, a discipline within the mathematical theory of probability, the arrival theorem (also referred to as the random observer property, ROP or job observer property) states that "upon arrival at a station, a job observes the system as if in steady state at an arbitrary instant for the system without that job."

Overview

The arrival theorem always holds in open product-form networks with unbounded queues at each node, but it also holds in more general networks. A necessary and sufficient condition for the arrival theorem to be satisfied in product-form networks is given in terms of Palm probabilities in Boucherie & Dijk, 1997. A similar result also holds in some closed networks. Examples of product-form networks where the arrival theorem does not hold include reversible Kingman networks and networks with a delay protocol.

Mitrani offers the intuition that "The state of node i as seen by an incoming job has a different distribution from the state seen by a random observer. For instance, an incoming job can never see all 'k jobs present at node i, because it itself cannot be among the jobs already present."

Theorem for arrivals governed by a Poisson process

For Poisson processes the property is often referred to as the PASTA property (Poisson Arrivals See Time Averages) and states that the probability of the state as seen by an outside random observer is the same as the probability of the state seen by an arriving customer. The property also holds for the case of a doubly stochastic Poisson process where the rate parameter is allowed to vary depending on the state.

Theorem for Jackson networks

In an open Jackson network with m queues, write

n

=

(

n

1

,

n

2

,

,

n

m

)

{\textstyle \mathbf {n} =(n_{1},n_{2},\ldots ,n_{m})}

for the state of the network. Suppose

π

(

n

)

{\textstyle \pi (\mathbf {n} )}

is the equilibrium probability that the network is in state

n

{\textstyle \mathbf {n} }

. Then the probability that the network is in state

n

{\textstyle \mathbf {n} }

immediately before an arrival to any node is also

π

(

n

)

{\textstyle \pi (\mathbf {n} )}

.

Editorial summary

This brief starts where responsible research should: with the source description of “Arrival theorem” as theorem. Everything that follows is an evidence route, not borrowed authority.

Editorial reviewA dependable orientation record for establishing vocabulary, names and a first evidence trail. The current lead gives the account dated anchors—1997—that can be checked directly. The selected authority fields contribute no independent date. The account is most persuasive where Arrival and theorem can be independently traced.
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Vocabulary and entity names are the principal evidence signals here, because they determine the precision of every later search. The source revision retrieved here is dated Dec 8, 2025. The linked authority identifier is Q283690. None of the 0 selected statements returned an explicit reference. The first chronological checks are 1997.

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This entry incorporates text from Arrival theorem” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.