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Poisson Processes

     A Poisson process is a stochastic process that occurs over a certain time period. Within this time period, the Poisson process counts the occurrences of events. The Poisson process is a continuous time process and a type of Markov process. An example of a Poisson process can be thought of as a piece of Uranium slowly experiencing radioactive decay. The radioactive particles are emitted in accordance to a function of the remaining number of particles; emission rate declines as number of particles left declines. This situation is consistent with all the properties of a Poisson process. At time zero, there are no particles emitted. The intervals between emitted particles are independently distributed from one another. No counted occurrences are simultaneous. The probability of the particles emitted at any given interval is dependent only on the time interval. Lastly the probability distribution becomes a Poisson distribution. Poisson processes can be classified into a few types. These are homogeneous, non-homogeneous, spatial, and space-time.  

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Statistics: Poisson Process for demands for an item on a warehouse

Demands for an item are part of a poisson process and are made on a warehouse 3 times per day. What is the probability that on a given day this item is requested: a) more than 4 times per day b) more than 4 times in 3 days? The professor gave the answers as: a) 8.4% b) 94.5% .

How likely is it that exactly 1 customer will arrive in any 5 minutes? If there are no additional customers waiting (or walking up to the counter) when a particular customer begins her transaction, what is the probability that no one will be waiting in line when her transaction is finished?

People arrive a a particular sales counter at the rate of 6 in any 15 minute period and are served on a "first come first served" basis. Arrivals occur randomly throughout the hour and the arrival rate is the same during the entire business day. It takes 5 minutes to service a single customer. How likely is it that exactly 1

The Wait Time at a Fast Food Restaurant

Harry's Hamburger is a restaurant where customers form a single line for 1 cashier. The average numbers of arrivals is 1 per minute and the Poison distribution accurately defines this rate. The average time to serve a customer is 30 seconds, and the exponential distribution may be used to describe the distribution of the serv

Discrete and Continuous Probability Distributions

The manager for Select-a-Seat, a company that sells tickets to athletic games, concerts, and other events, has determined that the number of people arriving at the Broadway location on a typical day is Poisson distributed with a mean of 12 per hours. It takes approximately 4 minutes to process a ticket request. Thus, if customer

Stochastic Process Models

Spare Parts for an Alarm System. Consider an alarm system that operates continuously. The alarm system contains a minicomputer that is critical for the alarm system to function properly. Accordingly, a large number of spare minicomputers are maintained, so that the minicomputer can be instantaneously replaced whenever it fail

Poisson Process and Probability

Assume the accidents occur at random according to a Poisson process a rate of one every two days. a) What is the probability that exactly two accidents will occur in a particular two-day interval? b)What is the probability that one will have to wait at least two days to observe the next two accidents.

Queueing Systems

This exam covers customer queuing systems including arrival rates, Poisson processes, probability density functions, steady states, operator utilisation, machine efficiency, and exponentially distributed service times.

Poisson Probability Distribution - Receiving Telephone Calls

Telephone calls arrive at the rate of 48 per Hour at the reservation Desk of Card Airways a)Find the probability of receiving 3 calls in a 5-minute interval b)Find the probability of receiving 10 calls in 15-minutes c)Suppose that no calls are currently on hold. If the agent takes 5 minutes to complete processing the curren

Showing the Poisson Process

We have two independent Poisson processes, Z1(t) and Z2(t), with respective parameters r and s. They are running parallel. If Y(t) is the process from the overlay of Z1(t) and Z2(t), show that Y(t) is also a Poisson process. What is the rate of Y(t)?