Stochastic modelling, poisson process

In summary, a book of 600 pages with 240 typographical errors can be approximated using a poisson distribution with an error rate of 0.4 errors per page. The probability of three particular successive pages being error-free is e^(-1.2), but the answer in the back of the book is e^(-12), which may be an error. The actual probability is closer to .3, suggesting that the answer in the back of the book is incorrect.
  • #1
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Homework Statement


Suppose a book of 600 pages contains a total of 240 typographical errors. Develop a poisson approximation for the probability that three partiular successive pages are error-free.




The Attempt at a Solution



I say that the number of errors is poissondistributed with errorate 240/600=0.4 errors/pages.

The probability of 0 errors for 3 pages is:

(3*0.4)^0*e^(-3*0.4)/0!=e^(-1.2)

But the answer index says that the answer is e^(-12). That is, there should not be a dot there, why?
 
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  • #2
Looks right to me. It could be an error in the back of the book.
 
  • #3
Just as a gut check, if each page had a .4 chance of an error independently, the chance of no errors would be .63 = .216. Your answer numerically is .3 which is fairly close, while the answer in the back of the book says that the odds of these three pages having no errors in on the order of one in a million, which sounds pretty unreasonable
 

1. What is stochastic modelling?

Stochastic modelling is a mathematical approach used to study systems that involve a degree of randomness or uncertainty. It involves using probability theory to model and analyze the behavior of these systems over time.

2. What is a Poisson process?

A Poisson process is a type of stochastic process that models the occurrence of events over time. It is characterized by the assumption that the number of events occurring in a given time period is independent of the number of events that occurred in any other time period, and that the average rate of events is constant.

3. What are some real-world applications of stochastic modelling and Poisson processes?

Stochastic modelling and Poisson processes have many applications in various fields, including finance, economics, biology, and engineering. They are commonly used to model the stock market, analyze the spread of diseases, and predict customer arrivals in queuing systems.

4. How are stochastic modelling and Poisson processes related?

Stochastic modelling often involves the use of Poisson processes to model and analyze the behavior of random systems. Poisson processes are a fundamental component of stochastic modelling and are used to represent the occurrence of random events over time.

5. What are the advantages of using stochastic modelling and Poisson processes?

Stochastic modelling and Poisson processes allow for a more realistic representation of random systems and can provide insights into their behavior and performance. They also allow for the analysis of complex systems that cannot be easily modeled using traditional methods, leading to more accurate predictions and decision-making.

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