How Does the Probability of Failure Change in Synchronous Machine Operations?

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  • #1
topgun08
15
0
Question:
Two faulty machines, M1 and M2, are repeatedly run synchronously in parallel (i.e., both machines execute
one run, then both execute a second run, and so on). On each run, M1 fails with probability p1 and M2 with
probability p2, all failure events being independent. Let the random variables X1, X2 denote the number of
runs until the first failure of M1, M2 respectively; thus X1, X2 have geometric distributions with parameters
p1, p2 respectively.
Let X denote the number of runs until the first failure of either machine. Show that X also has a geometric
distribution, with parameter p1 + p2 − p1p2

Attempt at an answer:
X1 has a geometric distribution of (1-p1)^i-1 * p1
X2 has a geometric distribution of (1-p2)^i-1 * p2

I'm confused an don't know how to proceed. Any help is appreciated.
 
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  • #2
Since this problem doesn't have anything to do with "Number Theory", I have it from that category.
 
  • #3
topgun08 said:
Question:
Two faulty machines, M1 and M2, are repeatedly run synchronously in parallel (i.e., both machines execute
one run, then both execute a second run, and so on). On each run, M1 fails with probability p1 and M2 with
probability p2, all failure events being independent. Let the random variables X1, X2 denote the number of
runs until the first failure of M1, M2 respectively; thus X1, X2 have geometric distributions with parameters
p1, p2 respectively.
Let X denote the number of runs until the first failure of either machine. Show that X also has a geometric
distribution, with parameter p1 + p2 − p1p2

Attempt at an answer:
X1 has a geometric distribution of (1-p1)^i-1 * p1
X2 has a geometric distribution of (1-p2)^i-1 * p2

I'm confused an don't know how to proceed. Any help is appreciated.

Look at the probability that neither M1 nor M2 has failed by n runs, for n = 1, 2, 3, ... .

RGV
 

Related to How Does the Probability of Failure Change in Synchronous Machine Operations?

1. What is the Geometric Distribution?

The Geometric Distribution is a probability distribution that models the number of trials needed to obtain a success in a series of independent Bernoulli trials. It is often used to analyze the probability of success or failure in repeated experiments.

2. How is the Geometric Distribution different from the Binomial Distribution?

The Binomial Distribution models the number of successes in a fixed number of trials, while the Geometric Distribution models the number of trials needed to obtain a success. In other words, the Binomial Distribution is concerned with the number of successes, while the Geometric Distribution is concerned with the number of trials.

3. What are the characteristics of the Geometric Distribution?

The Geometric Distribution has the following characteristics:

  • The trials must be independent.
  • Each trial has only two possible outcomes: success or failure.
  • The probability of success (p) must remain constant for each trial.
  • The number of trials (n) is not fixed, but continues until the first success occurs.

4. How is the Geometric Distribution used in real life?

The Geometric Distribution has many applications in real life. It can be used to analyze the probability of success or failure in repeated experiments, such as coin tosses or medical trials. It is also used in quality control to determine the probability of a defective product occurring after a certain number of trials.

5. What is the formula for calculating the Geometric Distribution?

The formula for calculating the Geometric Distribution is P(X=x) = (1-p)x-1 * p, where P(X=x) is the probability of obtaining x trials before the first success, p is the probability of success, and x is the number of trials. This formula can also be written as P(X=x) = qx-1 * p, where q is the probability of failure (1-p).

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