Finding probability related to Poisson and Exponential Distribution

In summary, the Poisson distribution is used to model the number of events occurring within a fixed interval of time or space, characterized by a known average rate (λ). It is particularly useful for rare events. The probability of observing k events is given by the formula P(X=k) = (λ^k * e^(-λ)) / k!, where e is the base of natural logarithms. The Exponential distribution, on the other hand, models the time between successive events in a Poisson process and is defined by the rate parameter (λ). Its probability density function is f(x) = λ * e^(-λx) for x ≥ 0. Together, these distributions help in calculating probabilities related to event occurrences and their timing
  • #1
songoku
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Homework Statement
Please see below
Relevant Equations
Poisson Distribution

Exponential Distribution
1699953819487.png


My attempt:
(i) ##\lambda =3##

(ii)
(a) ##P(N_{2} \geq 1=1-P(N_{2} =0)=1-e^{-6} \frac{(-6)^0}{0!}=0.997##

(b) ##P(N_{4} \geq 3)=1-P(N_{4} \leq 2)=0.999##

(c) ##P(N_{1} \geq 2) = 1-P(N_{4} \leq 1)=0.8##

Do I even understand the question correctly for part (i) and (ii)?(iii) The expectation of exponential distribution is ##\frac{1}{\lambda}## so the answer is ##\frac{1}{3}##?

Thanks
 
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  • #2
songoku said:
Do I even understand the question correctly for part (i) and (ii)?
Yes, I think so, but there are a couple of errors in notation.
In (a), (-6)0 should be 60.
In (b), N4 should be N1.
Otherwise it looks correct.
For (iii), what are you calculating the expectation of?
 
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  • #3
Here is a simple sanity check of your answer for (ii)(a):
On average, there are three failures per unit time. Your answer for part (ii)(a) is that it is almost certain that there are no failures in two time units. Does that seem right?
 
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  • #4
D'oh! should have seen that!
 
  • #5
FactChecker said:
Here is a simple sanity check of your answer for (ii)(a):
On average, there are three failures per unit time. Your answer for part (ii)(a) is that it is almost certain that there are no failures in two time units. Does that seem right?
Yeah, it does not make sense.

mjc123 said:
there are a couple of errors in notation.

In (b), N4 should be N1.
Sorry I don't understand why it should be N1, maybe I am misunderstanding something again.

N = number of failures
t = time interval

Is that correct?Revised attempt:
(ii)
(a) ##P(N_{2} = 0)=e^{-6}=0.0025##

(b) still the same as post #1

(c) still the same as post #1, with a revision ##N_{4}## should be ##N_{1}##
Oh wait, maybe you mean the error in notation is for (c) instead of (b)? @mjc123

mjc123 said:
For (iii), what are you calculating the expectation of?
Oh my god, the expectation of Sn

$$E(S_{n}-S_{n-1})=\frac{1}{\lambda}$$
$$E(S_{n})-E(S_{n-1})=\frac{1}{\lambda}$$
$$E(S_{n})=\frac{1}{\lambda}+E(S_{n-1})$$

Not even sure this is the correct approach

Thanks
 
  • #6
Oh no, yes I meant c not b. I must have been half asleep that day. Apologies for confusion.
(iii), you're on the right track.
S0 = 0
E(S1 - S0) = 1/λ
E(S1) = 1/λ
E(S2 - S1) = 1/λ
E(S2) = ?
etc.
 
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  • #7
I understand. Thank you very much for the help mjc123 and FactChecker
 

FAQ: Finding probability related to Poisson and Exponential Distribution

What is the Poisson distribution and when is it used?

The Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring within a fixed interval of time or space. It is used when the events are independent, occur at a constant rate, and the probability of more than one event occurring in an infinitesimally small time interval is negligible.

How do you calculate the probability of a specific number of events using the Poisson distribution?

The probability of observing exactly k events in a Poisson distribution is given by the formula: P(X = k) = (λ^k * e^(-λ)) / k!, where λ is the average rate of occurrence, k is the number of events, and e is the base of the natural logarithm (approximately equal to 2.71828).

What is the Exponential distribution and what is it used for?

The Exponential distribution is a continuous probability distribution that describes the time between events in a Poisson process. It is used to model the time until the next event occurs, given that events happen continuously and independently at a constant average rate.

How do you find the probability of an event occurring within a certain time frame using the Exponential distribution?

The probability that an event occurs within time t in an Exponential distribution is given by the cumulative distribution function (CDF): P(T ≤ t) = 1 - e^(-λt), where λ is the rate parameter (the inverse of the mean time between events) and t is the time frame.

How are the Poisson and Exponential distributions related?

The Poisson and Exponential distributions are closely related in that the Poisson distribution describes the number of events in fixed intervals of time or space, while the Exponential distribution describes the time between consecutive events in a Poisson process. Specifically, if events occur according to a Poisson process with rate λ, then the time between events follows an Exponential distribution with parameter λ.

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