Poisson Process Conditional Distribution

In summary, the problem involves finding the CDF F_X{}_t{}_|{}_X{}_t{}_+{}_Y{}_t{}_={}_n(x) for the Poisson processes X_t and Y_t with rates a and b, respectively. Using the definition of conditional probability, the solution involves calculating P(X_t≤m|X_t+Y_t=n) for a given value of m.
  • #1
jiml
3
0

Homework Statement


[itex]X_t[/itex] and [itex]Y_t[/itex] are poisson processes with rates [itex]a[/itex] and [itex]b[/itex]

[itex]n = 1,2,3...[/itex]Find the CDF [itex]F_X{}_t{}_|{}_X{}_t{}_+{}_Y{}_t{}_={}_n(x)[/itex]

Homework Equations


The Attempt at a Solution


[itex]F_X{}_t{}_|{}_X{}_t{}_+{}_Y{}_t{}_={}_n(x)[/itex]

[itex]=P(X_t<x|X_t+Y_t=n)[/itex]

[itex]=\frac{P(X_t<x,X_t+Y_t=n)}{P(X_t+Y_t=n)}[/itex]

Not sure from here, but here goes:

[itex]=\frac{P(Y_t>n-x)}{P(X_t+Y_t=n)}[/itex]

[itex]=1-\frac{P(Y_t<=n-x)}{P(X_t+Y_t=n)}[/itex]
Not sure if doing correctly.
 
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  • #2
jiml said:

Homework Statement


[itex]X_t[/itex] and [itex]Y_t[/itex] are poisson processes with rates [itex]a[/itex] and [itex]b[/itex]

[itex]n = 1,2,3...[/itex]


Find the CDF [itex]F_X{}_t{}_|{}_X{}_t{}_+{}_Y{}_t{}_={}_n(x)[/itex]


Homework Equations





The Attempt at a Solution


[itex]F_X{}_t{}_|{}_X{}_t{}_+{}_Y{}_t{}_={}_n(x)[/itex]

[itex]=P(X_t<x|X_t+Y_t=n)[/itex]

[itex]=\frac{P(X_t<x,X_t+Y_t=n)}{P(X_t+Y_t=n)}[/itex]

Not sure from here, but here goes:

[itex]=\frac{P(Y_t>n-x)}{P(X_t+Y_t=n)}[/itex]

[itex]=1-\frac{P(Y_t<=n-x)}{P(X_t+Y_t=n)}[/itex]



Not sure if doing correctly.

Since X and Y are counting processes, you should probably avoid using the letter 'x' for values of them, so instead, should use something like ##F_{X_t|X_t + Y_t = n}(m).## Note also that the standard definition of a cdf involves '≤', not '<', so
[tex]F_{X_t|X_t + Y_t = n}(m) = P(X_t \leq m|X_t+Y_t = n).[/tex]
 
  • #3
Can someone please help me with my solution, whether I am on the right track in my steps to get to a solution. Thanks
 
  • #4
jiml said:
Can someone please help me with my solution, whether I am on the right track in my steps to get to a solution. Thanks

All you have done is use the definition of conditional probability; you are nowhere near the final solution.
 
  • #5
ok,thanks
 
Last edited:

Related to Poisson Process Conditional Distribution

1. What is a Poisson Process Conditional Distribution?

A Poisson Process Conditional Distribution is a probability distribution that describes the number of events that occur in a given time interval, given that a certain number of events have already occurred. It is used to model the occurrence of events that happen randomly and independently over time.

2. How is a Poisson Process Conditional Distribution different from a regular Poisson Distribution?

A regular Poisson Distribution describes the probability of a certain number of events occurring in a given time interval, without any consideration for previous events. A Poisson Process Conditional Distribution takes into account the number of events that have already occurred and adjusts the probabilities accordingly.

3. What is the formula for calculating a Poisson Process Conditional Distribution?

The formula for calculating a Poisson Process Conditional Distribution is P(k;λ, t) = (e^-λt(λt)^k) / k!, where k is the number of events, λ is the average rate of events per unit time, and t is the time interval. This formula is derived from the regular Poisson Distribution formula and includes the conditional factor of e^-λt.

4. In what real-world situations can a Poisson Process Conditional Distribution be applied?

A Poisson Process Conditional Distribution can be applied to a variety of real-world situations, such as modeling the number of customers arriving at a store, the number of accidents on a highway, or the number of radioactive particles emitted from a substance. It can also be used in finance to model the number of trades executed per minute or in biology to model the number of mutations in a DNA sequence.

5. What are the assumptions made when using a Poisson Process Conditional Distribution?

The assumptions made when using a Poisson Process Conditional Distribution include that the events occur randomly and independently, the average rate of events per unit time is constant, and the probability of more than one event occurring in an infinitesimal time interval is negligible. Additionally, the events must be non-overlapping and the time interval must be fixed and measurable.

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