Explaining the Joint Distribution of T1,T2,...,Tn given N(t)=n

In summary, the conversation discusses a Poisson process of rate λ and the relationship between the time intervals [0,t1], (t1,t2],..., (tn-1,tn], and the number of events N(t) occurring in those intervals. It is noted that for a fixed t, N(t) must equal n and that the event {T1≤t1, T2≤t2,...,Tn≤tn, and N(t)=n} occurs if and only if exactly one event occurs in each of the intervals [0,t1], (t1,t2],..., (tn-1,tn], and no events occur in (tn,t]. This is important in finding the joint density
  • #1
kingwinner
1,270
0
Let {N(t): t≥0} be a Poisson process of rate λ.
We are given that for a fixed t, N(t)=n.
Let Ti be the time of the ith event, i=1,2,...,n.

Then the event {T1≤t1, T2≤t2,...,Tn≤tn, and N(t)=n} occurs if and only if exactly one event occurs in each of the intervals [0,t1], (t1,t2],..., (tn-1,tn], and no events occur in (tn,t].
=====================================

I don't understand the 'exactly one' part.
For example, T2≤t2 just says that T2 is less than or equal to t2, and T2 can very possibly be less than t1 as well, right? (since it did NOT say that T2 MUST be larger than t1) In this case, we would then have more than one event occurring in [0,t1]. Why is this not allowed? I don't get it...

Can someone please explain? I would really appreciate it!
 
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  • #2
Would N(t)=n be satisfied if T2 < t1?
 
  • #3
It seems to be a definitional choice that serves some purpose in the rest of the problem.
 
  • #4
EnumaElish said:
Would N(t)=n be satisfied if T2 < t1?

I think so! We can possibly have all n points in the interval [0,t1].
 
  • #5
The book is trying to prove that the joint density function of T1,T2,...,Tn given that N(t)=n is given by (n!)/(t^n), 0<t1<t2<...<t_n<t

They are first trying to find the joint distribution function for 0<t1<t2<...<t_n<t.
i.e. P(T1≤t1,...Tn≤tn |N(t)=n)
and they commented that "the event {T1≤t1, T2≤t2,...,Tn≤tn, and N(t)=n} occurs if and only if exactly one event occurs in each of the intervals [0,t1], (t1,t2],..., (tn-1,tn], and no events occur in (tn,t". This is where I got totally confused...

Can someone please help?
 

Related to Explaining the Joint Distribution of T1,T2,...,Tn given N(t)=n

1. What is a Poisson counting process?

A Poisson counting process is a type of stochastic process that models the occurrence of random events over time or space. It is named after the French mathematician Siméon Denis Poisson and is often used in fields such as statistics, physics, and finance.

2. How is a Poisson counting process defined?

A Poisson counting process is defined by three key characteristics: the events occur randomly and independently, the rate of occurrence is constant, and the number of events that occur in a given time or space interval follows a Poisson distribution.

3. What is the difference between a Poisson counting process and a Poisson distribution?

A Poisson counting process models the occurrence of events over time or space, while a Poisson distribution models the number of events that occur in a given interval. In other words, a Poisson counting process is a sequence of random variables following a Poisson distribution.

4. What are the applications of a Poisson counting process?

A Poisson counting process has many applications in various fields, such as modeling the number of customers arriving at a store, the number of earthquakes in a region, or the number of mutations in DNA. It is also used in queuing theory, inventory management, and reliability analysis.

5. How is a Poisson counting process different from other stochastic processes?

A Poisson counting process is different from other stochastic processes in that the events occur independently and at a constant rate. Other processes, such as the Wiener process or the Markov process, may have different characteristics, such as dependent events or variable rates of occurrence.

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