Newton-Raphson method in non-homogeneous poisson process

In summary, the conversation discusses the application of the non-homogeneous Poisson process to determine the probability of extinction by each generation. The use of Newton-Raphson method is suggested, and an equation is provided for part (e). The conversation also touches on the iteration scheme for NR and clarifies that the goal is to solve the equation (3 + 6t)*exp(-2t) = 0.051293.
  • #1
tottijohn
3
0

Homework Statement


The rate of occurrence of events in a non-homogeneous Poisson process is given by: λ(t)=12t e-2t.

(c) Find the p.d.f. of the time until the first event occurs after time t = 1.
(e) After what time is it 95% certain that no further events will occur?

Homework Equations


λ(t)=12t e-2t

The Attempt at a Solution


After using integration by parts, I found μ(t) = -3e-2t(2t+1) to solve other parts of this question. I know part (e) requires the use of Newton-raphson method but I have no idea how to go about. Any help will really be appreciated, thanks.
 
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  • #2
Newton-Raphson is a numerical method to estimate the zero of a function (i.e. find x such that f(x) = 0) to some desired accuracy. Are you having difficulty defining f(x) or applying NR or both?
 
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  • #3
Both.

I know the probability of extinction by each generation can be calculated using Newton_Raphson method given p.g.f. and finding p and q, but I am not sure how to apply to this question.
 
  • #4
tottijohn said:
Both.

I know the probability of extinction by each generation can be calculated using Newton_Raphson method given p.g.f. and finding p and q, but I am not sure how to apply to this question.

Are you trying to solve an equation? What IS the equation? Write it down in detail, so we have the basis for offering some advice.

RGV
 
  • #5
I have managed to solve (c).

Here is the equation i got for (e):
g(t) = (3 + 6t)e^-2t - 0.0513 = 0
g'(t) = -12te^(-2t)
tn+1 = tn - [(3 + 6tn)e^-2tn - 0.0513]/-12tne^(-2tn)

I am not sure how to proceed with NR to get the time of 95% certain?
 
  • #6
tottijohn said:
I have managed to solve (c).

Here is the equation i got for (e):
g(t) = (3 + 6t)e^-2t - 0.0513 = 0
g'(t) = -12te^(-2t)
tn+1 = tn - [(3 + 6tn)e^-2tn - 0.0513]/-12tne^(-2tn)

I am not sure how to proceed with NR to get the time of 95% certain?

Why the concentration on Newton-Raphson? Do you understand that you are just trying to solve the equation (3 + 6t)*exp(-2t) = 0.051293? Newton-Raphson (NR) is one way to do it, but there are many others. However, if you do want to use NR to solve the equation g(t) = 0, you just start with some initial guess, t0, then use the iteration scheme
tn+1 = tn - g(tn)/g'(tn). What is stopping you from doing this?

RGV
 

1. What is the Newton-Raphson method in non-homogeneous Poisson process?

The Newton-Raphson method is an iterative algorithm used to approximate the roots of a function. In the context of non-homogeneous Poisson processes, it is used to estimate the unknown parameters of the process based on observed data.

2. How does the Newton-Raphson method work in non-homogeneous Poisson process?

The method starts with an initial guess for the parameters and uses the derivative of the log-likelihood function to update the estimates in each iteration. This process is repeated until the estimates converge to a stable solution.

3. When is the Newton-Raphson method appropriate for non-homogeneous Poisson process?

The Newton-Raphson method is appropriate when the log-likelihood function is differentiable and the parameters can be estimated analytically. It is also useful when the number of observed events is large enough to ensure accurate estimation.

4. What are the advantages of using the Newton-Raphson method in non-homogeneous Poisson process?

The Newton-Raphson method is a fast and efficient way to estimate the parameters of a non-homogeneous Poisson process. It also provides unbiased estimates and can handle complex models and large datasets.

5. Are there any limitations to the Newton-Raphson method in non-homogeneous Poisson process?

One limitation is that the method may not converge if the initial guess is too far from the true values of the parameters. It also assumes that the log-likelihood function is well-behaved and may not work well with small sample sizes or rare events.

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