Why aren't those two probabilities equal (exponential dist)

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Discussion Overview

The discussion revolves around the relationship between probabilities in the context of exponential distributions and their application in modeling failure times. Participants explore the implications of strict convexity in exponential functions and the potential for finding an equivalent distribution for a composite system of objects A and B.

Discussion Character

  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • Some participants express concern over the interpretation of failure time and the parameterization of the exponential distribution.
  • It is noted that the exponential function is strictly convex, leading to the inequality between expected values and the exponential of expected values.
  • One participant questions whether a new parameter ##\lambda_3## can be defined as a function of ##\lambda_1## and ##\lambda_2## to satisfy certain conditions related to cumulative distribution functions (CDFs).
  • Another participant asserts that the existence of ##\lambda_3## is contingent upon ##\lambda_1## equaling ##\lambda_2## for any probability ##0 < p < 1##.
  • A participant expresses skepticism about the relevance of Poisson splitting to the original problem, while still considering the possibility of approximating or reducing composite probabilities into a simplified equivalent system.
  • It is concluded that the distribution function for the equivalent object C cannot be expressed in the form of a single exponential distribution, indicating a limitation in modeling with a Poisson process.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the possibility of finding an equivalent object C that models A and B. There are competing views regarding the applicability of Poisson processes and the conditions under which an equivalent distribution might exist.

Contextual Notes

Participants acknowledge limitations in their discussion, particularly regarding the assumptions needed for the existence of ##\lambda_3## and the implications of strict convexity on the relationships between the distributions.

Hamad
I don't know how to type latex in the forums so I took a picture and uploaded it
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I'm a little concerned by the wording "failure time of A is ##\lambda_1##. I trust you are not saying that is the expected time until failure, since that is in fact the inverse of this, i.e. ##\frac{1}{\lambda}##.

I believe you're saying that exponentially distributed A is parameterized by ##\lambda_1##.

Note that the exponential function is strictly convex over reals. (How do we know this?)

This means that if there is any variation at all in some random variable ##Y## (note in this case you could say that ##Y## takes on a value of ##\lambda_1## with probability p and ##\lambda_2## with probability 1-p, and assert that ##\lambda_1 \neq \lambda_2## and that ##0 \lt p \lt 1## but the idea more generally is that ##Y## is a random variable that is non-degenerate (which in this case I mean it is non deterministic and has a first moment).

Thus we have ##exp\big(E[Y]\big) \lt E\big[exp(Y)\big]## for strictly convex functions like the exponential function.

Do you see how this means your ##C ## cannot be equal to your convex combination of ##A## and ##B##? (Again setting aside cases where ##C## is equal to just ##A## or just ##B##, i.e. when p =0 or p =1 or ##\lambda_1 = \lambda_2##)

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n.b. your instincts are pretty good here though. There is something pretty similar that you can do in terms of splitting and combining Poisson processes. (on the real line:) Poisson processes can be interpreted as a counting process with inter-arrival times that are exponentially distributed. Put differently, the exponential distribution is a building block for the Poisson and you can do splitting and combining there. You may want to read up on Poisson processes.

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For Latex in the forums, see:

https://www.physicsforums.com/help/latexhelp/

It's basically like what you'd think, but you need to encapsulate things in double hashtags instead of single dollars. Plus some BB code or markdown collides with a couple LaTeX commands, but again basically what you'd think.
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and welcome to PF!
 
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Oh I know about poisson processes, and yes I meant ##\lambda## is the rate of failture, but anyway yeah we did study about that inequality in my old course, but I am still curious does that exist no ##\lambda_3=f(\lambda_1,\lambda_2)##to satisfy what I am trying to do such that F(##\lambda_3##) is the cdf of x
 
Hamad said:
Oh I know about poisson processes, and yes I meant ##\lambda## is the rate of failture, but anyway yeah we did study about that inequality in my old course, but I am still curious does that exist no ##\lambda_3=f(\lambda_1,\lambda_2)##to satisfy what I am trying to do such that F(##\lambda_3##) is the cdf of x

You may want to re-read parts of my post. By strict convexity the ##\lambda_3## you are seeking exists iff ##\lambda_1 = \lambda_2## for any ##0\lt p \lt 1##.

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edit: I have lingering concerns that I'm answering a slightly different problem than what you really want, and I keep thinking that Poisson splitting is the solution.
 
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StoneTemplePython said:
You may want to re-read parts of my post. By strict convexity the ##\lambda_3## you are seeking exists iff ##\lambda_1 = \lambda_2## for any ##0\lt p \lt 1##.

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edit: I have lingering concerns that I'm answering a slightly different problem than what you really want, and I keep thinking that Poisson splitting is the solution.
I wouldn't say you are answering a different problem than what I want but I also don't think poisson splitting is the answer. My objective is to know whether I can replace objects A,B with an equivalent object C to model the effects. As for example in circuit elements you can have an equivalent circuit describing your total system, and you know my background is electrical, so I was curious whether I could have an equivalent experiment of object C to model A and B, but I guess again non-linearity is probably why it's not possible, but I will still look for ways out of interest to even approximate/reduce problems with these composite probabilities into a simplified equivalent system if possible.
 
Hamad said:
whether I can replace objects A,B with an equivalent object C
As you found, C would have the distribution function ##1-pe^{-\lambda_1x}-(1-p)e^{-\lambda_2x}##. That cannot be written as ##1-e^{-\lambda_3x}##, no matter what you choose for ##\lambda_3##. So C cannot be Poisson.
 

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