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

In summary, the conversation discusses the failure time of a parameterized exponentially distributed object, and the concept of strict convexity in relation to splitting and combining Poisson processes. The possibility of replacing objects A and B with an equivalent object C is also explored, but it is found that C cannot be Poisson.
  • #1
Hamad
I don't know how to type latex in the forums so I took a picture and uploaded it
1x6oYuz.png
 

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  • #2
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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  • #3
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
 
  • #4
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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  • #5
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.
 
  • #6
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.
 

1. Why do we use exponential distribution?

Exponential distribution is commonly used in scientific research because it provides a mathematical model for situations where the time between events follows a specific pattern. This makes it useful for analyzing data related to waiting times, failure rates, and survival rates.

2. How is exponential distribution different from other probability distributions?

Exponential distribution differs from other probability distributions in that it is a continuous distribution, meaning that the random variable can take on any value within a certain range. It also has a constant failure rate, meaning that the probability of an event occurring does not change over time.

3. Why are two probabilities not always equal in exponential distribution?

Two probabilities are not always equal in exponential distribution because the distribution is affected by the rate parameter, which determines the shape of the curve. This means that the probabilities for different events can vary, depending on the value of the rate parameter.

4. Can exponential distribution be used to model real-world phenomena?

Yes, exponential distribution can be used to model real-world phenomena such as radioactive decay, the time between phone calls, or the time between natural disasters. However, it is important to note that it is an idealized model and may not always accurately represent real-world data.

5. How can we calculate probabilities in exponential distribution?

To calculate probabilities in exponential distribution, we can use the formula P(X > x) = e^(-λx), where x is the time or value of interest and λ is the rate parameter. We can also use statistical software or tables to find probabilities for specific values or ranges of values.

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