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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
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.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.
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.Hamad said:whether I can replace objects A,B with an equivalent object C
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.
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.
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.
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.
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.