Why is waiting time memoryless? (in Stochastic Processes)

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Waiting time is considered memoryless in certain stochastic processes, meaning that the probability of an event occurring in the future is independent of how much time has already elapsed. For example, if a light bulb has been functioning for 10 hours, the likelihood of it continuing to work for another 5 hours remains the same as it was initially. This concept applies specifically to processes defined as memoryless, such as exponential distributions. However, not all processes exhibit this property; some may indeed have a higher likelihood of failure as time progresses. Understanding this distinction is crucial in the study of stochastic processes.
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I am learning Stochastic Processes right now. Can someone some explain why waiting time is memoryless? Say, if a light bulb has been on for 10 hours, the probability that it will be on for another 5 is the same as the 1st 5 hours.
It doesn't make sense to me, because the longer you use it, the more likely it will break down. No?
 
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Waiting time is memory-less for processes that have been defined to be such. As you observed there are processes which may not be.
 
got it, thanks!
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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