What is the best approach for calculating long-term probability using a PDF?

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To calculate long-term probabilities using a probability density function (PDF), it is essential to consider how the distribution may change over time. For a data series described by a log-normal distribution, one approach is to model the parameters as functions of time to account for potential shifts in the distribution's shape. Another method involves defining a metadistribution that combines two distributions with a weight that varies over time. The Fokker-Planck equation may also be relevant for this analysis, as it can describe the evolution of probability distributions over time. Understanding these methods is crucial for accurately assessing the probability of events over extended periods.
Drewau2005
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Hi

I posted the other day but I think I could have explained better what I am looking for, so hence this post.

I was wondering how you account for time when using a PDF to try and ascertain the probability of an event happening over very long periods of time?
If I have a data series which is described by a distribution, say log-normal and I want to work out the proabability of x being greater than y happening say by 30 years ? I guess it is allied to asking to what happens if you are using the area under the curve to look at probabilities when the distribution might change shape substantially over a long sweep of time and could be better described by a different equation.

Would you use the Fokker-Planck equation in this instance ?

Many thanks
D
 
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You could model the parameters of your distribution to be simple functions of time.
 
Or, you could define a metadistribution H(x) = w F(x) + (1-w) G(x) where 0 < w < 1 is a monotonic function of time.
 
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