complexity of a statistical model

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bmrick

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How difficult/time consuming would it be for a professional mathematician to model a temporal probability distribution of when an event will occur when the temporal history of that event occurring is known. for instance, I know thing A undergoes event B at time 0, 10, 15, 22, 28, etc. As a bonus, lets say we can also know that thing A must necessarily be in or out of certain categories which allow and deny certain time periods for event B.
I would Imagine we would want to assign a percentage chance that thing A is in each category, or out of each category, and use some sort of complex algorithm to superimpose those probability distributions on top of each other, am I correct?
 
bmrick said:
How difficult/time consuming would it be for a professional mathematician to model a temporal probability distribution of when an event will occur when the temporal history of that event occurring is known. for instance, I know thing A undergoes event B at time 0, 10, 15, 22, 28, etc. As a bonus, lets say we can also know that thing A must necessarily be in or out of certain categories which allow and deny certain time periods for event B.
I would Imagine we would want to assign a percentage chance that thing A is in each category, or out of each category, and use some sort of complex algorithm to superimpose those probability distributions on top of each other, am I correct?
Click to expand...
What is the exact text of the exercise and its instructions? Thank you! ;)
 
bmrick said:
How difficult/time consuming would it be for a professional mathematician to model a temporal probability distribution of when an event will occur when the temporal history of that event occurring is known. for instance, I know thing A undergoes event B at time 0, 10, 15, 22, 28, etc. As a bonus, lets say we can also know that thing A must necessarily be in or out of certain categories which allow and deny certain time periods for event B.
I would Imagine we would want to assign a percentage chance that thing A is in each category, or out of each category, and use some sort of complex algorithm to superimpose those probability distributions on top of each other, am I correct?
Click to expand...

Basically a 'can the future behavior of a process be determined based on its behavior in the past' kind of question. It depends on the event. Were it a totally random process, then the specific behavior would be unpredictable although the mean, variance, autocorrelation, etc. might be 'detrendable' and 'guessable'.
 
It's not a problem from a text, it's a personal problem I'm having in developing a business model. Let me be more specific. In my model, lets say that thing A is a person and Event B is a conscious action to make a particular purchase on a nearly weekly to bi weekly basis.
 
also, while I do love math, I don't have time to write up the algorithm myself. I want to hire a professional mathematician, which is why I was hoping someone here could give me a reasonable estimate on the difficulty and amount of time it would take a professional to write the statistical model.
 
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