Empirical Probability Function

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An Empirical Probability Function is a probability function structure that is approximated/estimated by a Probability Function Estimate/?/Probability Distribution Estimation Task based on a set of Observed Outcomes.




  • (Larsen & Marx, 1986) ⇒ Richard J. Larsen, and Morris L. Marx. (1986). “An Introduction to Mathematical Statistics and Its Applications, 2nd edition." Prentice Hall
    • QUOTE: Consider a sample space, [math]S[/math], and any event, [math]A[/math], defined on S. If our experiment were performed one time, either [math]A[/math] or AC would be the outcome. If it were performed [math]n[/math] times, the resulting set of sample outcomes would be members of [math]A[/math] on [math]m[/math] occasions, [math]m[/math] being some integer between 0 and [math]n[/math], inclusive. Hypothetically, we could continue this process an infinite number of times. As [math]n[/math] gets large, the ratio m/n will fluctuate less and less (we will make that statement more precise a little later). The number that m/n convert to is called the empirical probability of [math]A[/math] : that is, P(A) = limn→∞(m/n). … the very act of repeating an experiment under identical conditions an infinite number of times is physically impossible. And left unanswered is the question of how large [math]n[/math] must be to give a good approximation for limn→∞(m/n)...