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Jul 1, 2012 at 10:29 vote accept MathematicalOrchid
Jun 27, 2012 at 20:08 comment added Michael R. Chernick I don't see how knowing the height of f at a single point x tells you anthing about the area. It does tell you that M must be > f(x). If you have f evaluated at several points you can approximate the integral by rectangles but who knows how good that approximation will be?
Jun 27, 2012 at 19:59 comment added MathematicalOrchid Just wondering... When you evaluate $f(x)$, you then know exactly how much of that vertical slice is under the curve. Can you not use that knowledge somehow to improve the estimate a bit?
Jun 26, 2012 at 20:43 comment added MathematicalOrchid What an ingenious idea... You're right, it doesn't work without bounds on $f$, but it looks like you can't do much of anything without that information.
Jun 26, 2012 at 15:23 comment added Michael R. Chernick It is an assumption. I used the term mimimal to say that i am making as few assumptions as possible to reach a definitive answer.
Jun 26, 2012 at 15:12 comment added Macro I certainly agree with your first sentence, which begins to address the OP's second question. But, the method you've described requires that you, a priori, know $M$, which is not a particularly minimal assumption.
Jun 26, 2012 at 15:05 comment added Michael R. Chernick @Macro Without knowing anything about f I do not see how one could say anything about the statistical accuracy of an estimate of the integral based on evaluating it at a fixed finite set of points. My assumptions are rather minimal. If f is bounded on the interval [a,b] there should be some M large enough that it could be used as an upper bound on f.
Jun 26, 2012 at 14:58 comment added Macro This would work under the assumptions you laid out in the first sentence but based on the problem description it seems unlikely that you can, a priori, bound the function values between $0$ and $M$. It appeared that all you're given is the ability to calculate $f$ and nothing else.
Jun 26, 2012 at 14:51 history answered Michael R. Chernick CC BY-SA 3.0