Integral arising in estimation of discrete series

In summary, the conversation discusses solving the integral f(t;a,b)=\int_a^b\sqrt{t-x^3}dx or finding a good estimate for it. The problem has nice assumptions such as 0\le a\le b\le t and other assumptions may also hold. A bad estimate is given as (b-a)\sqrt{t-a^3} and the person is looking for a better or tractable closed-form version. Mathematica gives a complicated form that may not apply to this case. The conversation also mentions using Simpson's rule to approximate the integral and suggests using t instead of t^3 as the upper bound.
  • #1
CRGreathouse
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I'm trying to solve
[tex]f(t;a,b)=\int_a^b\sqrt{t-x^3}dx[/tex]
or find a good estimate for it. The problem is 'nice', and so various niceness assumptions apply: [itex]0\le a\le b\le t[/itex] -- and if other assumptions are needed, they probably hold. :D

An example of a bad estimate would be [itex](b-a)\sqrt{t-a^3}[/itex] -- I'm looking for a better, or ideally a tractable closed-form version. Mathematica gives a complicated form that seems to be concerned mostly with the case that [itex]x^3>t[/itex] which will not be the case here.

If it helps, a will be 'close' to 0 and b will be close to t.
 
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  • #2
Failing a closed solution, any thoughts on a good estimate? Any fans of systems other than Mathematica want to test this integral?
 
  • #3
Playing around with it a bit, it looks like

[tex]\int_0^{t^3}\sqrt{t^3-x^3}dx=\frac{\Gamma(4/3)}{\Gamma(11/6)}\sqrt{t^5\pi}[/tex]
 
  • #4
Simpson's rule gives:

[tex] \int^b_0 \sqrt{b^3 - x^3} dx \approx \frac{b}{6} \left( \sqrt{b^3} + 4\sqrt{\frac{7b^3}{8} \right)[/tex]. Should be reasonable? Which reminds me, I think you meant t instead of t^3 as your upper bound in your last post.
 

1. What is an integral in the context of estimation of discrete series?

An integral in the context of estimation of discrete series refers to the process of finding the area under a curve, which represents the frequency distribution of a discrete data set. It is used to calculate the probability of a certain outcome occurring within the data set.

2. How is integral used in estimating the mean of a discrete series?

Integral is used in estimating the mean of a discrete series by finding the area under the curve and dividing it by the total number of observations. This gives an approximation of the average value of the data set.

3. Can integral be used to estimate other measures of central tendency, such as median or mode?

Yes, integral can be used to estimate other measures of central tendency such as median or mode. However, it is more commonly used to estimate the mean due to its relationship with the area under the curve.

4. Are there any limitations to using integral in estimation of discrete series?

Yes, there are limitations to using integral in estimation of discrete series. It assumes that the data set is continuous and follows a certain distribution, which may not always be the case in real-world scenarios. Additionally, it requires a large number of observations to provide an accurate estimate.

5. How does the use of integral in estimation of discrete series compare to other methods?

The use of integral in estimation of discrete series is one of the most common and widely used methods. It is often preferred due to its simplicity and versatility, as it can be applied to different types of data sets. However, other methods such as the method of moments or maximum likelihood estimation may be more accurate in certain situations.

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