How to solve this integral? (something to do with a beta distribution?)

In summary, the conversation discusses an integral in Maple notation and its solution, which is related to a beta distribution. The simplified form of the integrand is also mentioned, as well as the values used for the variables in the integral. A correction is made to the second equation mentioned.
  • #1
Ad VanderVen
169
13
TL;DR Summary
Solving the integral (in Maple notation): Int(exp(c[0]*ln(y)/a[0]+c[1]*M*ln(M-y)/a[1]), y = 0 .. M);
I have the following integral (in Maple notation):

Int(exp(c[0]*ln(y)/a[0]+c[1]*M*ln(M-y)/a[1]), y = 0 .. M);

with (in Maple notation):

0<a[0], 0<a[1], 0<c[0], 0<c[1], 0<y, y<M, 0<M.

What is the solution of this integral? I suspect that the solution has something to do with a beta distribution.
 
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  • #2
Simplifying the integrand and substituting [itex]y = Mt[/itex] yields [tex]
\int_0^M \exp\left(
c_0 \frac{\ln(y)}{a_0} + c_1 M \frac{\ln(M - y)}{a_1}
\right)\,dy [/tex]
$$= \int_0^M y^{c_0/a_0}(M - y)^{Mc_1/a_1}\,dy =
M^{(c_0/a_0) + (Mc_1/a_1) + 1} \int_0^1 t^{c_0/a_0} (1 - t)^{c_1/a_1}\,dt
$$ which is indeed a beta function.
 
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  • #3
Thank you very much. You were of great help.
 
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Likes berkeman
  • #4
If I enter the values ##a_{1}\, = \, 0.7##, ##a_{0}\, = \, 0.4##, ##c_{1}\, = \, 0.9##, ##c_{0}\, = \, 0.5## and ##M\, = \,7## in
$$\int_{0}^{M}\!{y}^{{\frac {c_{0}}{a_{0}}}} \left( M-y \right) ^{{\frac {Mc_{1}}{a_{1}}}}\,{\rm d}y$$
I get ## 17939559.61##. If I enter the same values in
$${M}^{{\frac {c_{0}}{a_{0}}}+{\frac {Mc_{1}}{a_{1}}}+1}\int_{0}^{1}\! \left( 1-x \right) ^{{\frac {c_{1}}{a_{1}}}}{x}^{{\frac {c_{0}}{a_{0}}}}\,{\rm d}x$$
I get ##344821202.1##.
 
  • #5
That should be [tex]
\int_0^M y^{c_0/a_0}(M - y)^{Mc_1/a_1}\,dy =
M^{(c_0/a_0) + (Mc_1/a_1) + 1} \int_0^1 t^{c_0/a_0} (1 - t)^{Mc_1/a_1}\,dt[/tex]
 
  • #6
Again thank you very much. Sorry for my mistake.
 

FAQ: How to solve this integral? (something to do with a beta distribution?)

1. What is a beta distribution?

A beta distribution is a continuous probability distribution that is often used to model random variables that have values between 0 and 1. It is defined by two parameters, alpha and beta, which determine the shape of the distribution.

2. How do I solve an integral involving a beta distribution?

To solve an integral involving a beta distribution, you can use the beta function, also known as the Euler integral of the first kind. This function is defined as B(x,y) = Γ(x)Γ(y)/Γ(x+y), where Γ is the gamma function. By substituting the appropriate values for x and y, you can evaluate the integral.

3. What are the properties of a beta distribution?

A beta distribution has several important properties, including: it is a continuous distribution, it is defined on the interval [0,1], it is symmetric when alpha = beta, and its mean is equal to alpha/(alpha+beta) and its variance is equal to alpha*beta/((alpha+beta)^2*(alpha+beta+1)).

4. Can I use software to solve an integral involving a beta distribution?

Yes, there are several software programs that can solve integrals involving a beta distribution, such as Wolfram Alpha, MATLAB, and R. These programs use numerical methods to approximate the solution, which can be helpful for more complex integrals.

5. Are there any real-world applications of the beta distribution?

Yes, the beta distribution has many real-world applications, including in economics, biology, and engineering. It is commonly used to model proportions or probabilities, such as the probability of success in a clinical trial or the proportion of a population with a certain trait.

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