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?)

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.

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.

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)).

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.

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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