Conjugate Beta Prior Proof – How to Prove the Conjugate Beta Prior in Bayesian Analysis

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The Conjugate Beta Prior

Hello.
I'm having a problem with trying to figure out this proof that shows the beta
distribution is conjugate to the binomial distribution (picture attached).
I understand it until the third row, but I got confused with this step from the third to the fourth row.
I would appreciate a simple explanation.
Thank you in advance.

Best Answer

To go from the third to fourth row just ignore factors that are constant with respect to $\pi$. That is, let

$$\binom{n}{y}\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}=k$$

so

$$p(\pi|y)=k \cdot \pi^y (1-\pi)^{(n-y)}\pi^{(\alpha-1)}(1-\pi)^{(\beta-1)}$$

or

$$p(\pi|y) \propto \pi^y(1-\pi)^{(n-y)}\pi^{(\alpha-1)}(1-\pi)^{(\beta-1)}$$

The idea's to deal just with the kernel of the probability distribution, knowing you can always put the normalizing constant back in later.

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