### Artificial IntelligenceAIMA Exercises

This exercise investigates properties of the Beta distribution defined in Equation (beta-equation).
1. By integrating over the range $[0,1]$, show that the normalization constant for the distribution $[a,b]$ is given by $\alpha = \Gamma(a+b)/\Gamma(a)\Gamma(b)$ where $\Gamma(x)$ is the Gamma function, defined by $\Gamma(x+1)x\cdot\Gamma(x)$ and $\Gamma(1)1$. (For integer $x$, $\Gamma(x+1)x!$.)
2. Show that the mean is $a/(a+b)$.
3. Find the mode(s) (the most likely value(s) of $\theta$).
4. Describe the distribution $[\epsilon,\epsilon]$ for very small $\epsilon$. What happens as such a distribution is updated?

This exercise investigates properties of the Beta distribution defined in Equation (beta-equation).
1. By integrating over the range $[0,1]$, show that the normalization constant for the distribution $[a,b]$ is given by $\alpha = \Gamma(a+b)/\Gamma(a)\Gamma(b)$ where $\Gamma(x)$ is the Gamma function, defined by $\Gamma(x+1)x\cdot\Gamma(x)$ and $\Gamma(1)1$. (For integer $x$, $\Gamma(x+1)x!$.)
2. Show that the mean is $a/(a+b)$.
3. Find the mode(s) (the most likely value(s) of $\theta$).
4. Describe the distribution $[\epsilon,\epsilon]$ for very small $\epsilon$. What happens as such a distribution is updated?

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