Back to Graduate School Applications

Last lecture we were trying to understand $\pi$ the acceptance rate of a graduate program in a specific department. Let's make a fresh start to the same problem. This time we will let $\pi \in [0,1]$.

Continuous probability models

Let $\pi$ be a continuous random variable with pdf $f(\pi)$. Then $f(\pi)$ has the following properties:

• $\int_\pi f(\pi)d\pi = 1$, ie. the area under $f(\pi)$ is 1
• $f(\pi) \ge 0$
• $P(a < \pi < b) = \int_a^b f(\pi) d\pi$ when $a \le b$

Interpreting $f(\pi)$:

$f(\pi)$ can be used to compare the plausibility of two different values of $\pi$.

Plotting the continuous prior

For each of the student's prior ideas for $\pi$ plot the pdf of the prior. Your plot will not be exact since no exact values are given.

Morteza thinks that it is extremely difficult to get into this program.

Jared thinks that it is difficult to get into this program.

Erin does not have any strong opinions whether it is difficult or easy to get into this program.

Xuan thinks that it is easy to get into this program.

Beyoncé thinks that it is extremely easy to get into this program.

Morteza's prior

Jared's prior

Erin's prior

Xuan's prior

Beyoncé's prior

Beta Prior model

Let $\pi$ be a random variable which can take any value between 0 and 1, ie. $\pi \in [0,1]$. Then the variability in $\pi$ might be well modeled by a Beta model with shape parameters $\alpha > 0$ and $\beta > 0$:

$$\pi \sim \text{Beta}(\alpha, \beta)$$ The Beta model is specified by continuous pdf $$\begin{equation} f(\pi) = \frac{\Gamma(\alpha + \beta)}{\Gamma(\alpha)\Gamma(\beta)} \pi^{\alpha-1} (1-\pi)^{\beta-1} \;\; \text{ for } \pi \in [0,1] \end{equation}$$ where $\Gamma(z) = \int_0^\infty y^{z-1}e^{-y}dy$ and $\Gamma(z + 1) = z \Gamma(z)$. Fun fact: when $z$ is a positive integer, then $\Gamma(z)$ simplifies to $\Gamma(z) = (z-1)!$.

Beta Prior model

Beta Prior model

$\pi \sim \text{Beta}(3, 8)$

dbeta(x = 0.5, 
      shape1 = 3, 
      shape2 = 8)

## [1] 0.703125

Plotting Beta Prior

Plotting Beta Prior with bayesrules package

Use the plot_beta() function in the bayesrules package to try different shape parameters. Example:

plot_beta(alpha = 5, beta = 7)

Beta Descriptives

$$E(\pi) = \frac{\alpha}{\alpha + \beta}$$

$$\text{Mode}(\pi) = \frac{\alpha - 1}{\alpha + \beta - 2}$$

$$\text{Var}(\pi) = \frac{\alpha \beta}{(\alpha + \beta)^2(\alpha + \beta + 1)}$$

Beta Descriptives with bayesrules package

Use the summarize_beta() function in the bayesrules package to find the mean, mode, and variance of various Beta distributions. Example:

summarize_beta(alpha = 5, beta = 7)

##        mean mode        var
## 1 0.4166667  0.4 0.01869658