# BDA3 Chapter 2 Exercise 22

Here’s my solution to exercise 22, chapter 2, of Gelman’s *Bayesian Data Analysis* (BDA), 3rd edition. There are solutions to some of the exercises on the book’s webpage.

Suppose you have a random sample of 100 college students in a basketball study. Each student takes 100 free-throws to establish a baseline success probability, \(\phi_0\). The students then take 50 practice free-throws per day for a month. After this month is up, each takes 100 shots for a final measurement of success probability, \(\phi_1\). We wish to estimate the increase in success probability, \(\theta := \phi_1 - \phi_0\).

We could use the uniform distribution on the range of theoretically possible values \([-1, 1]\) as a ‘non-informative’ prior, i.e. every level of increase is equally likely.

To construct an informative prior, I had to do a bit of background reading as I have next to zero basketball knowledge. After some google-fu, it seems the best of the best have a free-throw success rate of around 80-90%, and the worst of the best around 50-60%. This latter estimate probably lies on the upper extreme for random college students. From watching some free throw videos, I would guess that I would have a success rate of about 10% (baseline).

Let’s model the baseline average as beta distributed around a mean of 10% and upper extreme below 40%.

```
mu0 <- 0.10
n0 <- 15
alpha0 <- mu0 * n0
beta0 <- (1 - mu0) * n0
tibble(
p = seq(0, 1, 0.01),
density = dbeta(p, alpha0, beta0)
) %>%
ggplot() +
aes(p, density) +
geom_area(fill = 'skyblue', colour = 'white') +
scale_x_continuous(labels = percent, breaks = seq(0, 1, 0.1)) +
labs(
x = 'Average baseline success probability',
y = 'Density',
title = 'Baseline success probability simulation',
subtitle = str_glue('Beta({alpha0}, {beta0})')
)
```

With this assumption, there is a 0.286% chance of an average above 40%.

For the final estimate, the improvement could be quite significant given that random students are likely untrained (the better you get, the harder it is to improve). An average 15%% improvement seems plausible to me.

```
mu1 <- mu0 + 0.15
n1 <- 20
alpha1 <- mu1 * n1
beta1 <- (1 - mu1) * n1
tibble(
p = seq(0, 1, 0.01),
density = dbeta(p, alpha1, beta1)
) %>%
ggplot() +
aes(p, density) +
geom_area(fill = 'skyblue', colour = 'white') +
scale_x_continuous(labels = percent, breaks = seq(0, 1, 0.1)) +
labs(
x = 'Average final success probability',
y = 'Density',
title = 'Final success probability simulation',
subtitle = str_glue('Beta({alpha1}, {beta1})')
)
```

With the above assumptions, the distribution of the average increase is shown below. The bulk of the mass is above 0, which makes sense as most should improve through practice. There is also some chance of a decrease, with -25%% being fairly extreme.

```
increase <- tibble(
phi0 = rbeta(10000, alpha0, beta0),
phi1 = rbeta(10000, alpha1, beta1),
theta = phi1 - phi0
)
increase %>%
ggplot() +
aes(theta) +
geom_histogram(fill = 'skyblue') +
geom_vline(xintercept = 0, colour = 'dark orange', linetype = 'dashed') +
scale_x_continuous(labels = percent, breaks = seq(-1, 1, 0.2), limits = c(-1, 1)) +
labs(
x = 'Average increase in success probability (percentage points)',
y = 'Count',
title = 'Average increase in success probability simulation',
subtitle = str_glue('Beta({alpha1}, {beta1}) - Beta({alpha0}, {beta0})')
)
```

We could approximate this as a beta distribution on \([-1, 1]\) to ensure the prior is bounded. In this case, we can probably get away with using a normal distribution since the probability of exceeding the bounds is negligible.

```
mu <- mu1 - mu0
sigma <- sd(increase$theta)
tibble(
theta = seq(-1, 1, 0.01),
density = dnorm(theta, mu, sigma)
) %>%
ggplot() +
aes(theta, density) +
geom_area(fill = 'skyblue', colour = 'white') +
geom_vline(xintercept = 0, colour = 'dark orange', linetype = 'dashed') +
scale_x_continuous(labels = percent, breaks = seq(-1, 1, 0.25)) +
labs(
x = 'Average increase in success probability (percentage points)',
y = 'Density',
title = 'Strongly informative prior',
subtitle = str_glue('Normal(μ = {signif(mu, digits = 2)}, σ = {signif(sigma, digits = 2)})')
) +
NULL
```

The probability that this normal distribution exceeds 1 is 0.0000000000719%. A subjective prior for the average increase in success probability would thus be a \(\dnorm(0.15, 0.12)\) prior.

Finally, let’s construct a weakly informative prior. We’ll again use the beta distribution scaled to the interval \([-1, 1]\), with a mean of 0 so that an increase can be entirely attributed to the data and not to the prior. Since the best of the pros have a success probability around 90%, we’d expect the increase of 100 random students to be below this value.

```
alpha <- 5
beta <- 5
tibble(
theta = seq(-1, 1, 0.01),
density = dbeta(0.5 + theta / 2, alpha, beta)
) %>%
ggplot() +
aes(theta, density) +
geom_area(fill = 'skyblue', colour = 'white') +
geom_vline(xintercept = 0, colour = 'dark orange', linetype = 'dashed') +
scale_x_continuous(breaks = seq(-1, 1, 0.25), labels = percent) +
labs(
x = 'Average increase in success probability (percentage points)',
y = 'Density',
title = 'Weakly informative prior',
subtitle = str_glue('Beta({alpha}, {beta})')
) +
NULL
```