38  Basics

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The vast majority of Monte Carlo simulations aim to estimate either an expectation or a probability. The formulas we use for these two are both based on the Central Limit Theorem, but the formulas different depending on whether we are estimating an expectation or a probability.

38.1 Expectation

If we have \(X_i\) for \(i=1,\ldots,n\) that are independent and identically distributed with \(E[X_i] = \mu\) and \(Var[X_i] = \sigma^2 < \infty\), then \[ \frac{\overline{X}-\mu}{S/\sqrt{n}} \stackrel{d}{\to} N(0,1) \quad \mbox{as } n\to \infty. \] where \(\overline{X}\) is the sample average and \(S\) is the sample standard deviation.

Thus, for large enough n, \[ \overline{X} \stackrel{\cdot}{\sim} N(\mu, s^2/n) \] where \(\stackrel{\cdot}{\sim}\) expectations “approximately distributed.”

This result is the basis of a common asymptotic confidence interval formula for \(\mu = E[X_i]\): \[ \overline{x} \pm z_{\alpha/2} \, s/\sqrt{n} \] where \(z_{\alpha/2}\) is a critical value that depends on the confidence level (\(1-\alpha/2\)) of the confidence interval.

As a reminder, the term \(s/\sqrt{n}\) is often referred to as the standard error (SE) and \(z_{\alpha/2}s/\sqrt{n}\) as the margin of error.

38.1.1 Functions

Let’s first make a function to construct the data.frame we’ll need to plot to visualize

# Create data.frame that contains 
#  n:    number of simulations
#  xbar: cumulative expectation
#  s:    cumulative standard deviation
#  se:   cumulative standard error
#  lcl:  cumulative lower confidence limit of confidence interval
#  ucl:  cumulative upper confidence limit of confidence interval
create_expectation_dataframe <- function(x, alpha = 0.05) {
  data.frame(n = 1:length(x)) %>%
    mutate(
      xbar = cumsum(x) / n,
      s    = sqrt(cumsum( (x-xbar)^2 ) / (n-1)),
      se   = s/sqrt(n), 
      lcl  = xbar - qnorm( 1 - alpha/2 ) * se,
      ucl  = xbar + qnorm( 1 - alpha/2 ) * se
    )
}

As long as we’re at it, let’s make a function to create the plot

# Function to cumulative expectation with confidence interval
plot_expectation <- function(d, truth = NULL) {
  g <- ggplot(d, 
              aes(x    = n, 
                  y    = xbar, 
                  ymin = lcl, 
                  ymax = ucl)) + 
    geom_ribbon(
      fill  = "blue", 
      alpha = 0.3) + 
    geom_line(
      color = "blue")
  
  if (!is.null(truth))
    g <- g + geom_hline(yintercept = truth, 
                        color      = "red", 
                        linetype   = 2)
  
  return(g)
}

38.1.2 Examples

38.1.2.1 Normal expectation

Let \(X_i \stackrel{ind}{\sim} N(\mu, \sigma^2)\). If we sample enough \(X_i\), then we can learn about \(\mu\).

# Pick these values 
m <- 0 # true expectation
s <- 1 # true standard deviation

# Simulate data
x <- rnorm(1e3, mean = m, sd = s)

# Create and plot Monte Carlo estimates
d <- create_expectation_dataframe(x)
plot_expectation(d, truth = m)

38.1.2.2 Truncated normal expectation

Suppose our random variables have a normal distribution but are truncated between two values \(a\) and \(b\). We might write this as \(N(\mu,\sigma^2)[a,b]\).

Let’s first construct a function to simulate this random variable. (There are better ways to do this.)

# Function to simulate a truncated normal
rtnorm <- function(n, mean, sd, a, b) {
  # Propose n values [not all will be (a,b)]
  x <- rnorm(n, mean, sd) 
  
  # Replace values not in (a,b) 
  # with new values in (a,b)
  for (i in seq_along(x)) {
    while (x[i] < a || x[i] > b) {
      x[i] <- rnorm(1, mean, sd)
    }
  }
  
  return(x)
}

also the density for a truncated normal

# Density for a truncated normal
dtnorm <- function(x, mean, sd, a, b) {
  # Calculate probability of being in (a,b)
  c <- diff(pnorm(c(a,b), mean, sd))
  
  # Normalize untruncated normal by this probability
  # to ensure this density integrates to 1
  d <- dnorm(x, mean, sd)/c
  
  # Return this density or 0 if outside (a,b)
  return(d * (a < x & x < b))
}

Simulate and plot

# Pick these values
m <- 3  # untruncated expectation
s <- 8  # untruncated standard deviation
a <- 10 # lower truncation bound
b <- 20 # upper truncation bound

# Plot density
curve(dtnorm(x, m, s, a, b), 
      from = a - 1, 
      to   = b + 1, 
      n = 1001)

Monte Carlo

# Estimate expectation via Monte Carlo
x <- rtnorm(1e3, m, s, a, b)
d <- create_expectation_dataframe(x)
plot_expectation(d)

The expectation actually can be calculated for a truncated normal

# Calculate true expectation for truncated normal and plot it
alpha <- (a-m)/s
beta  <- (b-m)/s
expectation <- m + s * (dnorm(alpha) - dnorm( beta)) /
                       (pnorm(beta ) - pnorm(alpha)) 
plot_expectation(d, truth = expectation)

38.1.2.3 Cauchy expectation

Watch out!

You need to pay attention to whether the variance is finite amongst your random variables. A Cauchy distribution has no moments and therefore its expectation and variance are both undefined. This occurs because the Cauchy distribution has extremely heavy tails.

Let’s use Monte Carlo to try and estimate a Cauchy ``expectation’’

# Pick these
m <- 0 # Cauchy location 
s <- 1 # Cauchy scale

# Simulate random Cauchy
x <- rcauchy(1e3, m, s)

# Create Monte Carlo estimate and plot it
d <- create_expectation_dataframe(x)
plot_expectation(d)

plot_expectation(d) + xlim(25,100) # will likely need to adjust the limits

Warning: Removed 924 rows containing missing values (`geom_line()`).

The CLT is not relevant here because the variance of the random variables is not finite.

38.2 Probability

If we have \(Y \sim Bin(n,\theta)\) with \(\theta \in (0,1)\) and \(\hat\theta = Y/n\), then \[ \frac{\hat\theta-\theta}{\sqrt{\frac{\hat\theta(1-\hat\theta)}{n}}} \stackrel{d}{\to} N(0,1) \quad \mbox{as } n\to \infty. \] Thus, for large enough n, \[ \hat\theta \stackrel{\cdot}{\sim} N\left(\theta, \frac{\hat\theta(1-\hat\theta)}{n}\right) \] where \(\stackrel{\cdot}{\sim}\) expectations “approximately distributed.”

This result is the basis of a common asymptotic confidence interval formula for \(\theta\): \[ \hat\theta \pm z_{\alpha/2} \sqrt{\frac{\hat\theta(1-\hat\theta)}{n}} \] where \(z_{\alpha/2}\) is a critical value that depends on the confidence level \(100(1-\alpha)\)% of the confidence interval. The term \(\sqrt{\hat\theta(1-\hat\theta)/n}\) is referred to as the standard error (SE).

38.2.1 Functions

Function to create proportion data frame. The data will be binary (0/1) or logical (TRUE/FALSE).

# Create data frame to construct 
#  n : number of simulations
#  theta_hat : cumulative estimated proportion
#  se : cumulative standard error
#  lcl : cumulative lower confidence limit for CI
#  ucl : cumulative upper confidence limit for CI
create_proportion_dataframe <- function(x, alpha = 0.05) {
  data.frame(n = 1:length(x)) %>%
    mutate(
      theta_hat = cumsum(x)/n,
      se        = sqrt( theta_hat * (1-theta_hat) / n ),
      lcl       = theta_hat - qnorm( 1 - alpha/2 ) * se,
      ucl       = theta_hat + qnorm( 1 - alpha/2 ) * se
    )
}

and the plot function

# Function to plot cumulative proportion with CI
plot_proportion <- function(d, truth = NULL) {
  g <- ggplot(d, 
              aes(
                x    = n, 
                y    = theta_hat, 
                ymin = lcl, 
                ymax = ucl)) + 
    geom_ribbon(
      fill  = "blue", 
      alpha = 0.3) + 
    geom_line(
      color = "blue")
  
  if (!is.null(truth))
    g <- g + geom_hline(yintercept = truth, 
                        color      = "red", 
                        linetype   = 2)
  
  return(g)
}

38.2.2 Examples

38.2.2.1 Bernoulli

Let’s generate some Bernoulli data that, when summed, will be binomial.

# Pick this
p <- 0.3 # Bernoulli success probability

# Monte Carlo simulation and plot
x <- rbinom(1e3, size = 1, prob = p)
d <- create_proportion_dataframe(x)
plot_proportion(d, truth = p)

38.2.2.2 Normal probability

How about the probability a normal random variable is between two end points? Of course, we can calculate the truth here.

# Pick these
m <- 0 # normal expectation
s <- 1 # normal standard deviation
a <- 2 # lower endpoint
b <- 4 # upper endpoint

# Monte Carlo simulation
y <- rnorm(1e3, m, s)
x <- a < y & y < b    # data in (a,b)

# Plot
d <- create_proportion_dataframe(x)
plot_proportion(d, truth = diff(pnorm(c(a,b), 
                                      mean = m, 
                                      sd   = s)))

38.2.2.3 Unknown probability

How about the probability that the absolute value of a normal random variable is less than an exponential random variable?

# Let Y ~ N(0,1)
#     X ~ Exp(1)
# Estimate P(|Y| < X)

# Simulate X and Y
n <- 1e3
y <- rnorm(n)
e <- rexp(n)

# Calculate proportion |Y| < e
x <- abs(y) < e

# Create and plot
d <- create_proportion_dataframe(x)
plot_proportion(d)

tail(d, 1)

        n theta_hat        se       lcl       ucl
1000 1000     0.503 0.0158111 0.4720108 0.5339892

38.3 Summary

Throughout these examples, we have focused on visualizing the estimated expectation or probability as the number of simulations increases. We have noted how the uncertainty decreases as we increase the number of simulations. Typically, we simply need to run enough simulations so that our uncertainty is sufficiently small.

Below is some code and examples for estimating an expectation.

# Function to calculate Monte Carlo expectation with standard error
mc_expectation <- function(x) {
  return(
    c(mean = mean(x),
      se   = sd(x)/sqrt(length(x)))
  )
}

# Normal 
#  as Monte Carlo sample size increases
#  standard error decreases
mc_expectation(rnorm(1e3, mean = 10))

      mean         se 
9.98450435 0.03117801 

mc_expectation(rnorm(1e4, mean = 10))

       mean          se 
10.00506634  0.00993711 

mc_expectation(rnorm(1e5, mean = 10))

        mean           se 
10.001824620  0.003158979 

# Truncated normal
mc_expectation(rtnorm(1e4, mean = 10, sd = 1, a = 7, b = 11))

       mean          se 
9.707842735 0.007740931 

# Exponential
mc_expectation(rexp(1e4, rate = 5))

       mean          se 
0.198536608 0.001976137 

Below is some code and examples for estimating a proportion

# Function to calculate Monte Carlo proportion with standard error
mc_probability <- function(x) {
  p <- mean(x)
  
  return(
    c(prop = p,
      se   = sqrt(p*(1-p)/length(x)))
  )
}

# Normal
x <- rnorm(1e4, mean = 10, sd = 0.1)
mc_probability(x > 9)

prop   se 
   1    0 

mc_probability(x < 12)

prop   se 
   1    0 

mc_probability(9 < x & x < 12)

prop   se 
   1    0 

# Exponential
mc_probability(rexp(1e4, rate = 5) > 1)

        prop           se 
0.0058000000 0.0007593655 

38.3.1 Reproducibility

One frustrating aspect of Monte Carlo studies is that they are not reproducible. Every time you run a simulation, you will obtain a different answer. This is especially frustrating when you are trying to debug your code. In these situations, you can set the seed using the set.seed() function which will make the Monte Carlo study reproducible.

set.seed(20240423)
mc_expectation(rnorm(1e3, mean = 10))

       mean          se 
10.03307003  0.03261884 

mc_expectation(rnorm(1e3, mean = 10)) # not reproducible

      mean         se 
9.96191213 0.03218702 

set.seed(20240423)
mc_expectation(rnorm(1e3, mean = 10)) # reproducible

       mean          se 
10.03307003  0.03261884