# Approximating small probabilities using importance sampling

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Box plots are often used.

They are not always the best visualisation (e.g. is bi-modality not visible), but

I will not discuss that in this post.

Here, I will use it as an example of Importance sampling that

is a technique to estimate tail probabilities without requiring many, many

simulations.

**Assume that we have a standard normal distribution. What is
the probability of getting a box plot extreme outlier (here we use that it must be greater than 2.5 IQR from lower/upper quartile)?**

This can be solved theoretically. And solving problems theoretically

often yields great insight. But sometimes solving problems theoretically is impossible (or takes too long).

So another way of solving problems is by using simulations.

Here that way of solving the problem is demonstrated.

## Solving directly

We can simulate it directly:

set.seed(2019) x <- rnorm(10000) qs <- quantile(x, c(0.25, 0.75)) iqr <- qs[2] - qs[1] # or IQR() lwr <- qs[1] - 2.5*iqr upr <- qs[2] + 2.5*iqr indices <- x < lwr | x > upr mean(indices) ## [1] 1e-04

So based on 10,000 simulations we believe the answer is around 1/10,000 = 0.01%.

Because only one of the simulated values were outside the 2.5 IQR.

Not too trustworthy…

## Importance sampling

So we are trying to estimate a tail probability.

What if we instead of simulating directly from the distribution of interest,

we could simulate from some other distribution with more mass in the area of

interest and then reweight the sample?

That is the idea behind importance sampling.

So let us sample from a uniform distribution between -10 and 10.

set.seed(2019) z <- runif(2500, min = -10, max = 10)

We make a smaller sample to find limits (they are based on quartiles, which is fairly easy to estimate accurately by simulation):

qs <- quantile(rnorm(2500), c(0.25, 0.75)) iqr <- qs[2] - qs[1] # or IQR() lwr <- qs[1] - 2.5*iqr upr <- qs[2] + 2.5*iqr

Now, we do not count 0s (not outlier) and 1s (outlier) but instead

consider importance weights, \(w(z) = f_X(z) / f_Y(z)\),

where \(f_X(z)\) is the probability density function for a standard normal

distribution and \(f_Y(z)\) is the probability density function for a uniform

distribution on \([-10, 10]\).

indices <- z < lwr | z > upr mean(indices) # Now, a much larger fraction is in the area of interest ## [1] 0.5752 importance_weight <- dnorm(z) / dunif(z, min = -10, max = 10) sum(importance_weight[indices]) / length(indices) ## [1] 3.810393e-05

So based on 2,500 + 2,500 simulations our estimate is now 0.0038104%.

It can (easily) be shown that the true answer is 0.005%.

## Comparing approaches

We do both of the above 1,000 times to inspect the behaviour:

The reason that the density estimate for the direct method looks strange is because

it only samples a few different values, and often does not get any outliers:

table(direct) ## direct ## 0 1e-04 2e-04 3e-04 4e-04 5e-04 ## 570 337 78 9 5 1

The methods’ summaries are calculated:

Method | Mean of estimates | SD of estimates | log10(MSE) |
---|---|---|---|

Direct sampling | 5.45e-05 | 7.34e-05 | -8.268689 |

Importance sampling | 5.59e-05 | 2.35e-05 | -9.245949 |

As seen, on average both give the same answer (as expected),

but the importance sampling provides a lower standard error.

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