How to sample vector of independent bernoulli variables efficiently

When working with Julia, it is important to find efficient ways to sample vectors of independent Bernoulli variables. In this article, we will explore three different approaches to solve this problem.

Approach 1: Using a For Loop

One way to sample a vector of independent Bernoulli variables efficiently is by using a for loop. Here is a sample code that demonstrates this approach:

function sample_bernoulli(n::Int)
    vector = zeros(n)
    for i in 1:n
        vector[i] = rand() < 0.5 ? 0 : 1
    end
    return vector
end

sample = sample_bernoulli(10)
println(sample)

This approach uses a for loop to iterate over each element in the vector and assigns a random value based on the Bernoulli distribution. While this approach is simple and easy to understand, it may not be the most efficient solution for large vectors.

Approach 2: Using Vector Comprehension

Another approach to efficiently sample a vector of independent Bernoulli variables is by using vector comprehension. Here is a sample code that demonstrates this approach:

function sample_bernoulli(n::Int)
    vector = [rand() < 0.5 ? 0 : 1 for i in 1:n]
    return vector
end

sample = sample_bernoulli(10)
println(sample)

This approach uses vector comprehension to generate the vector of Bernoulli variables. It is a more concise and efficient solution compared to the for loop approach. Vector comprehension allows for a more compact code and avoids the need for explicit indexing.

Approach 3: Using BitArray

A third approach to efficiently sample a vector of independent Bernoulli variables is by using a BitArray. Here is a sample code that demonstrates this approach:

function sample_bernoulli(n::Int)
    vector = BitArray(rand(n) .< 0.5)
    return vector
end

sample = sample_bernoulli(10)
println(sample)

This approach uses a BitArray to efficiently store the Bernoulli variables. It takes advantage of the fact that a BitArray only requires one bit per element, resulting in memory savings. This approach is particularly useful when dealing with large vectors.

After exploring these three different approaches, it is clear that the most efficient solution for sampling a vector of independent Bernoulli variables is Approach 3: Using BitArray. This approach provides both memory efficiency and computational efficiency, making it the preferred choice.