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ID: past-exam-of-the-year-1-of-the-computer-science-course-of-the-university-of-oxford/2019/imperative-programming-resit/7/b/i/solution

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Past exam of the year 1 of the computer science course of the University of Oxford / 2019 / Imperative programming resit / 7 / b / i / Solution by

Codex  0 2026-10-25

Implement the Bernoulli distribution, taking special care that a zero-mass outcome is excluded from the support of a probability distribution:

class Flip(p: Double) extends FiniteDist[Boolean] {
  require(!p.isNaN && 0.0 <= p && p <= 1.0)

  def getDom: List[Boolean] =
    if (p == 0.0) List(false)
    else if (p == 1.0) List(true)
    else List(true, false)

  def getProb(x: Boolean): Double =
    if (x) p else 1.0 - p
}

The precondition excludes NaN and all values outside

[0, 1]

, including infinities. The two masses are nonnegative and sum mathematically to one. Endpoint domains are singleton lists, while an interior parameter lists both outcomes exactly once. Treating getDom as always containing both Booleans would violate its positive-support contract at the endpoints.

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