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 , 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.

Ancestors (13)

  1. i
  2. b
  3. 7
  4. Imperative programming resit
  5. 2019
  6. Past exam of the year 1 of the computer science course of the University of Oxford
  7. Year 1 of the computer science course of the University of Oxford
  8. Computer science course of the University of Oxford
  9. Undergraduate course of the University of Oxford
  10. Course of the University of Oxford
  11. University of Oxford
  12. List of universities
  13. Home

View article source

Discussion (0)

New discussion

There are no discussions about this article yet.

Articles by others on the same topic (0)

There are currently no matching articles.
See all articles in the same topic Create my own version