For Uniform, require a nonempty integer interval. Compute its size in Long before converting to Double, avoiding integer overflow in u-l+1. For Flip, require , and omit zero-probability outcomes from the reported support.
class Uniform(l: Int, u: Int) extends FiniteDist[Int] {
  require(l <= u)
  private val size = u.toLong - l.toLong + 1L
  def getDom: List[Int] = {
    val result = List.newBuilder[Int]
    var x = l.toLong
    while (x <= u.toLong) { result += x.toInt; x += 1L }
    result.result()
  }
  def getProb(x: Int): Double =
    if (l <= x && x <= u) 1.0 / size.toDouble else 0.0
}

class Flip(p: Double) extends FiniteDist[Boolean] {
  require(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 first is a discrete uniform distribution; the second is a Bernoulli distribution on Boolean values. Their exact mathematical probability mass functions are
(1)
Each support contains exactly the positive-probability outcomes once, and the probabilities sum to one. Double approximates these exact values by floating-point arithmetic. Materializing a huge support is limited by available memory; the Long calculation keeps its cardinality and endpoints arithmetically correct.

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