tensorflow_probability/python/distributions/binomial.py
def _bdtr(k, n, p):
  """The binomial cumulative distribution function.

  Args:
    k: floating point `Tensor`.
    n: floating point `Tensor`.
    p: floating point `Tensor`.

  Returns:
    `sum_{j=0}^k p^j (1 - p)^(n - j)`.
  """
  # Trick for getting safe backprop/gradients into n, k when
  #   betainc(a = 0, ..) = nan
  # Write:
  #   where(unsafe, safe_output, betainc(where(unsafe, safe_input, input)))
  ones = tf.ones_like(n - k)
  k_eq_n = tf.equal(k, n)
  safe_dn = tf.where(k_eq_n, ones, n - k)
  dk = tf.math.betainc(a=safe_dn, b=k + 1, x=1 - p)
  return tf.where(k_eq_n, ones, dk)
def _bdtr(k, n, p):
  # Trick for getting safe backprop/gradients into n, k when
  #   betainc(a = 0, ..) = nan
  # Write:
  #   where(unsafe, safe_output, betainc(where(unsafe, safe_input, input)))
  ones = tf.ones_like(n - k)
  k_eq_n = tf.equal(k, n)
  safe_dn = tf.where(k_eq_n, ones, n - k)
  dk = tf.math.betainc(a=safe_dn, b=k + 1, x=1 - p)
  return tf.where(k_eq_n, ones, dk)
def _bdtr ( k , n , p ) : # Trick for getting safe backprop/gradients into n, k when #   betainc(a = 0, ..) = nan # Write: #   where(unsafe, safe_output, betainc(where(unsafe, safe_input, input))) ones = tf . ones_like ( n - k ) k_eq_n = tf . equal ( k , n ) safe_dn = tf . where ( k_eq_n , ones , n - k ) dk = tf . math . betainc ( a = safe_dn , b = k + 1 , x = 1 - p ) return tf . where ( k_eq_n , ones , dk )
The binomial cumulative distribution function.

  Args:
    k: floating point `Tensor`.
    n: floating point `Tensor`.
    p: floating point `Tensor`.

  Returns:
    `sum_{j=0}^k p^j (1 - p)^(n - j)`.
The binomial cumulative distribution function .