Average Error: 0.1 → 0.1
Time: 1.1m
Precision: 64
Internal Precision: 128
\[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot \left(1 - m\right)\]
\[\left(\left(\frac{m}{v} - \frac{m \cdot m}{v}\right) - 1\right) \cdot \left(1 - m\right)\]

Error

Bits error versus m

Bits error versus v

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Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 0.1

    \[\left(\frac{m \cdot \left(1 - m\right)}{v} - 1\right) \cdot \left(1 - m\right)\]
  2. Using strategy rm
  3. Applied sub-neg0.1

    \[\leadsto \left(\frac{m \cdot \color{blue}{\left(1 + \left(-m\right)\right)}}{v} - 1\right) \cdot \left(1 - m\right)\]
  4. Applied distribute-lft-in0.1

    \[\leadsto \left(\frac{\color{blue}{m \cdot 1 + m \cdot \left(-m\right)}}{v} - 1\right) \cdot \left(1 - m\right)\]
  5. Using strategy rm
  6. Applied distribute-rgt-neg-out0.1

    \[\leadsto \left(\frac{m \cdot 1 + \color{blue}{\left(-m \cdot m\right)}}{v} - 1\right) \cdot \left(1 - m\right)\]
  7. Applied unsub-neg0.1

    \[\leadsto \left(\frac{\color{blue}{m \cdot 1 - m \cdot m}}{v} - 1\right) \cdot \left(1 - m\right)\]
  8. Applied div-sub0.1

    \[\leadsto \left(\color{blue}{\left(\frac{m \cdot 1}{v} - \frac{m \cdot m}{v}\right)} - 1\right) \cdot \left(1 - m\right)\]
  9. Simplified0.1

    \[\leadsto \left(\left(\color{blue}{\frac{m}{v}} - \frac{m \cdot m}{v}\right) - 1\right) \cdot \left(1 - m\right)\]
  10. Final simplification0.1

    \[\leadsto \left(\left(\frac{m}{v} - \frac{m \cdot m}{v}\right) - 1\right) \cdot \left(1 - m\right)\]

Runtime

Time bar (total: 1.1m)Debug logProfile

herbie shell --seed 2018285 +o rules:numerics
(FPCore (m v)
  :name "b parameter of renormalized beta distribution"
  :pre (and (< 0 m) (< 0 v) (< v 0.25))
  (* (- (/ (* m (- 1 m)) v) 1) (- 1 m)))