Find the expected loss from two Beta posteriors if one assumes (wrongly)
that a / b > c / d. That is, `E[max(Beta(c, d) - Beta(a, b), 0)]`

. This comes from
https://www.chrisstucchio.com/blog/2014/bayesian_ab_decision_rule.html. Note
that the `approx = TRUE`

version is vectorized, but the exact
version is not.

1 | ```
expected_loss(a, b, c, d, approx = FALSE, ...)
``` |

`a` |
alpha parameter for first Beta |

`b` |
beta parameter for second Beta |

`c` |
alpha parameter for first Beta |

`d` |
beta parameter for second Beta |

`approx` |
whether to use a normal approximation when calculating h |

`...` |
Extra arguments, ignored |

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