Prior

\( \mu_0\) | |
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\( \sigma_0\) |

\( \mu_0\) | |
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\( \sigma_0\) |

\( \alpha_0\) | |
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\( \beta_0\) |

Numerator Beta, parameter \( a_1 \) | |
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Numerator Beta, parameter \( b_1 \) | |

Denominator Beta, parameter \( a_2 \) | |

Denominator Beta, parameter \( b_2 \) |

The distribution of log(R) where R is the ratio of a Beta distribution
on the numerator and another Beta distribution on the denominator. Useful when R is
a risk ratio, and a point estimate and standard error for the *log risk ratio* is provided
by the study.

The distribution is calculated using Monte Carlo simulation (with 10,000 samples) and kernel density estimation. This is a slight approximation, which becomes bigger as you go out towards the tails of the distribution. It's also a little bit slower than other distribution families (Monte Carlo simulation is the fastest method I know of).

Numerator Beta, parameter \( a_1 \) | |
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Numerator Beta, parameter \( b_1 \) | |

Denominator Beta, parameter \( a_2 \) | |

Denominator Beta, parameter \( b_2 \) |

The distribution of the ratio of two beta distributions. Useful for a prior over a risk ratio.

The distribution is calculated using Monte Carlo simulation (with 10,000 samples) and kernel density estimation. This is a slight approximation, which becomes bigger as you go out towards the tails of the distribution. It's also a little bit slower than other distribution families (Monte Carlo simulation is the fastest method I know of).

Likelihood

\( \mu_1\) | |
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\( \sigma_1\) |

2.5% | |
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97.5% |

\( \mu_1\) | |
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\( \sigma_1\) |

2.5% | |
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97.5% |

\( \alpha_1\) | |
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\( \beta_1\) |

successes \(s\) | |
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failures \(f\) |

From | |
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To |

Click here to see an example.

Status: waiting for status

One use case that may be of particular interest is updating a prior on a parameter B based on b,
an a statistical estimate of B (for example from a study you conducted or are reading about).

- If b is a
**mean**or a difference in means (such as a**treatment effect**), the likelihood distribution will be a normal distribution centered around b with a standard deviation equal to the standard error of b. The log-normal distribution may be a good choice of prior for positive quantities.

Quick link:**Update from statistical estimate of a mean or treatment effect** - If b is a ratio, its error distribution converges to normality slowly.
In the case of a
**risk ratio**, both risks are positive, so the error distribution of log(b), which converges faster, is often used. In this case, you can take logs of both prior and likelihood, so that the likelihood becomes a normal distribution. A good choice for the prior over a risk ratio is a ratio of Beta distributions, whose log is a difference of logs of betas distributions.

Quick link:**Update from statistical estimate of a risk ratio**(log space)