I have 1000 experiments on the same data, each of which is trying to decide the probability that the data agrees with a model.(adsbygoogle = window.adsbygoogle || []).push({});

The problem is, even if each experiment REALLY agrees with the model (ie. p(d1) = .99, p(d2) = .99, etc), when I multiply these together to get P(d1 & d2 & d3.... & dn), the result is some really really small number (near 0). If I do 2 sets of such experiments, I can tell which set of data had a higher probability of matching the model (simply by seeing which of the two combined numbers was larger), but the actual numbers at that point are meaningless - they are only useful in a "relative to a different one" sense.

Is there a way to combine these to get a "valid" probability without just multiplying them? A reasonable idea seems to just "average" them, ie [tex]\sum p(dn) / n[/tex], but everyone tells me this is a really terrible idea and it is not a real probability any more.

Thoughts?

Thanks,

Dave

**Physics Forums - The Fusion of Science and Community**

Join Physics Forums Today!

The friendliest, high quality science and math community on the planet! Everyone who loves science is here!

The friendliest, high quality science and math community on the planet! Everyone who loves science is here!

# Combining independent probabilities in a meaningful way

Loading...

Similar Threads for Combining independent probabilities | Date |
---|---|

B Combinations of n elements in pairs | Apr 6, 2018 |

I Combinatorics & probability density | Apr 6, 2018 |

I A specific combination problem | Feb 6, 2018 |

I Combination of Non Adjacent Numbers | Dec 3, 2017 |

Combination probability of variables that are not independent | Mar 2, 2013 |

**Physics Forums - The Fusion of Science and Community**