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Chapter6_Priorities/Priors.ipynb

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"\n",
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"## Jefferys Priors\n",
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"Earlier, we talked about objective priors rarely are *objective*. Partly what we mean by this is that we want a prior that doesn't bias our posterior estimates. The flat prior seems like a reasonable choice as it assigns equal probability to all values. \n",
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"Earlier, we talked about objective priors rarely being *objective*. Partly what we mean by this is that we want a prior that doesn't bias our posterior estimates. The flat prior seems like a reasonable choice as it assigns equal probability to all values. \n",
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"But the flat prior is not transformation invariant. What does this mean? Suppose we have a random variable $ \\bf X $ from Bernoulli($\\theta$). We define the prior on $p(\\theta) = 1$. \n",
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"metadata": {}
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}
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]
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}
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}

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