Tutorial for sampling the conditional #667
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psteinb
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Mar 21, 2022
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Next, we follow the sampler interface and create a potential_function. The observation in this example is `x_o=[1, 1, 1, 1]`." |
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Just watching this from the sidelines. the meaning of potential_function is not discussed here. I also checked that in none of the previous tutorials, a potential_function is mentioned. It might be worthwhile to share here, what mathematical meaning you connect with the potential_function. It gives readers some context.
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Thanks for the input Peter! The potential function is expained in the sampler interface tutorial. Quote:
Next, we obtain the potential function. A potential function is a function of the parameter f(theta).
The posterior is proportional to the product of likelihood and prior: p(theta|x) \propto p(x|theta)p(theta).
The potential function is the logarithm of the right-hand side of this equation: f(theta) = log(p(x|theta)p(theta))
)
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Cool, then I suggest to insert it in here too. Just in case people traverse the tutorials from start to end.
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I agree, I specifically added a pointer to it.
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janfb
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Mar 21, 2022
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This adds documentation for sampling the conditional posterior. I added it to the end of the already existing tutorial on conditional distributions