What prior distribution could I exploit for estimating a proportion, if I realize some proportions are difficult?
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in the tensor, given that The brand new condition is appropriate with the shape of the original tensor. Let's realize this in detail employing a concrete instance.
be equal to the overall variety of factors in the original tensor (18 in our example). Yet another point to look at is always that we utilized a -one in one of several spots in Every of The form tuples.
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What prior distribution could I exploit for estimating a proportion, if I know some proportions are not possible?
Now, for a more complex use circumstance. The documentation claims Business solutions that each new view dimension need to either be described as a subspace of an original dimension, or only span
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The new condition should be appropriate with the initial form. If an integer, then the result will likely be a one-D array of that duration. A single condition dimension is usually -one. In this circumstance, the value is inferred through the size in the array and remaining Proportions.