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In particular, the problem of structural learning is exacerbated in the presence of hidden variables, because the hidden variables and the structure between them and the observables have to be inferred.
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If the initial values of the hidden variables were known, it would be possible to predict which nuclei would decay.
Figure2a also depicts the causality relationship between the hidden variables and the observed variables.
The formula offered a litmus test for determining whether the hidden variables were really there.
If the hidden variables were exposed to the light of day, he argued, the apparent statistical predilection of nature would evaporate.
The hidden variables are oriented in a hierarchical structure.
The hidden variables cannot be directly observed from experiments.
Often, we want to infer the value of the hidden variables from the observed variables.
Briefly, EM learns parameters in models with hidden variables by iterating between inferring the probabilities of hidden variables and changing parameters to maximize the likelihood given the probabilities of the hidden variables.
This study provides useful information in handling the hidden variables and improving the inference precision.
It is easy to see that the hidden variables follow a hierarchical structure.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com