1 probability:
There’s little likelihood of a compromise.
When the joint likelihoods are multiplied, a small transition value is added to each entry.
By sampling the trained neural network, approximations of likelihoods were obtained for design variable values, and, consequently, constraint satisfaction problemsolving capability was achieved.
Only the likelihoods associated with the outcomes that actually occurred are used.
There are all sorts of possible situations which none of us takes seriously as real likelihoods.
If likelihoods can be calculated for alternative models, then a likelihoodratio test may be used to discriminate between hypotheses.
In practice, approximate likelihoods may be used to replace full likelihoods that are computationally not feasible.
In decision-theoretic troubleshooting, costs and likelihoods are balanced in order to find the next action.
However, this problem can be handled by implementing a sparse matrix to represent these joint likelihoods.
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