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The implementation of sample parametric models requires knowledge of the probability laws of target random elements.
Please note that this policy requires the aggregator to have perfect knowledge of the probability of success in the channels.
The selection of the best channel requires the knowledge of the probability of collision in the channels.
The evaluation of risk in food safety requires knowledge of the probability that microbial population sizes will not exceed defined levels.
This approach has the advantage that is does not require numerical simulations nor does it require the knowledge of the probability density function of the input parameters.
However, obtaining the bit-rate subject to a certain objective bit error rate (BER) requires the knowledge of the probability distribution of the noise and the distortion.
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We note here that θ can be different from α and the notion (g_{t}(theta)) contains a broad class of information that can be formulated from the knowledge on the probability distribution of (X_{t}), e.g. the moment and their generalizations [7].
> -wrap-foot> True optimization requires knowledge of the probabilities we want to estimate, which is obviously circular.
Assuming that class prior probabilities do not vary, knowledge of the prior probability and class-conditional probability density function for each class allows the Bayes decision rule to be optimal in the sense that it minimizes the probability of error or the expected cost [ 5, 33].
Without the knowledge of x1, the probability for A to generate η is just 2-k.
However, the receiver will make use of the knowledge of the error probability at the relay.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com