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There are two parts to the answer. One part is about how computers are designerad internally. There is altardy a good answer to that. The other part is about information theory. In information theory the unit is bits.

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"Symbol Codes" means codes that work over a (finite) alphabet with an arbitrary size. Many times people focus only on binary codes, and Shannon Thm says that the minimal code length of some random variable $X$ is between $H(X)$ and $H(X)+1$ bits. But what if, instead of bits, you use trits, or symbols from a larger alphabet of size, say, $a$? Then, the ...

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Well, the fact is that binary values are much easier to handle technically that ternary values. And there are reasons for it. So if there was some technical breakthrough where suddenly ternary values become easy to handle, there would be a good reason to believe that binary values would have a similar breakthrough very soon. So yours is a purely ...

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Maximizing $P(r|t)$ is known as the Maximum Likelihood (ML) principle, while maximizing $P(t|r)$ is known as the Maximum A Posteriori probability (MAP) principle. Here are some facts that answer your question: Optimal decoding (minimizing the probability of error) is achieved by the MAP principle. See p. 152 of the book. However, using MAP would ...

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