Coding and Information Theory

alphabet amount apply assume average base binary bits block bound called channel Chapter clearly code word column conditional consider corresponding course decoding defined definition depends detection digits distance distribution double encoding entropy equal equation error correction exactly example Exercises expect extension Figure final follows frequency function given gives going Huffman code idea independent inequality information theory input least length less letters log₂ look Markov process matrix maximum means method modulus names necessary noise Note occur original P₁ parity check particular polynomial position possible practice predict prime probability problem prove random received remainder result sent sequence shows signaling simple single error space sphere Suppose symbols syndrome term theorem theory tion tree uniquely usually variable write zero