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This book is intended to introduce coding theory and information theory to undergraduate students of mathematics and computer science. It begins with a review of probablity theory as applied to finite sample spaces and a general introduction to the nature and types of codes. The two subsequent chapters discuss information theory: efficiency of codes, the entropy of information sources, and Shannon's Noiseless Coding Theorem. The remaining three chapters deal with coding theory: communication channels, decoding in the presence of errors, the general theory of linear codes, and such specific codes as Hamming codes, the simplex codes, and many others
0. reliminaries; Miscellany; Some Probability; Matrices
1. An Introduction to Codes Strings and Things; What are codes? Uniquely Decipherable Codes; Instantaneous Codes and Kraft's Theorem
2. Efficient Encoding Information Sources; Average Codeword Length; Huffman Encoding; The Proof that Huffman Encoding is the Most Efficient
3. Noiseless Coding Entropy; Properties of Entropy; Extensions of an Information 1= Source; The Noiseless Coding Theorem II Coding Theory
4. The Main Coding Theory Problem Communications Channels; Decision Rules; Nearest Neighbor Decoding
5. Linear Codes
6. Some Special Codes
7. An Introduction to Cyclic Codes
Answers to Selected Odd-Numbered Exercises
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