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An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
"This book is recommended reading, both as a textbook and as a reference." (Computing Reviews.com, December 28, 2006)
Information theory answers two fundamental questions in communication theory: what is the ultimate data compression (answer: the entropy H), and what is the ultimate transmission rate of communication (answer: the channel capacity C). For this reason some consider information theory to be a subset of communication theory. We will argue that it is much more. Indeed, it has fundamental contributions to make in statistical physics (thermodynamics), computer science (Kolmogorov complexity or algorithmic complexity), statistical inference (Occam's Razor: "The simplest explanation is best") and to probability and statistics (error rates for optimal hypothesis testing and estimation).
Figure 1.1 illustrates the relationship of information theory to other fields. As the figure suggests, information theory intersects physics (statistical mechanics), mathematics (probability theory), electrical engineering (communication theory) and computer science (algorithmic complexity). We now describe the areas of intersection in greater detail:
Electrical Engineering (Communication Theory). In the early 1940s, it was thought that increasing the transmission rate of information over a communication channel increased the probability of error. Shannon surprised the communication theory community by proving that this was not true as long as the communication rate was below channel capacity. The capacity can be simply computed from the noise characteristics of the channel. Shannon further argued that random processes such as music and speech have an irreducible complexity below which the signal cannot be compressed. This he named the entropy, in deference to the parallel use of this word in thermodynamics, and argued that if the entropy of the source is less than the capacity of the channel, then asymptotically error free communication can be achieved.
Information theory today represents the extreme points of the set of all possible communication schemes, as shown in the fanciful Figure 1.2. The data compression minimum I(X; X) lies at one extreme of the set of communication ideas. All data compression schemes require description rates at least equal to this minimum. At the other extreme is the data transmission maximum I(X; Y), known as the channel capacity. Thus all modulation schemes and data compression schemes lie between these limits.
Information theory also suggests means of achieving these ultimate limits of communication. However, these theoretically optimal communication schemes, beautiful as they are, may turn out to be computationally impractical. It is only because of the computational feasibility of simple modulation and demodulation schemes that we use them rather than the random coding and nearest neighbor decoding rule suggested by Shannon's proof of the channel capacity theorem. Progress in integrated circuits and code design has enabled us to reap some of the gains suggested by Shannon's theory. A good example of an application of the ideas of information theory is the use of error correcting codes on compact discs.
Modern work on the communication aspects of information theory has concentrated on network information theory: the theory of the simultaneous rates of communication from many senders to many receivers in a communication network. Some of the trade-offs of rates between senders and receivers are unexpected, and all have a certain mathematical simplicity. A unifying theory, however, remains to be found.
Computer Science (Kolmogorov Complexity). Kolmogorov, Chaitin and Solomonoff put forth the idea that the complexity of a string of data can be defined by the length of the shortest binary program for computing the string. Thus the complexity is the minimal description length. This definition of complexity turns out to be universal, that is, computer independent, and is of fundamental importance. Thus Kolmogorov complexity lays the foundation for the theory of descriptive complexity. Gratifyingly, the Kolmogorov complexity K is approximately equal to the Shannon entropy H if the sequence is drawn at random from a distribution that has entropy H. So the tie-in between information theory and Kolmogorov complexity is perfect. Indeed, we consider Kolmogorov complexity to be more fundamental than Shannon entropy. It is the ultimate data compression and leads to a logically consistent procedure for inference.
There is a pleasing complementary relationship between algorithmic complexity and computational complexity. One can think about computational complexity (time complexity) and Kolmogorov complexity (program length or descriptive complexity) as two axes corresponding to program running time and program length. Kolmogorov complexity focuses on minimizing along the second axis, and computational complexity focuses on minimizing along the first axis. Little work has been done on the simultaneous minimization of the two...
|1||Introduction and preview||1|
|2||Entropy, relative entropy, and mutual information||13|
|3||Asymptotic equipartition property||57|
|4||Entropy rates of a stochastic process||71|
|6||Gambling and data compression||159|
|10||Rate distortion theory||301|
|11||Information theory and statistics||347|
|13||Universal source coding||427|
|15||Network information theory||509|
|16||Information theory and portfolio theory||613|
|17||Inequalities in information theory||657|
Posted February 4, 2007