Abstract
Weak typicality requires that the empirical entropy of a sequence is close to the true entropy. In this chapter, we introduce a stronger notion of typicality which requires that the relative frequency of each possible outcome is close to the corresponding probability. As we will see later, strong typicality is more powerful and flexible than weak typicality as a tool for theorem proving for memoryless problems. However, strong typicality can be used only for random variables with finite alphabets. Throughout this chapter, typicality refers to strong typicality and all the logarithms are in the base 2 unless otherwise specified.
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© 2002 Springer Science+Business Media New York
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Yeung, R.W. (2002). Strong Typicality. In: A First Course in Information Theory. Information Technology: Transmission, Processing and Storage. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8608-5_5
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DOI: https://doi.org/10.1007/978-1-4419-8608-5_5
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4613-4645-6
Online ISBN: 978-1-4419-8608-5
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