In Chapter 6, we discuss a stronger notion of typicality of sequences, called strong typicality. [BLANK_AUDIO] In section 6.1, we discuss a stronger version of the AEP, called the strong AEP. In this setup, we consider a random process X_k, k greater than or equal to 1, where X_k are i.i.d. with generic distribution p(x). Let X denotes the generic random variable with finite entropy. As before, we denote a random sequence by bold X, then the probability of the X sequence, is equal to, probability of X_1 times the probability of X_2, all the way to probability of X_n, because X_1 up to X_n are independent. Compared with the setup for weak typicality, we make the additional assumption, that the alphabet is finite. Let the base of the logarithm be 2, that is, the entropy of X is in bits. Here are some notations. Consider a sequence of length n. Let N(x;x) sequence be the number of occurrences of a particular value x in the sequence x. Dividing it by n, we obtain the relative frequency of the value x in the sequence x. The collection of relative frequency of x over all x is called empirical distribution of the sequence x. As an example, consider the length 5 sequence consists of the values 1, 3, 2, 1, and 1. Then the number of occurrences of 1 is equal to 3, the number of occurrences of 2 is equal to 1, and the number of occurrences of 3 is equal to 1. The empirical distribution of this sequence x is equal to 3 over 5, 1 over 5 and 1 over 5. [BLANK_AUDIO] In the strongly typical set to be defined, there are two parameters: n, a positive integer, and delta, a small positive quantity. The strongly typical set T sub x delta sup n, with respect to the generic distribution p(x), is the set of sequences of length n, such that, the number of occurrences of x is equal to zero, for all x not in the support, and summation x, the absolute value of the relative frequency of x, minus, the probability of x, is less than or equal to delta. The sequences, in a typical set, are called strongly delta typical sequences. Note that in the above definition, if the summation is small, then so is every term in the summation. That is, the absolute difference between the relative frequency of x, and, the probability of x, is small, for every x in the alphabet. In other words, the relative frequency of x is very close to the probability of x for all x. Therefore, if x is strongly typical, the empirical distribution of x, is approximately equal to the generic distribution p(x). [BLANK_AUDIO] Further, if x is strongly typical, then the probability of x_k is strictly positive for all k, because in equation 1, it is required that for any x not in the support it can not occur in the sequence.