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accurate. There was no written core of the Greek religion, no equivalent of the Bible or the Koran, instead there was a complex web of myth, stories told to illustrate the nature of divinity, always combining entertainment with education. This exible structure meant that even the gods themselves changed with time. Originally it was Helios who rode the sun s chariot across the sky, for example, but he became absorbed into the central gure of Apollo, son of Zeus. In these ever-changing tales, the most striking human interaction with light came in the experience of Daedelus and Icarus. Daedelus was an inventor, said to have designed the labyrinth for King Minos of Crete that contained the half-man, half-bull monstrosity the Minotaur. Architects of secret structures of the time were sometimes killed to destroy their knowledge. It might have been better for Minos if Daedalus had suffered that fate. Instead the inventor gave the secret of the labyrinth to the king s daughter, Ariadne, who passed it on to her lover, Theseus. After Theseus managed to kill the Minotaur and escape, Daedelus was imprisoned along with his son Icarus. Daedelus built wings of wax and feathers so the two of them could y away to safety, but Icarus was too bold, revelling in the freedom of ight as he soared higher and higher. Forgetting his purpose, he trespassed on the territory of the sun. The heat of the sun s rays melted the wax on his wings, leaving Icarus to plunge to his death in the sea. This story was a pointed parable, demonstrating the



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(13.5) - P(x))-'M(x)l 2 2v x =exp(-REb /4No) where we have used the matrix identity ( I - P)-' = EEoP i , analogous to the identity for scalars 1 1 p p 2 p 3 . . - . The identity holds when hmax< 1, the largest = 1 -P eigenvalue of P ( x ) . When the code is noncatastrophicand the SNR is sufficiently large, this is the case. The bound (13.5) is referred to as the transferfunction bound. Computationally, actually computing this bound could be difficult, since it requires computing the inverse of a ( N 2 - N ) x ( N 2 - N ) matrix, where N = 2'. A tighter bound can be obtained (see, e.g., [303, p.1311) by using a tight union bound for path differences up to a certain length, then employing the transfer function bound for the tail.





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This can be derived as follows, where we integrate first over x and then over y, which is simpler than integrating in the opposite order, where we would have to add the two contributions, one over the square and the other over the triangle, which would be somewhat cumbersome. Solving the equation for the right boundary y for x, we have x and thus 1 I 2 y

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This concept of selecting the simplest model is often referred to as Occam s Razor Separating Test and Training Sets The goal of building a predictive model is to generalize the relationship between the input descriptors and the output responses The quality of the model depends on how well the model is able to predict correctly for a given set of input descriptors If the model generalizes the input/output relationships too much, the accuracy of the model will be low If the model does not generalize the relationships enough, then the model will have dif culties making predictions for observations not included in the data set used to build the model Hence, when assessing the quality of the model, it is important to use a data set to build the model, which is different from the data set used to test the accuracy of the model.

Let us consider real-valued processes de ned on [0, 1] (extensions to the whole line do not present essential dif culties). We need the following function spaces:

1 . k

will look at both the variation within the groups and the variation between the groups. The test has the following steps: 1. Calculate group means and standard deviations 2. Determine the within group variation 3. Determine the between group variation 4. Determine the F-statistic, using the within and between group variation 5. Test the signi cance of the F-statistic The following sections describe these steps in detail: Calculate Group Means and Variances In Table 5.9, for each call center a count along with the mean and variance has been calculated. In addition, the total number of groups is listed (k 4) and the total number of observations (N 29). In addition, an average of the means (x 140:8) is calculated by taking each mean value for each call center and dividing it by the number of groups: x 139:1 129:9 142:4 153:7 141:3 4

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