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Equation 2.14 can be simpli ed to: 1 n Ti n +1 = Fo Ti n 1 - 2 T + Ti n 1 + - Fo i The corresponding equations for a boundary node and an interface node are: for the boundary nodes: 1 n n T1n +1 = 2 Fo BiTe,1 - 1 + Bi T + T2n 2 Fo 1 1 n n n n TN +1 = 2 Fo TN -1 - 1 + Bi T + BiTe, N 2 Fo N for the interface node between two layers of different materials: Ti n +1 = 2 Foa Ti n 1 - (2 Foa + 2 Fob - 1)Ti n + 2 FobTi n 1 + where: Bi = hoDx = Biot number k (2.20) (2.19) (2.17) (2.16)



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was true In this respect, X (bad weather) is independent of Z (John late) given Y (hazardous roads) The general case is given by the following If a Bayesian network is made up of n nodes linked in a linear structure, and if node Nk is instantiated (where k < n), then knowledge of nodes N1 through Nk 1 would not have any effect on nodes Nk+1 through Nn (Figure 816) The second type of connection is a converging structure, in which two causes (X and Z) converge on a single effect (Y ) Obviously, any evidence of either X alone or Z alone, or both together will affect Z Moreover, if there is evidence for both X and Z, then the probability of Z is likely to be greater than if we had only evidence for only X or Z alone.

(17.4)

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1.21 For the (7,4) Hamming code generator polynomial g ( x ) = 1 + x + x3, generate all possible code polynomials c ( x ) . Verify that they correspond to the codewords in (1.35). Take a nonzero codeword c ( x ) and compute c ( x ) h ( x ) modulo x7 + 1. Do this also for two other nonzero codewords. What is the check condition for this code 1.22 Is it possible that the polynomial g(x) = x4 + x 3 x2 1 is a generator polynomial for a cyclic code 1.23 For the parity check matrix H = O l O l O

Further, if there is evidence for Y , then this would also transmit back to its causes, X and Z An example of a converging connection in Figure 814 is mechanical failure and bad weather, two nodes that both cause plane late (Note that in our example, we had evidence for plane late and this transmitted to both causes by increasing the probability of both mechanical failure and bad weather) What about the transmission of information from X to Z If we have no evidence of Y , then X and Z are independent (In our example, if we have no evidence for plane late, then mechanical failure and bad weather are independent of one another) Therefore, evidence of X does not transmit to Z and vice versa, given no evidence of Y However, if something is known about Y , then X and Z become dependent.

(b) 5000 4000 3000 2000 1000 0 1000 2000 3000 160 180

For example, let us suppose that we know that a plane is late Further, let us suppose that we have evidence for mechanical failure We would actually expect the probability of bad weather to decrease, because we would reason that it was the mechanical failure that caused the plane to be late, and not the bad weather In this manner, we have explained away the bad weather as a possible cause On the other hand, if we have evidence for plane late, and we have evidence that would decrease the probability of mechanical failure, then, by analogous argument, we would expect the probability of bad weather to increase The third type of connection is a diverging structure, in which two effects, X and Z, have a common cause Y Certainly, any evidence for Y would transmit to both X and Z.

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