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The example in this section demonstrates an Automation session using Word. The MakeMemos procedure creates three customized memos in Word and then saves each document to a file. The information used to create the memos is stored in a worksheet, shown in Figure 20-6.

The essence of the VA is that when two or more paths arrive at a given time instant and state of the trellis, only one of them would have the minimum cumulative distance, and should be selected from among the others as the survivor In fact, this decision procedure signi cantly reduces the number of distance calculations required Decisions of this kind start to be performed as soon as two or more paths arrive at the same state and time instant of the trellis This happens after time instant t4 in this example, as seen in Figure 618 In this particular example, at time instant t4 , and at successive time instants, decisions can be taken at all the states of the trellis The reason is that there are two paths arriving at each state, but only one of them has the minimum Hamming distance in each case.

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Keep in mind that a user can press Ctrl+Break to break out of the macro. In this example, pressing Ctrl+Break while the UserForm is displayed will cause the UserForm to be dismissed. To prevent this from happening, execute the following statement prior to displaying the UserForm: Application.EnableCancelKey = xlDisabled Make sure your application is debugged before you add this statement. Otherwise, you ll find that it s impossible to break out of an accidental endless loop.

The other path has a higher cumulative Hamming distance, and it is discarded However, it is seen that at time instant t5 , in state Sb , there are two arriving paths that both have the same cumulative Hamming distance In this case the decision is taken by randomly selecting one of these two possible paths as the survivor; the upper path in this example On average, these random selections do not prevent effective operation of the VA: If the error pattern is within the error-correction capability of the code, then the decoded sequence does not pass through the state node concerned; if the code s correction capability has been exceeded, then the decoder fails anyway, and normally outputs a burst of errors in the decoded sequence The discarding procedure is successively applied to the following time instants over each state, now taking into account previous decisions already taken.

themselves: (a) the degree of mathematical speci cation, (b) the way these statistical models are tted, and (c) the clarity and substantive meaning of the results These issues have not been completely resolved, so we end with some comments about each of these topics The Mathematical Basis of Growth Curve Analyses Most current growth curve models can be written in a common symbolic form (Seber & Wild, 1992) That is, a general model for a change in the scores over time (often using derivatives dy/dt or differences y/ t) can be based on some mathematical functional form ( f{x, t}) with unobserved scores (x[t]) and with unknown parameters (A[t]) to be estimated Additional forms not discussed here can be included, such as auto-regressive residual structures, Markov chains, and Poisson processes The list of growth functions described here is not exhaustive, and future extensions to other generalized functions (eg.

, Ramsey & Silverman, 1997) and dynamic and chaotic formulations (May, 1997) are likely It is now clear that growth curve models of arbitrary complexity can be tted to any observed trajectory over time (ie, the integral), and the unknown parameters can be estimated to minimize some statistical function (eg, weighted least squares, maximum likelihood) using, for example, nonlinear programming Several different computer programs were used for the growth curve analyses discussed here For many of the initial analyses, standard SAS programs were used, including PROC MIXED and PROC NLMIXED The Mx-SEM computer program used herein was based on a simpli ed matrix approach to model expectations All of these programs can deal with incomplete data patterns using the likelihood-based incomplete data approach presented earlier.

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