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Complex business processing at the client side demands robust client systems. Security is more difficult to implement because the algorithms and logic reside on the client side making it more vulnerable to hacking. Increased network bandwidth is needed to accommodate many calls to the server, which can impose scalability restrictions. Maintenance and upgrades of client applications are extremely difficult because each client has to be maintained separately. Client-server architecture suits mostly database-oriented standalone applications and does not target robust reusable componentoriented applications.

PROPOSITION. pQ(At,T , T | S) is a lognormal density of the Black-Scholes form: (6.10) 2 ln ST A S dAt ,T 1 (6.10) p Q (At ,T , T | S, t )dAt ,T = exp 2 2 2 A At ,T 2 A 2 where A = r qA A/2. The volatility of the continuous geometric average is A = / 3 and the risk-neutral drift of the continuous geometric average is r qA = 1 2 r q 2 6

Thus, under the forward measure the stochastic discount factor and the future cash ow are decoupled (separable). Since the cash ow depends only on the state of the economy at time T, the preceding expectation can be calculated if one knows the joint distribution of the state variables in the economy at time T conditional on time t. In particular, if the model is Gaussian, the state variables at time T will have a joint normal distribution and the expectation can be calculated in closed form. The relationship between the two domestic forward measures of different maturities T and S, T S on the interval [0, S] is dzT(t) = dzS(t) ( P(t, T) p(t, S))dt Returning to our bond pricing formula, we have C(t) = P(t, T)ET [max(F(t; T, S) X, 0)] t where the expectation is with respect to the forward bond price process: dF(t ; T , S) = ( P (t , S) P (t , T ))dz T (t) F(t ; T , S) The forward bond price is a martingale and its instantaneous volatility is deterministic. Thus, the expectation is given by the Black-Scholes formula: C(t) = P(t, T)[F(t; T, S)N(d+) XN(d )] where F(t; T, S) is the forward bond price given in (10.60), d = F(t ; T , S) P (t ; T ; S) 1 ln P (t ; T , S) 2 X (10.70) (10.69) (10.68)

evidence refers to particular observations in the training set that either support or do not support a particular prediction. Naive Bayes models have the following restrictions:

If you omit this argument, the function uses the range specified in the first argument. The examples that follow demonstrate the use of the SUMIF function. These formulas are based on the worksheet shown in Figure 7-14, set up to track invoices. Column F contains a formula that subtracts the date in column E from the date in column D. A negative number in column F indicates a past-due payment. The worksheet uses named ranges that correspond to the labels in row 1.

You can see that the validation criteria we established earlier are tested against the data that came over from our request. Because we did not enter values for UserID and password, they both evaluate to null. The error messages error.userid and error.password are retrieved from the properties file we discussed earlier and then added to a list of errors before being returned to the controller. A common error for new users is to omit the errors.header and errors.footer definitions in the properties. This results in <null> being displayed for the header when the <html:errors/> tag processes the error map. The relevant portion of ApplicationResources.properties is shown below:

get()

Summary