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The fast Kalman filter is another efficient RLS implementation (actually the earliest such implementation); its equations are in essence similar to what we have done so far for FTF and FAEST except that they rely on propagating the gain vector g M , i instead of its normalized version C M , ~ . The update of g M , i is based on equations (38.13) and (38.23), as we shall explain below. Also, the fast Kalman filter does not use the conversion factors to

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maintenance margin of 35 percent, the fund would get a margin call if the value of the positions fell below $1,538,461.54. The remaining value of the margin is $538,461.54 after subtracting the $1 million loan balance from the total value. This margin exactly equals 35 percent of the position. If the value of the position fell below $1,538,461.54, the hedge fund would need to either post additional margin in cash or sell part of the position until the maintenance requirement is met. If the fund failed to meet this margin maintenance voluntarily, the broker would sell positions to satisfy the requirement. More generally, suppose the margin maintenance requirement was Maintain%. The current loan amount is Loan, and the minimum margin for that loan amount is Margin. The margin requirement can be stated algebraically as: Maintain% = which is reordered as follows: Maintain% Margin + Maintain% Loan = Margin Maintain% Loan = (1 Maintain%) Margin Maintain% Loan = Margin (1 Maintain%) (6.2) (6.3) (6.4) Margin Margin + Loan (6.1)

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(5.61)

2 3 4 5

where p = 0, 1, . . . , L, p0 is the Kronecker delta and M is a diagonal positive de nite matrix. 11.2.3 Derivation of the Natural Gradient Algorithm for State Space Model

(15.29)

Figure 10-32: In the Layers palette, duplicate the original photo and convert the upper layer to greyscale. Then, when you use the eraser on the upper layer, it will reveal portions of the original color photo. The result can look like Figure 10-31. (Layers palette from Photoshop, courtesy of Adobe Systems)

4. Shift-select the three hotspots in the Web layer and drag them into the Navigation sub layer (Figure 5-30).

1. Knowledge creation or acquisition 2. Knowledge modification 3. Immediate use 4. Archiving 5. Transfer 6. Translation/repurposing 7. User access 8. Disposal

29

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To access the content of the CD, follow these steps: 1. Insert the CD into your computer s CD-ROM drive. 2. A window appears with the following options: Explore: Enables you to view the contents of the CD-ROM in its directory structure. Software: Gives you the option to install the supplied software on the CD-ROM. Files: Allows you to view the bonus files provided with the CD. Links: Opens a hyperlinked page of Web sites. Exit: Closes the autorun window. If you do not have autorun enabled or if the autorun window does not appear, follow the steps below to access the CD. 1. Click Start Run. 2. In the dialog box that appears, type D:\SETUP.EXE, where D is the letter of your CD-ROM drive. This brings up the autorun window described above. 3. Choose the Explore, Software, Files, Links, Exit option from the menu. See Step 2 of the preceding list for a description of these options.

The matrix R can be regarded as the covariance matrix of the column vector coI{z, y}, namely, (1.16) where the symbol T denotes vector transposition, and the notation col{cr, p } denotes a column vector whose entries are a and p. As explained in Sec. A.4, every such covariance matrix is necessarily symmetric, R = RT. Moreover, R is also nonnegative-definite, written as R 2 0. To proceed with the analysis, we are going to assume that the covariance matrix R is positive-definite and, hence, invertible - see Prob. 1.6. Now the joint pdf of two jointly Gaussian random variables {z, y} is given by (see Sec. A.5 for a review of Gaussian random variables and their probability density functions):

where g a r g b E R. The solution u is sought in the form u = U G, where G E H'(R) is a Dirichlet lift satisfying G(b) = gb. and the new unknown function U lies in the space V defined in (1.65),

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