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PARASITIC RESISTANCES, CAPACITANCES, AND INDUCTANCES
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// Call getSerialPortInfo to determine if any // Bluetooth serial ports are available for you to use BluetoothSerialPortInfo[] info = i BluetoothSerialPort.getSerialPortInfo(); if( info == null || info.length == 0 ) { // no Bluetooth ports were found, give the user the bad // news Dialog.alert( Sorry! No Bluetooth serial ports i are available! ); } else { // Great - a Bluetooth serial port is available! bluetoothConnection = (StreamConnection)Connector.open( info[0].toString(), Connector.READ_WRITE ); outputStream = bluetoothConnection.openDataOutputStream(); // Create a string object with the text value I want to print
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If bandwidth is de ned as ( 2 1)/ 0 for a given V, the Q bandwidth product becomes: Q BW = V 1 (antenna only) 2 V (1.14)
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Although it is common among practitioners to write the Monte-Carlo version of their Hull White model in the spot measure, for the sake of brevity our implementation is in the terminal measure. The implementation of a state component suitable for Monte-Carlo pricing shown below has been taken from the module ppf.model.hull white.monte carlo.state.
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portfolio. Among industry types in the United States that are subject to more formal restrictions on the way they can invest, banking and insurance are most certainly at the top of the list. With banks, restrictions exist with investing in any type of equity product, as well as having to designate if the investments they have made are held for portfolio (a long-term investment) or available for sale (a short-term investment). Another restriction on bank investments relates to credit considerations. In particular, banks often are required by the government where they operate to follow strict formulas for how much capital must be set aside relative to the types of securities they have purchased. Many times guidelines are taken directly from the Bank of International Settlements (BIS). For example, in 1988 the BIS released a document covering credit risk. The document outlines how different asset classes can be weighted in a capital-at-risk according to a security s riskiness. There are five risk weightings: 0 percent, 10 percent, 20 percent, 50 percent, and 100 percent. OECD (Organization for Economic Cooperation and Development) government debt or cash, for example, has a zero or low weight, loans on banks get 20 percent, while loans fully secured by mortgages on residential property are weighted at 50 percent. All claims on the private sector or on banks incorporated outside the OECD with a residual maturity of over one year are weighted at 100 percent. To allow for a more dynamic approach to risk-based capital guidelines, the BIS has issued a new framework for credit risk. The new framework is designed to improve the way regulatory capital reflects underlying risk, and it consists of three pillars: 1. Minimum capital requirements 2. Supervisory review of capital adequacy 3. Market discipline
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Let us assume as in the relational example that we know that attribute A has value a4 . Obviously the corresponding probability distributions for B and C can be determined from the three-dimensional distribution by restricting it to the slice that corresponds to A = a4 , i.e. by conditioning it on A = a4 , and computing the marginal distributions of that slice . This is demonstrated in Figure 3.12. Note that all numbers in the slices corresponding to other values of attribute A are set to zero, because now these are known to be impossible. Note also that the probabilities in the slice corresponding to 1 A = a4 have been renormalized by multiplying them by P (A=a4 ) = 1000 in 280 order to make them sum up to 1 (as required for a probability distribution). However as in the relational example distributions on two-dimensional subspaces are also sufficient to draw this inference; see Figure 3.13. The information A = a4 is extended to the subspace {A, B} by multiplying the joint probabilities by the quotient of posterior and prior probability of A = ai , i = 1, 2, 3, 4. Then the marginal distribution on B is determined by summing over the rows. In the same way the information of the new probability distribution on B is propagated to C: the joint distribution on {B, C} is multiplied with the quotient of prior and posterior probability of B = bj , j = 1, 2, 3, and the marginal distribution on C is computed by summing over the columns. It is easy to check that the results obtained in this way are the same as those of corresponding computations on the three-dimensional domain.
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