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Take smaller bites and chew slowly. Appreciate the aroma, texture, and flavors, rather than gulping down your food. Eat at the table, not at the kitchen counter. This will help you relax, slow down, and appreciate the food's flavors and aromas. Get in the habit of putting smaller amounts of food on your plate and on your fork. Don't overload, either. Set down your fork between bites. Pick it up again after you've chewed and swallowed your food. Americans have a habit of filling their fork for a second bite before they've finished the first. Food-in-hand quickly becomes food-inmouth.
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With these definitions, most of the interesting measures can be defined. But the special case of models containing absorbing states, like those in Fig. 2.8, deserves additional attention. Here, it would be interesting to compute measures based on the time a CTMC spends in non-absorbing states before an absorbing state is ultimately reached. Let the state space S = A U N be partitioned into the set A of absorbing and the set N of non-absorbing (or transient) states. Then, the time spent before absorption can be calculated by taking the limit limt+oo LN(t) restricted to the states of the set N. Note that, in general, unless very special cases for the initial probability vector are considered, the limit does not exist for the states in A. Therefore, to calculate LN(oo), the initially given infinitesimal generator matrix Q is restricted to those states in N, so that matrix QN of size IN] x IN] results. Note that QN is not an infinitesimal generator matrix. Restricting also the initial probability vector ~(0) to the non-absorbing states N results in TN(O) and allows the
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FIGURE 9.31
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Depending on SF, the initial probability vector a(O) is given by 5; (0) according to Eq. (5.58) or by 5(O) according to Eq. (5.59). The aggregation part has now completely been described. With this technique, the transient analysis of large stiff Markov chains can be reduced to the analysis of smaller non-stiff chains. Transient analysis is accomplished only for the macro states in the model, be it either the initially present slow states or the macro states resulting from an aggregation of fast recurrent subsets of states. The accuracy of the method is inversely proportional to the stiffness ratio of the initial model. With the assumption that steady state is reached much quicker for the fast states than for the slow states, further approximations become possible. Applying certain disaggregation steps, approximations of the transient fast state probabilities can also be derived, thus resulting in a complete state probability vector.
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worthless and keep the net credit. The maximum pro t, however, occurs if the underlying asset falls sharply. In this case, the maximum pro t is equal to $2,725. The maximum risk, meanwhile, is limited and equal to the difference in the strike prices times the number of short options minus the net credit. 15. What is the difference between a forward and a reverse volatility skew Answer: With a forward price skew, the options with the higher strike prices have higher implied volatility than the options with lower strike prices. The reverse volatility skew, in contrast, will have higher implied volatility in the lower strike prices when compared to the higher strike prices. Discussion: Ratio backspreads and other types of vertical spreads can take advantage of volatility skews. For example, with a forward skew, strategists can sell the options with the higher strike price and buy those with the lower strike price. On the other hand, strategists can buy the higher strike price and sell the lower one if a reverse volatility skew exists. The type of spread, and whether to use puts or calls, will depend on the outlook for the underlying asset. For instance, if a contract has a reverse skew and the strategist is bullish on the underlying asset, a call ratio backspread might be the appropriate strategy to employ.
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p12 = 0.5 p13 = 0.5 p24 = 0.9
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11 Life and Death at the Smallest Scale (pp. 201 221)
Figure 5.16 Feedforward mode (hash function).
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Backer, Rogers & Sopory (1992, p. xiv) also suggest mass media campaigns have bene ted by setting more modest achievable campaign objectives and recent projects, such as the New Zealand Community Alcohol Project, have bene ted by setting realistic objectives (Casswell, Ransom & Gilmore, 1990). Early campaigns that sought to create individual behaviour change focused their outcome evaluations on identifying signi cant communitywide changes in behaviour. This simplistic approach did not acknowledge the complex nature of the process of change or the complexities involved in the communication of information. More often than not, there are a range of intermediary steps that can be identi ed between campaign implementation and large-scale community change. For example, a campaign targeting binge drinkers, who are unaware that their drinking behaviour is de ned as binge-drinking , will not engage the target group. Such a campaign, targeting a group of people in a precontemplation stage, should have problem awareness as the primary
G = {1,2,. . . ,m).
3.3.1 Gaussian Elimination
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