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The MVA was developed by Reiser and Lavenberg [ReLa80] for the analysis of closed queueing networks with product-form solution. The advantage of this method is that the performance measures can be computed without explicitly computing the normalization constant. The method is based on two fundamental equations and it allows us to compute the mean values of measures of interest such as the mean waiting time, throughput, and the mean number of jobs at each node. In these computations only mean values are computed (hence the name). For the case of multiserver nodes (mi > 1)) it is necessary, however, to compute the marginal probabilities. The MVA method is based on two simple laws: 1. Little s theorem, which is introduced in Eq. (6.9) to express a relation between the mean number of jobs, the throughput, and the mean response time of a node or the overall system: K=X.T. (8.30) 2. Theorem of the distribution at arrival time (in short, arrival theorem), proven by [LaRe80] and [SeMi81], for all networks that have a productform solution. The arrival theorem says that in a closed product-form queueing network, the pmf of the number of jobs seen at the time of arrival to a node i when there are Ic jobs in the network is equal to the

of how badly I bombed as a beginner, and then how, when I d garnered a lot more skill, I made the maneuver work My heaviest unplanned losses to date have been caused by incorrectly averaging-down As a beginner I had basic know-how you know, buy low and sell high (yeah right) I would enter knowing what price to sell at, if the price happened to shoot up to my limit price (pro t), and I also had a planned stop/loss price But if the price dropped very quickly or I was having a bad trading day, I would get too emotional I would get angry at the stock I wouldn t apply my stop/loss; instead, I would watch it continue to drop I would then buy more stock at lower price levels I would completely abandon my original plan.

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If the stock continued to drop, I would buy even more without hesitation I did try to nd new support levels, but once I started incorrectly averaging-down, I was dangling, and I had no smart stratagem This would go on until I used up all my capital Sometimes, if I were lucky, the stock would rebound just enough for me to break even You might ask why I didn t allow it to run up into the green Usually I never let it ride any higher because I was so drained from being in over my head that I just wanted out, and quickly In some cases I was down over $1,000 on one single intraday trade, and then it would nally retrace, at the end of the day Once it got back to break-even, I was so stressed that I sold all my positions and bailed out.

I was relieved, but also I felt lucky, because, after all, I was gambling Most trades where I averaged-down unfortunately never rebounded, so I had to sell at a very large loss, or else I held overnight When I held it was because I was hoping, of course, that tomorrow it would go back up And most often, of course, it did not Averaging-down as an amateur, I broke every trading rule I wasn t emotionally disciplined, and I lacked the experience to execute complex technical tricks I felt like a starving puppy, lost and alone in the woods, trying to outfox my prey, and ending up more lost and hungry than ever Later on, when I d gained more experience, I began to average-down with success Eventually I was able to include the device as part of my overall strategy..

In each individual asset class we should seek the best manager (or managers) we can get, regardless of geographic location. With literally thousands of managers to choose from, our adviser should recommend ones for each asset class. What kind of questions should we ask about those recommendations Why does the adviser believe his candidate is the best we can get in that asset class And how does the candidate meet our criteria for the hiring and retention of managers

Figure 15-30: The Brush Presets toolbar lets you quickly select from a selection of predefined brushes.

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