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Not all uncertain quantities involve simple probabilities of discrete events. Many of the parameters needed for engineering analysis have a realized value along a scale, and the issue facing expert elicitation is to assess the uncertainty about the parameter s value over that scale. For example, the base friction between a concrete mass and its foundation, measured as a drained friction angle, could in principle have a value anywhere between zero and ninety degrees. Uncertainty about the interval of this scale in which the actual value lies is described by a probability distribution. A probability distribution summarizes the relative uncertainty about the parameter s value lying within speci c intervals of the scale. In expert elicitation it is often convenient to represent probability distributions as cumulative functions, which graph the scale of the parameter along the horizontal axis and the (judgmental) probability that the realized value of the parameter is less than speci c values along the vertical axis. The process starts by asking the expert to suggest extreme values for the uncertain quantity. It is useful to have the expert describe ways that values outside these extremes might

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that inherit from ListItem, each with its own Dump method that is specifically designed to dump the data for that particular type of item.

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As you ve just witnessed, reversing IL code is far easier than reversing native assembly language such as IA-32. There are far less redundant details such as flags and registers, and far more relevant details such as class definitions, local variable declarations, and accurate data type information. This means that it can be exceedingly easy to decompile IL code back into a high-level language code. In fact, there is rarely a reason to actually sit down and read IL code as we did in the previous section, unless that code is so badly obfuscated that decompilers can t produce a reasonably readable high-level language representation of it. Let s try and decompile an IL method and see what kind of output we end up with. Remember the AddItem method from Listing 12.4 Let s decompile this method using Spices.Net (9Rays.Net, www.9rays.net) and see what it looks like.

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occur Then, the expert is asked to assess probabilities that values outside the extremes occur Starting with extreme values rather than best estimates is important in guarding against overcon dence and anchoring Asking the expert to conceive extreme scenarios makes those scenarios available , and allows the expert to think about the extremes more readily As numerical values are elicited, the facilitator should begin plotting these on graph paper; however, at this point the plot should not be showed to the expert, because it might bias future responses to conform to the previous ones As ever more assessments are made, these are plotted on the graph to begin establishing bounds and to point out inconsistencies After establishing extreme values and their exceedance probabilities, the next step is to ll in values and their corresponding probabilities between the extremes.

public virtual void AddItem(ListItem NewItem) { NewItem.Next = ListHead; if (ListHead != null) { ListHead.Prev = NewItem; } ListHead = NewItem; }

As in the beginning, it is good practice to avoid early assessment of the mean or mode and rather to focus on values without particular signi cance to the expert The judgmental probability that the realized value of the uncertain quantity is less than a speci c number can be elicited using comparative gambles, a probability wheel, or other aid, as discussed above The plot of assessed values is used to identify gaps where more assessments are needed, and inconsistencies in the expert s responses where further assessments or clari cation is called for Toward the end of the process, the interval technique might be used to elicit the median and quartiles of the distribution In checking for consistency, it is useful to compare numerical results elicited as values with those elicited as probabilities.

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