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The best way of approaching a race is to decide the main strategy, for there may be several strategies in con ict For example, one side of the course may have more adverse tide but also more wind Sailing is a very dynamic sport: there are often many variables When you have all the available information, you can make an informed decision We.

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This can be extended to more variables: P [ABC] = P [A|BC] P [B|C] P [C] (A.7)

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A very useful result, analogous to the chain rule for partial differentiation is that, if E1 , E2 , . . . , En is a set of mutually exclusive and exhaustive events (that is, they include all possible events Ei ) and A is some event conditional on one or more of the Ei , then P [A] = P [A|E1 ] P [E1 ] + P [A|E2 ] P [E2 ] + + P [A|En ] P [En ]

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P [A|Ei ] P [Ei ]

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will now consider several factors, one at time (assuming that no other factors are applicable). You will notice there is often a link between the upwind and downwind strategy, so when thinking ahead always consider what is currently paying and more importantly WHY. On any particular race day there will be factors that are the most important. For slow boats, current may make the most difference. For faster boats in light winds it may be nding the areas of the course with the most wind. For boats which tack and gybe easily and are at near maximum hull speed, it will be all about the shifts.

Conclusion 13 Decompilation Native Code Decompilation: An Unsolvable Problem Typical Decompiler Architecture Intermediate Representations

(A.8)

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This is known as the Total Probability Theorem. Suppose that there is an exhaustive series of events with estimated probabilities Ei . These are called prior probabilities. Now, suppose some event A occurs. (This might be the result of some measurements or some happening such as a failure.) We want to know how this affects the estimate of the probability of Ei . From the above results, P [AEi ] = P [A|Ei ] P [Ei ] = P [Ei |A] P [A] P [A|Ei ] P [Ei ] P [A|Ei ] P [Ei ] P [Ei |A] = = P [A] P [A|Ei ] P [Ei ] (A.9) (A.10)

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Data-Flow Analysis Single Static Assignment (SSA) Data Propagation Register Variable Identification Data Type Propagation Type Analysis Primitive Data Types Complex Data Types Control Flow Analysis Finding Library Functions

In other words, Equation (A.10) describes how to revise the estimate of the probability of Ei after we have learned that A has occurred. To do this we must know the prior estimates of the probabilities of the Ei s and the conditional probabilities of A. This theorem is known as Bayes Theorem; it forms the basis of much modern reliability work.

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Several well-known functions can be used to describe the uncertainty in a variable These have become widely used because they describe situations that arise in practice, but many variables do not conform to any of the standard functions The distributions are usually divided into two categories: discrete and continuous Discrete distributions describe cases in which the uncertain variable can have only discrete values, such as the value of pips on a die or the number of oods in a given period of time Continuous distributions describe cases in which the uncertain variable can have any value within a given range, such as the amount of rainfall in a year or the shear strength of a soil This section describes discrete distributions, and the next deals with continuous distributions It is customary to refer to the variable by an upper case letter (eg.

The Back End Real-World IA-32 Decompilation Conclusion Appendix A Deciphering Code Structures Appendix B Understanding Compiled Arithmetic Appendix C Deciphering Program Data Index

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