Decision rules

The decision rules for each of the three forms are defined as follows If the sample t falls within the acceptance region, accept Hq for lack of contrary evidence. If the sample t falls in the critical region, reject Hq at a significance level of lOOot percent. 

The decision rule for each of the three forms would be to reject the null hypothesis if the sample value oft fell in that area of the t distribution defined by Ot, which is called the critical region. Other wise, the alternative hypothesis would be accepted for lack of contrary evidence. 

Revenues are summed from an initial period thronghont the economic lifetime of the investment. The business decision rule is that an investment is profitable when its net present value is positive. If revenues accrue at a constant rate, continuously and forever, the equation becomes simpler. In the inequality I < R/r, an investment is profitable il aimiial revenue, R, divided by the discount rate, r. 

What is most surprising about many of the runs using GA-derived personalities is that the red force appears to task different agents with different missions, despite the fact that each red agent is endowed with exactly the same personality Thus, a higher-level tactic - such as use the two forward positioned agents to weaken the enemy s center - emerges out of the collective interactions of the same low-level decision rules i.e., an apparently centralized order induced by decentralized local dynamics. 

Yeh and Spiegelman [24], Very good results were also obtained by using simple neural networks of the type described in Section 33.2.9 to derive a decision rule at each branching of the tree [25]. Classification trees have been used relatively rarely in chemometrics, but it seems that in general [26] their performance is comparable to that of the best pattern recognition methods. 

In general, it is found that prediction ability is somewhat less good than recognition ability. If the prediction and the recognition ability are substantially different, this means that the decision rules depend too much on the actual objects in the training set the solution obtained is not stable and should therefore not be trusted. 

Because a hyperplane corresponds to a boundary between pattern classes, such a discriminant function naturally forms a decision rule. The global nature of this approach is apparent in Fig. 19. An infinitely long decision line is drawn based on the given data. Regardless of how closely or distantly related an arbitrary pattern is to the data used to generate the discriminant, the pattern will be classified as either o>i or <02. When the arbitrary pattern is far removed from the data used to generate the discriminant, the approach is extremely prone to extrapolation errors. 

This function provides a convenient means of determining the location of an arbitrary pattern x in the representation space. As shown, patterns above the hyperplane result in z(x) > 0, while patterns below generate values of z(x) < 0. The simplest form of the decision rule then is... 

Class membership is assigned using some decision rule that is typically some inequality test performed on P(co,. r ). 

The most straightforward decision rule that can be employed is what is referred to as the 1-nearest neighbor (1-NN) rule. This rule assigns a pattern x of unknown classification to the class of its nearest neighbor Zj from the set of known patterns and pattern classes ... 

Economic evaluation has shown7 that in order to maximize benefits in a context of budget restraint, it is necessary to calculate the updated costs and benefits of various possible investments. The resulting decision rule will depend on the nature of the decision ... 

The answer given by economic evaluation to the question of whether a given product should be financed or not seems to be fairly clear. However, the decision rules mentioned above clash with certain problems, and neither these problems nor some possible solutions should be overlooked. 

Problem 3 The decision rules mentioned above assume that projects are perfectly divisible. 

Karlsson, G. and M. Johannesson (1996), The decision rules of cost-effectiveness analysis , PharmacoEconomics, 9,113-20. 

A variety of decision rules were also provided to further assist clinicians in the use of the DSM-III and, ultimately, to increase interrater reliability. These decision rules pertain to specific criteria as well as to differential diagnoses and are presented to give clinicians more guidance and allow for less leeway in making diagnostic decisions. The diagnostic process is effectively... 

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