By Julia Pet-Edwards, Yacov Y. Haimes, Vira Chankong, Herbert S. Rosenkranz, Fanny K. Ennever (auth.)
The problems linked to making threat tests at the foundation of experimental effects are time-honored to practitioners in lots of fields engineering, epidemiology, chemistry, and so forth. those problems are rather universal in difficulties that experience dynamic and stochastic features pushed by means of a number of reasons and objectives, with complicated interconnections and inter dependencies. buying a suitable facts base, processing and reading version effects, and transmitting those effects at a suitable technical, social, political, and institutional point are extra problems that has to be addressed. This booklet is grounded at the premise that dangers are top assessed at the foundation of experimental effects and sound mathematical analyses, coupled with the data of specialists. The carcinogenicity prediction and battery choice (ePBS) technique defined herein presents a scientific mechan ism-a synthesis of platforms and statistical and choice analyses-to reduction researchers and choice makers within the serious box of carcinogenicity prediction in picking a suitable battery of checks to exploit and in translating experimental effects into details that may be used as an relief to determination making.
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Extra resources for Risk Assessment and Decision Making Using Test Results: The Carcinogenicity Prediction and Battery Selection Approach
A decision node is then assigned to each outcome branch, to begin the next stage. The process is then repeated at each decision node until the final stage. Finally, at the tip of each terminal branch, the corresponding payoff or utility is assigned. The next step is to compute the probability associated with each outcome branch, which represents the probability of getting that particular outcome conditional on the actions and outcomes on the branches leading to it. For example, the probability associated with terminal branch 1 is the probability that X is a carcinogen given that test A is not used, which is simply the prior probability Pr(CA).
In our problem, the set of alternatives would consist of all feasible testing options. In order to compare the set of 46 Chapter 2 alternatives, a set of attributes or objective measures must be clearly specified. The values of these attributes for a given alternative can be obtained either from models or through subjective judgments. The levels of these attributes serve as yardsticks by which the degree of attainment of the objectives specified in the preceding step can be assessed. In our example, this would involve quantifying the costs, time-efficiencies, and reliabilities of each of the feasible testing options.
These are called the hierarchical clustering methods. Hierarchical methods produce a nested sequence of groupings. A typical algorithm in this class would proceed as follows: Initially, the algorithm would place each data item in a separate cluster or group. Then in each proceeding iteration, a new cluster would be formed from two old clusters based on the measures of alikeness found in the proximity matrix and the algorithm's criterion for "linking" or combining clusters. The algorithm would continue to link pairs of clusters until all data items are in one large cluster.
Risk Assessment and Decision Making Using Test Results: The Carcinogenicity Prediction and Battery Selection Approach by Julia Pet-Edwards, Yacov Y. Haimes, Vira Chankong, Herbert S. Rosenkranz, Fanny K. Ennever (auth.)