Potential probabilistic

A probabilistic statement of the likelihood of human-error events presents each error in the task analysis as the right limb in a binary branch of the HRA event tree. These binary branches form the chronological limbs of the HRA event tree, with the first potential error siai ting from the highest point on the tree. (Figure 4.5-4). Any given [ask appears as a two-limb branch the left limb represents the probability of success the right limb represents the probability of failure. 

The measure used to describe the potential for noncarcinogenic toxicity to occur in an individual is not expressed as tlie probability of an individual suffering an adverse effect. The EPA does not at tlie present time use a probabilistic approach to estimate tlie potential for noncarcinogenic healtli effects. Instead, tlie potential for non carcinogenic effects is evaluated by comparing an exposure level over a specified time period (e.g., lifetime) witli a reference dose derived for a similar exposure period. Tliis ratio of exposure to toxicity is called a liazard quotient and is described below. (The reader is referred to Chapter 11 for additional details on tlie material tliat follows). The noncancer liazard quotient assumes tliat tliere is a level of exposure (i.e., RfD) below which it is unlikely for even sensitive populations to experience adverse healtli effects. 

Probabilistic techniques of estimation provide some Insights Into the potential error of estimation. In the case of krlglng, the variable pCic) spread over the site A is first elevated to the status of a random function PC c). An estimator P (2c) is then built to minimize the estimation variance E [P(2c)-P (2c) ], defined as the expected squared error ( ). The krlglng process not only provides the estimated values pCiyc) from which a kriged map can be produced, but also the corresponding minimum estimation variances 0 (39 ) ... 

By dividing the cumulative potential function of a class by the number of samples contributing to it, one obtains the (mean) potential function of the class. In this way, the potential function assumes a probabilistic character and, therefore, the density method permits probabilistic classification. 

Partly with the high stakes in mind, changes have been made in U.S. reactor equipment and operation since the TMI accident to reduce the chance of another accident. The results of these changes are reflected in the predictions of probabilistic risk assessments and by a variety of direct performance indicators. For example, in one measure for U.S. reactors, since the pre-TMI days there has been a reduction of more than a factor of 100 in the number of precursors to potential core damage accidents, as reported by the Nuclear Regulatory Commission (Muley, 1990 Belles et al., 1998). 

Only within about the last ten years have probabilistic methods been accepted in evaluation of potential explosion accidents by the Department of Defense in the United States. Such methods have a much longer history of development in Europe, particularly in Switzerland, and consequently are in much wider use there. 

Current evidence strongly suggests the existence of olfactory cues to ovulation in humans. Such cues could be used by men for monitoring their current or potential partner. It should be noted, however, that men cannot determine ovulation exactly but rather in a probabilistic manner. Therefore we believe that cyclic odour cues are rather a byproduct of physiological changes than a specific signal of ovulation. 

The Society of Petroleum Engineers (SPE) and the World Petroleum Congress (WPC) developed a probabilistic hydrocarbon-resource classification scheme, that takes into account the probability with which a reserve can be produced (SPE, 2007) 4 but such a probabilistic assessment is also subject to a potential level of misinterpretation.5 Finally, as for resources, very few estimates exist, and those estimates that do exist are also subject to considerable uncertainty and the speculative character is even more pronounced than for reserves.6 BGR (2003) refers to resources as those quantities that are geologically demonstrated, but at present... 

Despite these problems EST databases are a valuable source of large-scale analysis of human variation. They will become even more valuable as the data continue to grow at the present rate. An algorithm for computer-aided SNP mining should contain filters to eliminate the potential sequence errors. Such filters can be based on the probabilistic analysis of sequence features. It can also take into account that multiple occurrences of a variant are more trustworthy, and it may furthermore focus on improving the quality of base-calling if the fluorescent traces are available for closer srcutiny. 

An important question when planning a probabilistic assessment is whether to separate variability and uncertainty in the analysis and results. This is one of the key issues that were given special consideration at the Pellston workshop that developed this book. While there was not a consensus, the majority view was that there are potential advantages to separating variability and uncertainty, but further case studies are needed to evalnate the benehts and practicality of this for routine pesticide assessment. 

A probabilistic risk assessment (PRA) deals with many types of uncertainties. In addition to the uncertainties associated with the model itself and model input, there is also the meta-uncertainty about whether the entire PRA process has been performed properly. Employment of sophisticated mathematical and statistical methods may easily convey the false impression of accuracy, especially when numerical results are presented with a high number of significant figures. But those who produce PR As, and those who evaluate them, should exert caution there are many possible pitfalls, traps, and potential swindles that can arise. Because of the potential for generating seemingly correct results that are far from the intended model of reality, it is imperative that the PRA practitioner carefully evaluates not only model input data but also the assumptions used in the PRA, the model itself, and the calculations inherent within the model. This chapter presents information on performing PRA in a manner that will minimize the introduction of errors associated with the PRA process. 

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