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Probabilistic risk assessment (PRA) is a systematic and comprehensive methodology to evaluate risks associated with a complex engineered technological entity (such as an airliner or a nuclear power plant) or the effects of stressors on the environment (probabilistic environmental risk assessment, or PERA).
Risk management tools help address uncertainty by identifying risks, generating metrics, setting parameters, prioritizing issues, developing responses, and tracking risks. [1] Without the use of these tools, techniques, documentation, and information systems, it can be challenging to effectively monitor these activities.
Example of risk assessment: A NASA model showing areas at high risk from impact for the International Space Station. Risk management is the identification, evaluation, and prioritization of risks, [1] followed by the minimization, monitoring, and control of the impact or probability of those risks occurring. [2]
In project management, risk assessment is an integral part of the risk management plan, studying the probability, the impact, and the effect of every known risk on the project, as well as the corrective action to take should an incident be implied by a risk occur. [40]
Risk is the lack of certainty about the outcome of making a particular choice. Statistically, the level of downside risk can be calculated as the product of the probability that harm occurs (e.g., that an accident happens) multiplied by the severity of that harm (i.e., the average amount of harm or more conservatively the maximum credible amount of harm).
local management aim to keep the plant open despite a desperate need for re-vamping and maintenance work; if the plant is closed down for a short period, if the problems are unattended, there is a risk that it may remain closed permanently.
Performing a probabilistic risk assessment starts with a set of initiating events that change the state or configuration of the system. [3] An initiating event is an event that starts a reaction, such as the way a spark (initiating event) can start a fire that could lead to other events (intermediate events) such as a tree burning down, and then finally an outcome, for example, the burnt tree ...
A probability distribution can be represented by its moments (in the Gaussian case, the mean and covariance suffice, although, in general, even knowledge of all moments to arbitrarily high order still does not specify the distribution function uniquely), or more recently, by techniques such as Karhunen–Loève and polynomial chaos expansions ...