Glossary of Terms

Analytical Error: The measurement error of an analytical process.

Bayesian Inference: A probabilistic framework using Bayes' Theorem to update the probability distribution of statistical model parameters based on observed data.

Clinical: Related to the diagnosis and treatment of patients.

Complex Systems: Highly structured systems that are very sensitive to the initial conditions. Their behavior is often chaotic as it is characterized by variations that are very hard to be predicted.

Critical Failure: A failure that can initiate hazard.

Diagnostic Accuracy: A measure of the classification accuracy of a diagnostic test. It is characterized by the sensitivity and the specificity of the test.

DNA Barcoding : The species identification of a live organism based on a standardized short sequense of its DNA.

Entropy: A measure of the disorder of a system.

Failure: The termination of the ability of an item to perform a required function.

Genetic Algorithms: Robust search algorithms that do not require knowledge of the objective function to be optimized and can search through large spaces quickly. They were derived from processes of molecular biology and the evolution of life. Their operators, crossover, mutation, and reproduction, are isomorphic with the synonymous biological processes. Instead of DNA or RNA strands, genetic algorithms usually process strings of symbols of finite length; these symbols encode the parameters to be optimized.

Hazard: The potential source of harm.

Measurement Uncertainty: A parameter, associated with the result of a measurement, that characterizes the range of the values that could reasonably be attributed to the measurand.

Negative Predictive Value: The fraction of the population with a negative diagnostic test that are nondiseased.

Network: The graph representation of a system. The nodes or vertices of the graph represent the components of the system and the edges represent their relations.

Neural Networks: Artificial neural networks are adaptive computational algorithms, for statistical data modeling and classification of arbitrary precision, inspired by the brain structure and information processing.

Numerical Methods: Approximate calculation methods.

Optimization: The selection of a better or best alternative among a number of possible states or affairs. Usually the maximization or minimization of a function that depends on the objective of the system, the objective function.

Positive Predictive Value: The fraction of the population with a positive diagnostic test that are diseased.

Prevalence: The proportion of a population who have a particular disease or attribute in a given time period.

Probability Density Function: A measure of the probability of a random variable.

Quality Control: The statistical methods that are used for understanding, monitoring, and improving a process or product.

Receiver Operating Characteristic (ROC) Plots (Curves): Plots of the fraction of the diseased population with a positive diagnostic test versus the fraction of the nondiseased population with a positive diagnostic test.

Reliability: The probability that an item will perform a required function, under stated conditions, for a stated period of time.

Residual Risk: The risk remaining after the control measures have been taken.

Risk: Combination of the probability of occurrence of harm and the severity of that harm due to a hazard.

Risk Management: The practice of analyzing, evaluating, controlling and monitoring risk.

Sensitivity: The fraction of the diseased population with a positive diagnostic test.

Simulation: The numerical evaluation of the model of a system, to estimate the true characteristics of the system.

Specificity: The fraction of the nondiseased population with a negative diagnostic test.

Symbolic Computation: The mathematical transformation of symbolic expressions, using computer algorithms.

Uncertainty: An expression of imperfect or deficient information. When quantifiable it can be represented with probability.