[permalink] [id link]
* Uncertainty models, uncertainty quantification, and uncertainty processing in engineering
from
Wikipedia
Some Related Sentences
Uncertainty and models
Uncertainty and errors within regional models are introduced by the global model used for the boundary conditions of the edge of the regional model, as well as errors attributable to the regional model itself.
Uncertainty and uncertainty
* Uncertainty in a physical variable as seen in for instance the uncertainty principle
The Cone of Uncertainty explains some of this as the planning made on the initial phase of the project suffers from a high degree of uncertainty.
The World Wide Web Consortium ( W3C ) Incubator Group for Uncertainty Reasoning for the World Wide Web ( URW3-XG ) final report lumps these problems together under the single heading of " uncertainty ".
In his seminal work Risk, Uncertainty, and Profit University of Chicago economist Frank Knight ( 1921 ) established the important distinction between risk and uncertainty:
The most commonly used procedure for calculating measurement uncertainty is described in the Guide to the Expression of Uncertainty in Measurement ( often referred to as " the GUM ") published by ISO.
* Uncertainty avoidance is the coping with uncertainty about the future.
* Guide to the Expression of Uncertainty in Measurement ( ISO ), see Measurement uncertainty
His most influential work was Risk, Uncertainty and Profit ( 1921 ) from which was coined the term Knightian uncertainty.
Uncertainty and Sensitivity Analysis offer valid tools for characterizing the uncertainty associated with a model.
Uncertainty analysis ( UA ) quantifies the uncertainty in the outcome of a model.
Uncertainty arising from different sources — errors in the data, parameter estimation procedure, alternative model structures — are propagated through the model for uncertainty analysis and their relative importance is quantified via sensitivity analysis.
:* Uncertainty node ( corresponding to each uncertainty to be modeled ) is drawn as an oval.
Knightian uncertainty is named after University of Chicago economist Frank Knight ( 1885 – 1972 ), who distinguished risk and uncertainty in his work Risk, Uncertainty, and Profit:
Uncertainty Reduction Theory discusses the processes through which individuals go to reduce uncertainty about one another when placed in an unknown or unfamiliar environment ( Berger & Calabrese, 100 ).
Expectation Violations Theory has its roots in Uncertainty Reduction research, which attempts to predict and explain how communication is used to reduce the uncertainty among people involved in conversations with one another the first time they meet.
Uncertainty reduction theory focuses on when and why individuals use communication to reduce the uncertainty they have about others.
Kellerman and Reynolds ( 1990 ) pointed out that sometimes there are high level of uncertainty in interaction that no one wants to reduce ( Miller, 180 – 183 ). As a result of the critique, researchers formed the Uncertainty Management theory.
Uncertainty and quantification
* Uncertainty quantification
* Uncertainty quantification
Uncertainty and engineering
Uncertainty is a term used in subtly different ways in a number of fields, including physics, philosophy, statistics, economics, finance, insurance, psychology, sociology, engineering, and information science.
< li > Uncertainty or error is used in science and engineering notation.
models and uncertainty
It centres on decision making under uncertainty in the context of the financial markets, and the resultant economic and financial models.
In 2007 he collaborated on a study that found tropospheric temperature trends of " Climate of the 20th Century " models differed from satellite observations by twice the model mean uncertainty.
models for reasoning about uncertainty.
* Rational agent, entity which has clear preferences, models uncertainty via expected values, and always chooses to perform the action that results in the optimal outcome for itself from among all feasible actions
* Research results regarding uncertainty models, uncertainty quantification, and uncertainty processing
Modeling expectations is crucial in all models which study how a large number of individuals, firms and organizations make choices under uncertainty.
Some MPOs do some additional submodeling on things like automobile ownership, time of travel, location of land development, location and firms and location of households to help fill in these holes, but regardless what is created are models, and models always include some level of uncertainty.
Since macroeconomic behavior is derived from the interaction of the decisions of all these players, acting over time, in the face of uncertainty about future conditions, these models are classified as dynamic stochastic general equilibrium ( DSGE ) models.
* bayesian analysis and advanced probability models, fuzziness and uncertainty in archaeological data
Expected utility and discounted utility models began to gain acceptance, generating testable hypotheses about decision making given uncertainty and intertemporal consumption respectively.
For options with several sources of uncertainty ( e. g., real options ) and for options with complicated features ( e. g., Asian options ), binomial methods are less practical due to several difficulties, and Monte Carlo option models are commonly used instead.
Under both models, R & D differs from the vast majority of a company's activities which are intended to yield nearly immediate profit or immediate improvements in operations and involve little uncertainty as to the return on investment ( ROI ).
Yeats as models and these writers struck a chord with a readership who were uncomfortable with the experimentation and uncertainty preferred by the modernists.
IPCC graphic of uncertainty ranges with various models over time.
In economics, game theory, decision theory, and artificial intelligence, a rational agent is an agent which has clear preferences, models uncertainty via expected values, and always chooses to perform the action that results in the optimal outcome for itself from among all feasible actions.
But some models may provide useful guidance for the design engineer if adequately calibrated and verified for local conditions and if the design accounts for the uncertainty.
He was intrigued by paradoxes, and with the related concepts of uncertainty, undecidability / unprovability, and incompleteness ; he sought models of cognition that could embrace these elements, rather than simply explain them away.
Micro-level models need to account in the uncertainty management the assumptions and simplifications, which may pose significant limitations of that approach.
Finally, randomness and noise in the process, measurements, and prediction models are unavoidable and hence prognostics inevitably involves uncertainty in its estimates.
0.230 seconds.