Add to ORKGIn many practical applications, we are interested in the values of the quantities y1, ..., ym which are difficult (or even impossible) to measure directly. A natural idea to estimate these values is to find easier-to-measure related quantities x1, ..., xn and to use the known relation to estimate the desired values yi. Measurements come with uncertainty, and often, the only thing we know about the actual value of each auxiliary quantity xi is that it belongs to the interval [Xi − Δi, Xi + Δi], where Xi is the measurement result, and Δi is the upper bound on the absolute value of the measurement error Δ xi = Xi − xi. In such situations, instead of a single value of a tuple y = (y1, ..., ym), we have a range of possible values. In this paper,...
Why indirect measurements? In many real-life situations, we are interested in the value of a physica...
In statistical analysis, we usually use the observed sample values x1,..., xn to compute the values ...
In many real-life situations, we do not know the probability distribution of measurement errors but ...
In many real-life situations, we are interested in the physical quantities that are difficult or eve...
In many practical situations, the only information that we have about measurement errors is the uppe...
In many real-life situations, we are interested in the value of a physical quantity y that is diffic...
In many practical situations, we only know the upper bound D on the (absolute value of the) measurem...
In many practical situations, the quantity of interest is difficult to measure directly. In such sit...
In many real-life situations, we are interested in the physical quantities that are difficult or ev...
In statistical analysis, we usually use the observed sample values x1, ..., xn to compute the values...
Often, we are interested in a quantity that is difficult or impossible to measure directly, e.g., to...
In many real-life situations, we do not know the probability distribu-tion of measurement errors but...
Interval computations estimate the uncertainty of the result of data processing in situations in whi...
In many practical problems, we need to process measurement results. For example, we need such data p...
In many real-life data processing situations, we only know the values of the inputs with interval un...
Why indirect measurements? In many real-life situations, we are interested in the value of a physica...
In statistical analysis, we usually use the observed sample values x1,..., xn to compute the values ...
In many real-life situations, we do not know the probability distribution of measurement errors but ...
In many real-life situations, we are interested in the physical quantities that are difficult or eve...
In many practical situations, the only information that we have about measurement errors is the uppe...
In many real-life situations, we are interested in the value of a physical quantity y that is diffic...
In many practical situations, we only know the upper bound D on the (absolute value of the) measurem...
In many practical situations, the quantity of interest is difficult to measure directly. In such sit...
In many real-life situations, we are interested in the physical quantities that are difficult or ev...
In statistical analysis, we usually use the observed sample values x1, ..., xn to compute the values...
Often, we are interested in a quantity that is difficult or impossible to measure directly, e.g., to...
In many real-life situations, we do not know the probability distribu-tion of measurement errors but...
Interval computations estimate the uncertainty of the result of data processing in situations in whi...
In many practical problems, we need to process measurement results. For example, we need such data p...
In many real-life data processing situations, we only know the values of the inputs with interval un...
Why indirect measurements? In many real-life situations, we are interested in the value of a physica...
In statistical analysis, we usually use the observed sample values x1,..., xn to compute the values ...
In many real-life situations, we do not know the probability distribution of measurement errors but ...