Universal joint-measurement uncertainty relation for error bars

The uncertainty relation for joint measurement of position and momentum

We prove an uncertainty relation, which imposes a bound on any joint measurement of position and momentum. It is of the form (∆P )(∆Q) ≥ C~, where the ‘uncertainties’ quantify the difference between the marginals of the joint measurement and the corresponding ideal observable. Applied to an approximate position measurement followed by a momentum measurement, the uncertainties become the precisi...

Bayesian uncertainty relation for a joint measurement of canonical variables

We present a joint-measurement uncertainty relation for a pair of mean square deviations of canonical variables averaged over Gaussian distributed quantum optical states. Our Bayesian formulation is free from the unbiasedness assumption, and enables us to quantify experimentally implemented joint-measurement devices by feeding a moderate set of coherent states. Our result also reproduces the mo...

Error - Bars for Belief Net

smartphone and universal goniometer for measurement of elbow joint motions: a comparative study

results the results of this study showed high reliability and validity of smartphone in regarding ug with icc > 0.95. the highest reliability for both methods was in elbow supination and the lowest was in the elbow flexion (0.84). objectives the study was designed to compare the smartphone inclinometer-based app and ug in evaluation of rom of elbow. materials and methods the maximum rom of elbo...

Quantifying the uncertainty of a belief net response: Bayesian error-bars for belief net inference

A Bayesian belief network models a joint distribution over variables using a DAG to represent variable dependencies and network parameters to represent the conditional probability of each variable given an assignment to its immediate parents. Existing algorithms assume each network parameter is fixed. From a Bayesian perspective, however, these network parameters can be random variables that re...