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Robust Bayes also uses a similar strategy to combine a class of probability models with a class of utility functions to infer a class of decisions, any of which might be the answer given the uncertainty about best probability model and utility function. In both cases, the result is said to be robust if it is approximately the same for each such ...
Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to ...
Audio engineers use dynamic range to describe the ratio of the amplitude of the loudest possible undistorted signal to the noise floor, say of a microphone or loudspeaker. [18] Dynamic range is therefore the signal-to-noise ratio (SNR) for the case where the signal is the loudest possible for the system. For example, if the ceiling of a device ...
The dynamic range of a microphone is the difference in SPL between the noise floor and the maximum SPL. If stated on its own, for example, "120 dB", it conveys significantly less information than having the self-noise and maximum SPL figures individually. Sensitivity indicates how well the microphone converts acoustic pressure to an output voltage.
The sensitivity of an electronic device, such as a communications system receiver, or detection device, such as a PIN diode, is the minimum magnitude of input signal required to produce a specified output signal having a specified signal-to-noise ratio, or other specified criteria. In general, it is the signal level required for a particular ...
Variance-based sensitivity analysis (often referred to as the Sobol’ method or Sobol’ indices, after Ilya M. Sobol’) is a form of global sensitivity analysis. [1] [2] Working within a probabilistic framework, it decomposes the variance of the output of the model or system into fractions which can be attributed to inputs or sets of inputs.
Cooking Performance: The best Dutch ovens excel at a wide range of cooking tasks, from braising meat to baking bread. To test each pot’s cooking abilities, we used it for three key tasks: high ...
The adjoint state method is a numerical method for efficiently computing the gradient of a function or operator in a numerical optimization problem. [1] It has applications in geophysics, seismic imaging, photonics and more recently in neural networks.