Two features of Processing Random Data differentiate it from other
similar books: the focus on computing the reproducibility error for
statistical measurements, and its comprehensive coverage of Maximum
Likelihood parameter estimation techniques. The book is useful for
dealing with situations where there is a model relating to the
input and output of a process, but with a random component, which
could be noise in the system or the process itself could be random,
like turbulence. Parameter estimation techniques are shown for many
different types of statistical models, including joint Gaussian.
The Cramer?Rao bounds are described as useful estimates of
reproducibility errors. Finally, using an example with a random
sampling of turbulent flows that can occur when using laser
anemometry the book also explains the use of conditional
probabilities.
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