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William A Gardner (born Allen William Mclean, November 4, 1942) is a theoretically inclined electrical engineer who specializes in the advancement of the theory of statistical time-series analysis and statistical inference with emphasis on signal processing algorithm design and performance analysis. [1] He is also an entrepreneur, a professor ...
Estimation theory. Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data. An estimator attempts to approximate ...
Mendel has authored or co-authored 13 books. In the 1995 textbook Lessons in Estimation Theory for Signal Processing, Communications, and Control, he provided a one semester graduate course about the field of estimation theory and estimation algorithms. J.J. Shynk commented, "Graduate students and researchers in electrical engineering will find ...
Maximum likelihood estimation (MLE) is a standard statistical tool for finding parameter values (e.g. the unmixing matrix ) that provide the best fit of some data (e.g., the extracted signals ) to a given a model (e.g., the assumed joint probability density function (pdf) of source signals).
The approximation of a normal distribution with a Monte Carlo method. Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deterministic in principle.
Signal processing is an electrical engineering subfield that focuses on analyzing, modifying and synthesizing signals, such as sound, images, potential fields, seismic signals, altimetry processing, and scientific measurements. [1] Signal processing techniques are used to optimize transmissions, digital storage efficiency, correcting distorted ...
His research interests lie in the areas of stochastic analysis, statistical signal processing and information theory, and their applications in a number of fields including wireless networks, social networks, and smart grid. This research work has attracted over 10,000 citations. [10] He has published a book on Signal Detection and Estimation. [11]
In statistical signal processing, the goal of spectral density estimation (SDE) or simply spectral estimation is to estimate the spectral density (also known as the power spectral density) of a signal from a sequence of time samples of the signal. [1] Intuitively speaking, the spectral density characterizes the frequency content of the signal.