Edward R. Dougherty | |
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Born | New Jersey, U.S. | September 10, 1945
Alma mater | Rutgers University, (Ph.D. 1974) Fairleigh Dickinson University, (B.S. and MS, 1967, 1969) |
Known for | Nonlinear filtering and image processing Fuzzy systems Probabilistic boolean network |
Scientific career | |
Fields | Mathematics, electrical engineering, bioinformatics |
Institutions | Texas A&M University, since 1996, Rochester Institute of Technology,1988-1996, Fairleigh Dickinson University,1972-1988 |
Doctoral advisor | Joanne Elliott |
Edward R. Dougherty is an American mathematician, electrical engineer, Robert M. Kennedy '26 Chair, and Distinguished Professor [1] of Electrical Engineering at Texas A&M University. [2] He is also the Scientific Director of the Center for Bioinformatics and Genomic Systems Engineering. [3] Dougherty is a specialist in nonlinear image processing, small-sample classification problems, and modeling gene regulatory networks. He is the Fellow of IEEE and SPIE.
Dougherty is the author of 16 books, whose topics range from basic probability books to advanced computational biology and genomic systems engineering. He proposed the Probabilistic Boolean Network (PBN) model for gene regulatory networks. [4] PBNs have been extensively used for intervention and classification in genomic problems. [5] [6] He has also introduced the notion of Bolstered Error Estimation [7] and Coefficient of Determination for Nonlinear Signal Processing. [8]
Signal processing is an electrical engineering subfield that focuses on analysing, modifying, and synthesizing signals such as sound, images, and scientific measurements. Signal processing techniques can be used to improve transmission, storage efficiency and subjective quality and to also emphasize or detect components of interest in a measured signal.
A Dynamic Bayesian Network (DBN) is a Bayesian network (BN) which relates variables to each other over adjacent time steps. This is often called a Two-Timeslice BN (2TBN) because it says that at any point in time T, the value of a variable can be calculated from the internal regressors and the immediate prior value. DBNs were developed by Paul Dagum in the early 1990s at Stanford University's Section on Medical Informatics. Dagum developed DBNs to unify and extend traditional linear state-space models such as Kalman filters, linear and normal forecasting models such as ARMA and simple dependency models such as hidden Markov models into a general probabilistic representation and inference mechanism for arbitrary nonlinear and non-normal time-dependent domains.
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A Boolean network consists of a discrete set of boolean variables each of which has a Boolean function assigned to it which takes inputs from a subset of those variables and output that determines the state of the variable it is assigned to. This set of functions in effect determines a topology (connectivity) on the set of variables, which then become nodes in a network. Usually, the dynamics of the system is taken as a discrete time series where the state of the entire network at time t+1 is determined by evaluating each variable's function on the state of the network at time t. This may be done synchronously or asynchronously.
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