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me series methods, statistical machine learning, digital signal processing and optimization methods; Mathematical language environments: It, S-PLUS; Maple, OOD and parallel processing in C++, Java; RDB and KM design, development, FIX, International equity markets; EXPERIENCE October 2012 — December 2
TIVE AND ECONOMETRIC METHODS • Multivariate statistics, Principal Component Analysis (PCA), Kernel PCA and Independent Component Analysis(ICA) • Stochastic Control and Stochastic Calculus. • DSP, Adaptive filtering, Kalman filters, Hidden Markov Model • Kernel Methods, Support Vector Machines • Cointegrati
ime series methods, statistical machine learning, digital signal processing and optimization methods; Mathematical language environments: R, S-PLUS; Maple, OOD and parallel processing in C++, Java; RDB and KDB design, development, FIX, International equity markets; EXPERIENCE October 2012 — December
TIVE AND ECONOMETRIC METHODS • Multivariate statistics, Principal Component Analysis (PCA), Kernel PCA and Independent Component Analysis(ICA) • Stochastic Control and Stochastic Calculus. • DSP, Adaptive filtering, Kalman filters, Hidden Igarkov Model • Kernel Methods, Support Vector Machines • Cointegrat
ime series methods, statistical machine learning, digital signal processing and optimization methods; Mathematical language environments: R, S-PLUS; Maple, OOD and parallel processing in C++, Java; RDB and KDB design, development, FIX, International equity markets; EXPERIENCE October 2012 — December
TIVE AND ECONOMETRIC METHODS • Multivariate statistics, Principal Component Analysis (PCA), Kernel PCA and Independent Component Analysis(ICA) • Stochastic Control and Stochastic Calculus. • DSP, Adaptive filtering, Kalman filters, Hidden Markov Model • Kernel Methods, Support Vector Machines • Cointegrati
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