Data Science: Measuring Uncertainties (MDPI - Multidisciplinary Digital Publishing Institute, 2021)
Keywords: algebraic Riccati equation; artificial intelligence; attribute weights; bank profile shape; Bayesian forecasting; Bayesian hierarchical modeling; Bayesian inference; Bayesian nonparametrics; big data; binary probit regression; channel; cloud model; clustering; cointegration; confirmation measure; continuous Kalman filter; cross-entropy; data science; decoy distributions; density estimation; discrete Kalman filter; distribution free; earthquake shelters; EM algorithm; entropy; fuzzy time series; gene expression programming (GEP); genetic programming; grey correlation analysis; Heikin–Ashi candlestick; hypothesis testing; inductive logic; integrated approach; intrinsic prior; intuitionistic fuzzy cross-entropy; Markov random fields; mathematical entropy model; Mathematics and Science; maximum entropy method; mean-field approximation; medical test; mixture model; model-based clustering; multi-attribute emergency decision-making; multilayer networks; mutual investment funds; n/a; non-parametric statistical test; nonlinear differential Riccati equation; objective Bayesian inference; outlier detection; overfitting detection; Pitman–Yor process; prior sensitivity; probabilistic graphical models; raven paradox; Reference, Information and Interdisciplinary subjects; relative entropy; Research and information: general; robust singular spectrum analysis; Rényi entropy; scaled quantile residual; scoring function; semantic information; singular spectrum analysis; size invariance; stock trend; time series; time series forecasting; time series of counts; uncertain reasoning; unit root; variational inference; water resources