Data Science: Measuring Uncertainties

With the increase in data processing and storage capacity, a large amount of data is available. Data without analysis does not have much value. Thus, the demand for data analysis is increasing daily, and the consequence is the appearance of a large number of jobs and published articles. Data science...

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Бібліографічні деталі
Формат:	Online
Мова:	Англійська
Опубліковано:	MDPI - Multidisciplinary Digital Publishing Institute 2022
Предмети:	model-based clustering mixture model EM algorithm integrated approach density estimation distribution free non-parametric statistical test decoy distributions size invariance scaled quantile residual maximum entropy method scoring function outlier detection overfitting detection time series of counts Bayesian hierarchical modeling Bayesian nonparametrics Pitman–Yor process prior sensitivity clustering Bayesian forecasting singular spectrum analysis robust singular spectrum analysis time series forecasting mutual investment funds relative entropy cross-entropy uncertain reasoning inductive logic confirmation measure semantic information medical test raven paradox Markov random fields probabilistic graphical models multilayer networks objective Bayesian inference intrinsic prior variational inference binary probit regression mean-field approximation multi-attribute emergency decision-making intuitionistic fuzzy cross-entropy grey correlation analysis earthquake shelters attribute weights time series Bayesian inference hypothesis testing unit root cointegration Rényi entropy discrete Kalman filter continuous Kalman filter algebraic Riccati equation nonlinear differential Riccati equation cloud model fuzzy time series stock trend Heikin–Ashi candlestick water resources channel mathematical entropy model bank profile shape gene expression programming (GEP) entropy genetic programming artificial intelligence data science big data n/a thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science
Онлайн доступ:	ONIX_20220111_9783036507927_216
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Data Science: Measuring Uncertainties

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