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Data Science: Measuring Uncertainties
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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 has emerged as a multidisciplinary field to support data-driven activities, integrating and developing ideas, methods, and processes to extract information from data. This includes methods built from different knowledge areas: Statistics, Computer Science, Mathematics, Physics, Information Science, and Engineering. This mixture of areas has given rise to what we call Data Science. New solutions to the new problems are reproducing rapidly to generate large volumes of data. Current and future challenges require greater care in creating new solutions that satisfy the rationality for each type of problem. Labels such as Big Data, Data Science, Machine Learning, Statistical Learning, and Artificial Intelligence are demanding more sophistication in the foundations and how they are being applied. This point highlights the importance of building the foundations of Data Science. This book is dedicated to solutions and discussions of measuring uncertainties in data analysis problems.
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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 & 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 & interdisciplinary subjects
- relative entropy
- Rényi entropy
- Research & information: general
- robust singular spectrum analysis
- 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
Links
DOI: 10.3390/books978-3-0365-0793-4Editions