TY  - GEN
AU  - De Bragança Pereira,Carlos Alberto
AU  - Polpo,Adriano
AU  - Rodrigues,Agatha
AU  - De Bragança Pereira,Carlos Alberto
AU  - Polpo,Adriano
AU  - Rodrigues,Agatha
TI  - Data Science: Measuring Uncertainties
SN  - books978-3-0365-0793-4
PY  - 2021///
CY  - Basel, Switzerland
PB  - MDPI - Multidisciplinary Digital Publishing Institute
KW  - Research & information: general
KW  - bicssc
KW  - Mathematics & science
KW  - model-based clustering
KW  - mixture model
KW  - EM algorithm
KW  - integrated approach
KW  - density estimation
KW  - distribution free
KW  - non-parametric statistical test
KW  - decoy distributions
KW  - size invariance
KW  - scaled quantile residual
KW  - maximum entropy method
KW  - scoring function
KW  - outlier detection
KW  - overfitting detection
KW  - time series of counts
KW  - Bayesian hierarchical modeling
KW  - Bayesian nonparametrics
KW  - Pitman–Yor process
KW  - prior sensitivity
KW  - clustering
KW  - Bayesian forecasting
KW  - singular spectrum analysis
KW  - robust singular spectrum analysis
KW  - time series forecasting
KW  - mutual investment funds
KW  - relative entropy
KW  - cross-entropy
KW  - uncertain reasoning
KW  - inductive logic
KW  - confirmation measure
KW  - semantic information
KW  - medical test
KW  - raven paradox
KW  - Markov random fields
KW  - probabilistic graphical models
KW  - multilayer networks
KW  - objective Bayesian inference
KW  - intrinsic prior
KW  - variational inference
KW  - binary probit regression
KW  - mean-field approximation
KW  - multi-attribute emergency decision-making
KW  - intuitionistic fuzzy cross-entropy
KW  - grey correlation analysis
KW  - earthquake shelters
KW  - attribute weights
KW  - time series
KW  - Bayesian inference
KW  - hypothesis testing
KW  - unit root
KW  - cointegration
KW  - Rényi entropy
KW  - discrete Kalman filter
KW  - continuous Kalman filter
KW  - algebraic Riccati equation
KW  - nonlinear differential Riccati equation
KW  - cloud model
KW  - fuzzy time series
KW  - stock trend
KW  - Heikin–Ashi candlestick
KW  - water resources
KW  - channel
KW  - mathematical entropy model
KW  - bank profile shape
KW  - gene expression programming (GEP)
KW  - entropy
KW  - genetic programming
KW  - artificial intelligence
KW  - data science
KW  - big data
KW  - n/a
N1  - Open Access
N2  - 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
UR  - https://mdpi.com/books/pdfview/book/3916
UR  - https://directory.doabooks.org/handle/20.500.12854/76480
ER  -