Data Science: Measuring Uncertainties
Material type: ArticleLanguage: English Publication details: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021Description: 1 electronic resource (256 p.)ISBN:- 9783036507927
- 9783036507934
- Анализ данных
- 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
- robust singular spectrum analysis
- time series forecasting
- uncertain reasoning
- semantic information
- medical test
- 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
Item type | Current library | Collection | Shelving location | Call number | Status | Notes | Date due | Barcode |
---|---|---|---|---|---|---|---|---|
Electronic edition | Bucheon University Library | Computers | MDPI books | 004.04 D24 | Not for loan | Смотреть (pdf) | 1010569 |
Open Access star Unrestricted online access
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.
Creative Commons https://creativecommons.org/licenses/by/4.0/ cc
English
There are no comments on this title.