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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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Bibliographic Details
Main Author: De Bragança Pereira, Carlos Alberto
Other Authors: Polpo, Adriano, Rodrigues, Agatha
Format: eBook
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
Subjects:
Unit Root
Pitman-yor Process
Integrated Approach
Model-based Clustering
Mixture Model
Multi-attribute Emergency Decision-making
Data Science
Heikin-ashi Candlestick
Bank Profile Shape
N/a
Bayesian Forecasting
Raven Paradox
Nonlinear Differential Riccati Equation
Time Series Of Counts
Overfitting Detection
Big Data
Multilayer Networks
Intrinsic Prior
Robust Singular Spectrum Analysis
Time Series
Algebraic Riccati Equation
Variational Inference
Gene Expression Programming (gep)
Mathematical Entropy Model
Mean-field Approximation
Relative Entropy
Em Algorithm
Rényi Entropy
Semantic Information
Stock Trend
Fuzzy Time Series
Uncertain Reasoning
Intuitionistic Fuzzy Cross-entropy
Scoring Function
Decoy Distributions
Probabilistic Graphical Models
Clustering
Non-parametric Statistical Test
Channel
Earthquake Shelters
Confirmation Measure
Continuous Kalman Filter
Mathematics And Science / Bicssc
Objective Bayesian Inference
Outlier Detection
Mutual Investment Funds
Cointegration
Grey Correlation Analysis
Bayesian Hierarchical Modeling
Inductive Logic
Cross-entropy
Singular Spectrum Analysis
Maximum Entropy Method
Entropy
Bayesian Nonparametrics
Artificial Intelligence
Discrete Kalman Filter
Markov Random Fields
Distribution Free
Water Resources
Size Invariance
Scaled Quantile Residual
Binary Probit Regression
Medical Test
Genetic Programming
Density Estimation
Prior Sensitivity
Time Series Forecasting
Cloud Model
Hypothesis Testing
Attribute Weights
Research And Information: General / Bicssc
Bayesian Inference
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary: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.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (256 p.)
ISBN:books978-3-0365-0793-4
9783036507927
9783036507934

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