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Computational Statistics in Data Science
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Страница 1
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Computational Statistics in Data Science
Страница 8
List of Contributors
Страница 10
Страница 11
1 Computational Statistics and Data Science in the Twenty‐First Century
1 Introduction
2 Core Challenges 1–3
2.1 Big
N
2.2 Big
P
2.3 Big
M
3 Model‐Specific Advances
3.1 Bayesian Sparse Regression in the Age of Big
N
and Big
P
3.1.1 Continuous shrinkage: alleviating big
M
3.1.2 Conjugate gradient sampler for structured high‐dimensional Gaussians
3.2 Phylogenetic Reconstruction
4 Core Challenges 4 and 5
4.1 Fast, Flexible, and Friendly Statistical Algo‐Ware
4.2 Hardware‐Optimized Inference
5 Rise of Data Science
Acknowledgments
Notes
References
2 Statistical Software
1 User Development Environments
1.1 Extensible Text Editors: Emacs and Vim
1.2 Jupyter Notebooks
1.3 RStudio and Rmarkdown
2 Popular Statistical Software
2.1 R
2.1.1 Why use R over Python or Minitab?
2.1.2 Where can users find R support?
2.1.3 How easy is R to develop?
2.1.4 What is the downside of R?
2.1.5 Summary of R
2.2 Python
2.3 SAS®
2.4 SPSS®
3 Noteworthy Statistical Software and Related Tools
3.1 BUGS/JAGS
3.2 C++
3.3 Microsoft Excel/Spreadsheets
3.4 Git
3.5 Java
3.6 JavaScript, Typescript
3.7 Maple
3.8 MATLAB, GNU Octave
3.9 Minitab®
3.10 Workload Managers: SLURM/LSF
3.11 SQL
3.12 Stata®
3.13 Tableau®
4 Promising and Emerging Statistical Software
4.1 Edward, Pyro, NumPyro, and PyMC3
4.2 Julia
4.3 NIMBLE
4.4 Scala
4.5 Stan
5 The Future of Statistical Computing
6 Concluding Remarks
Acknowledgments
References
Further Reading
3 An Introduction to Deep Learning Methods
1 Introduction
2 Machine Learning: An Overview 2.1 Introduction
2.2 Supervised Learning
2.3 Gradient Descent
3 Feedforward Neural Networks 3.1 Introduction
3.2 Model Description
3.3 Training an MLP
4 Convolutional Neural Networks 4.1 Introduction
4.2 Convolutional Layer
4.3 LeNet‐5
5 Autoencoders 5.1 Introduction
5.2 Objective Function
5.3 Variational Autoencoder
6 Recurrent Neural Networks 6.1 Introduction
6.2 Architecture
6.3 Long Short‐Term Memory Networks
7 Conclusion
References
4 Streaming Data and Data Streams
1 Introduction
2 Data Stream Computing
3 Issues in Data Stream Mining
3.1 Scalability
3.2 Integration
3.3 Fault‐Tolerance
3.4 Timeliness
3.5 Consistency
3.6 Heterogeneity and Incompleteness
3.7 Load Balancing
3.8 High Throughput
3.9 Privacy
3.10 Accuracy
4 Streaming Data Tools and Technologies
5 Streaming Data Pre‐Processing: Concept and Implementation
6 Streaming Data Algorithms
6.1 Unsupervised Learning
6.2 Semi‐Supervised Learning
6.3 Supervised Learning
6.4 Ontology‐Based Methods
7 Strategies for Processing Data Streams
8 Best Practices for Managing Data Streams
9 Conclusion and the Way Forward
References
Страница 114
5 Monte Carlo Simulation: Are We There Yet?
1 Introduction
2 Estimation
2.1 Expectations
2.2 Quantiles
2.3 Other Estimators
3 Sampling Distribution
3.1 Means
Theorem 1.
3.2 Quantiles
Theorem 2.
3.3 Other Estimators
3.4 Confidence Regions for Means
4 Estimating
5 Stopping Rules
5.1 IID Monte Carlo
5.2 MCMC
6 Workflow
7 Examples 7.1 Action Figure Collector Problem
7.2 Estimating Risk for Empirical Bayes
7.3 Bayesian Nonlinear Regression
Note
References
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