Computational Statistics in Data Science

Cover
Title Page
Copyright
Contents
List of Contributors
Preface
Part I Computational Statistics and Data Science
	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
		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
Part II Simulation‐Based Methods
	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
			3.2 Quantiles
			3.3 Other Estimators
			3.4 Confidence Regions for Means
		4 Estimating