دانلود کتاب Computational Statistics in Data Science

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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