Computational Statistics in Data Science. Группа авторов

      Table of Contents

      1  Cover

      2  Title Page

      3  Copyright

      4  List of Contributors

      5  Preface Reference

      6  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 3 Model‐Specific Advances 4 Core Challenges 4 and 5 5 Rise of Data Science Acknowledgments Notes References 2 Statistical Software 1 User Development Environments 2 Popular Statistical Software 3 Noteworthy Statistical Software and Related Tools 4 Promising and Emerging Statistical Software 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 3 Feedforward Neural Networks 4 Convolutional Neural Networks 5 Autoencoders 6 Recurrent Neural Networks 7 Conclusion References 4 Streaming Data and Data Streams 1 Introduction 2 Data Stream Computing 3 Issues in Data Stream Mining 4 Streaming Data Tools and Technologies 5 Streaming Data Pre‐Processing: Concept and Implementation 6 Streaming Data Algorithms 7 Strategies for Processing Data Streams 8 Best Practices for Managing Data Streams 9 Conclusion and the Way Forward References

      7  Part II: Simulation‐Based Methods 5 Monte Carlo Simulation: Are We There Yet? 1 Introduction 2 Estimation 3 Sampling Distribution 4 Estimating

5 Stopping Rules 6 Workflow 7 Examples References 6 Sequential Monte Carlo: Particle Filters and Beyond 1 Introduction 2 Sequential Importance Sampling and Resampling 3 SMC in Statistical Contexts 4 Selected Recent Developments Acknowledgments Note References 7 Markov Chain Monte Carlo Methods, A Survey with Some Frequent Misunderstandings 1 Introduction 2 Monte Carlo Methods 3 Markov Chain Monte Carlo Methods 4 Approximate Bayesian Computation 5 Further Reading Abbreviations and Acronyms Notes References Note