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1004 results in Computational statistics, machine learning and information science

Page 9 of 51

Probabilistic Numerics
  • Computation as Machine Learning
  • Philipp Hennig, Michael A. Osborne, Hans P. Kersting
  • Published online:
    01 June 2022
    Print publication:
    30 June 2022
  • Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.

A First Course in Statistical Programming with R
  • 3rd edition
  • W. John Braun, Duncan J. Murdoch
  • Published online:
    08 November 2021
    Print publication:
    20 May 2021
  • This third edition of Braun and Murdoch's bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the Box–Muller and Metropolis–Hastings algorithms. New examples and exercises have been added throughout. This is the only introduction you'll need to start programming in R, the computing standard for analyzing data. This book comes with real R code that teaches the standards of the language. Unlike other introductory books on the R system, this book emphasizes portable programming skills that apply to most computing languages and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from www.statprogr.science. Worked examples - from real applications - hundreds of exercises, and downloadable code, datasets, and solutions make a complete package for anyone working in or learning practical data science.

  • 3 - Programming statistical graphics

  • W. John Braun, Duncan J. Murdoch, University of Western Ontario
  • Book:
    A First Course in Statistical Programming with R
    Published online:
    08 November 2021
    Print publication:
    20 May 2021, pp 53-92

  • Summary

    Users of statistical computing need to produce graphs of their data and the results of their computations. In this chapter we start with a general overview of how this is done in R, and learn how to draw some basic plots. We then discuss some of the issues involved in choosing a style of plot to draw: it is not always an easy choice, and there are plenty of bad examples in the world to lead us astray. We will talk briefly about customizing graphs, and then move on to alternate packages for producing graphics.

Page 9 of 51