## Computing and graphics in statisticsComputing and Graphics in Statistics presents issues that arise in the development of integrated statistical software systems which have led to the adaptation of ideas from computer science, particularly programming environments, programming paradigms, and artificial intelligence. Examples are given that distinguish statistics from many physical sciences such as genuine high-dimensional objects - multivariate data or functions of many variables. Demonstrates automatic methods for finding reasonable domains and ranges for plotting univariate functions. Deals with computer intensive methodology including the bootstrap method, Bayesian inferrence and its associated integration problems. |

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### Contents

Foreword | vi |

Integrating a robust option into a multiple | 41 |

Importance sampling for Bayesian estimation | 63 |

Copyright | |

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a-shells Abstract algorithm array asymptotic axis batch binning bisquare bivariate boxplot cells cognitive mode cognostics components computation covariance cubes data analysis data space data structures dataflow diagram dataflow mode defined density plots dimensional dimensions dynamic environment estimate exploratory exploratory data analysis fertility-vs-education Figure function gauss-uv Genstat graph graphical display guided tour hexagon high-dimensional highlights histogram icons implemented interactive interface labels large data sets layout least squares linear Lisp load balancing Mathematica matrix measurement object memoized methods MIDAS multivariate data nodes object-oriented object-oriented programming observations optimization parallel coordinate Places Rated Analysis point symbol Point-Cloud posterior distribution problem procedure projection projection pursuit random variable reexpression region regression situation represent residuals returns robust sampling distribution scatterplot screen setq solution set sqrt statistical graphics subviews summary truncated octahedron Tukey univariate situation values variance vector viewport visual space weights