Computing and graphics in statistics

Front Cover
Computing 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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Integrating a robust option into a multiple
Importance sampling for Bayesian estimation

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