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AP Statistics: Experimental Design (1)

More by Elizabeth G. Read more about accessing full-text Buy article. Abstract Article info and citation First page References Abstract Big Datasets are endemic, but are often notoriously difficult to analyse because of their size, heterogeneity and quality.

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Article information Source Statist. Export citation. Export Cancel. References Amzal, B.

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Bayesian-optimal design via interacting particle systems. Austin, P.

An introduction to propensity score methods for reducing the effects of confounding in observational studies. Bardenet, R.

Quality Engineering: Control, Design and Optimization

Towards scaling up Markov chain Monte Carlo: An adaptive subsampling approach. On Markov chain Monte Carlo methods for tall data. Available at arXiv You have access to this content. You have partial access to this content.

You do not have access to this content. More like this On statistics, computation and scalability Jordan, Michael I. With its particular consideration of the fact that all aspects of everyday life are designed, design research reveals an astounding ability to discuss current economic, social, environmental and media-related processes.

This kind of design research, when intelligently implemented, also provides a fundamental critique of design as well as a variety of possible perspectives to better understand the present and prospects for the future. Young and very dedicated researchers come together to present their latest fundamental and contemporary research in design.

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On the basis of their respective abstracts, ten design researchers from Japan, Hong Kong, Melbourne, New York, Canada, London and other places have been invited to present their theses and analyses in the form of a 30 minute presentation, followed by 30 minutes of discussion with the audience. This is always a very exciting, stimulating and enlightening experience.