English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The goal of this paper is to provide a way for statisticians to answer thequestion posed in the title of this article using any Bayesian hierarchicalmodel of their choosing and without imposing additional restrictive modelassumptions. We are motivated by the fact that the rise of ``big data'' hascreated difficulties for statisticians to directly apply their methods to bigdatasets. We introduce a ``data subset model'' to the popular ``data model,process model, and parameter model'' framework used to summarize Bayesianhierarchical models. The hyperparameters of the data subset model are specifiedconstructively in that they are chosen such that the implied size of the subsetsatisfies pre-defined computational constraints. Thus, these hyperparameterseffectively calibrates the statistical model to the computer itself to obtainpredictions/estimations in a pre-specified amount of time. Several propertiesof the data subset model are provided including: propriety, partialsufficiency, and semi-parametric properties. Furthermore, we show that subsetsof normally distributed data are asymptotically partially sufficient underreasonable constraints. Results from a simulated dataset will be presentedacross different computers, to show the effect of the computer on thestatistical analysis. Additionally, we provide a joint spatial analysis of twodifferent environmental datasets.

What is the best predictor that you can compute in five minutes using a given Bayesian hierarchical model?