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

We present an iterative Markov chainMonte Carlo algorithm forcomputingreference priors and minimax risk forgeneral parametric families.Ourapproach uses MCMC techniques based onthe Blahut-Arimoto algorithmforcomputing channel capacity ininformation theory. We give astatisticalanalysis of the algorithm,bounding the number of samples requiredfor thestochastic algorithm to closelyapproximate the deterministic algorithmin eachiteration. Simulations arepresented for several examples fromexponentialfamilies. Although we focuson applications to reference priors andminimax risk,the methods and analysiswe develop are applicable to a muchbroader class ofoptimization problemsand iterative algorithms.

Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk