Reference priors for non-Normal two-sample problems

Prior data for non-normal priors.

Data augmentation priors facilitate contextual evaluation of prior distributions and the generation of Bayesian outputs from frequentist software. Previous papers have presented approximate Bayesian methods using 2x2 tables of 'prior data' to represent lognormal relative-risk priors in stratified and regression analyses. The present paper describes extensions that use the tables to represent ge...

Implications of Reference Priors for Prior Information and for Sample Size

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. American Statistical Association is collaborating with JST...

On Independent Reference Priors

Variational Reference Priors

Posterior distributions are useful for a broad range of tasks in machine learning ranging from model selection to reinforcement learning. Given that modern machine learning models can have millions of parameters, selecting an informative prior is typically infeasible, resulting in widespread use of priors that avoid strong assumptions. For example, recent work on deep generative models (Kingma ...

A Fuzzy Goal Programming Approach for Optimizing Non-normal Fuzzy Multiple Response Problems

In most manufacturing processes, each product may contain a variety of quality characteristics which are of the interest to be optimized simultaneously through determination of the optimum setting of controllable factors. Although, classic experimental design presents some solutions for this regard, in a fuzzy environment, and in cases where the response data follow non-normal distributions, th...