.png){kind=icon}
.png){kind=icon}
Return
Comparing distributions by using dependent normalized random-measure mixtures
.png){kind=icon}
OA
AI
DOI：10.1111/rssb.12002.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A methodology for the simultaneous Bayesian non-parametric modelling of several distributions is developed. Our approach uses normalized random measures with independent increments and builds dependence through the superposition of shared processes. The properties of the prior are described and the modelling possibilities of this framework are explored in detail. Efficient slice sampling methods are developed for inference. Various posterior summaries are introduced which allow better understanding of the differences between distributions. The methods are illustrated on simulated data and examples from survival analysis and stochastic frontier analysis.
Keywords:
Bayesian non-parametrics
Dependent distributions
Dirichlet process
Normalized generalized gamma process
Slice sampling
Utility function
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
3.6
Papers:
1.5K
Citations:
3.2W
View more organizations