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

arXiv: https://arxiv.org/abs/1301.2286 · HTML: https://mlsanity.com/paper/1301.2286/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2013.

## Built on by (most cited first, 1 of 1)

- [Far from Asymptopia](https://mlsanity.com/paper/2205.03343/) (2022)

## Abstract (arXiv)

We present an iterative Markov chainMonte Carlo algorithm for computingreference priors and minimax risk forgeneral parametric families. Ourapproach uses MCMC techniques based onthe Blahut-Arimoto algorithm forcomputing channel capacity ininformation theory. We give astatistical analysis of the algorithm,bounding the number of samples requiredfor the stochastic algorithm to closelyapproximate the deterministic algorithmin each iteration. Simulations arepresented for several examples fromexponential families. Although we focuson applications to reference priors andminimax risk, the methods and analysiswe develop are applicable to a muchbroader class of optimization problemsand iterative algorithms.