# Sampling Biased Monotonic Surfaces using Exponential Metrics

## TL;DR

This paper analyzes a Markov chain for sampling monotonic surfaces with a bias towards higher surfaces, proving rapid mixing under certain conditions and extending results to variable bias scenarios.

## Contribution

It introduces a simplified proof of rapid mixing for biased monotonic surface sampling and extends analysis to cases with fluctuating bias.

## Key findings

- Rapid mixing in Z^2 for any uniform bias
- Rapid mixing in Z^d for bias λ > d when d > 2
- Exponential slow mixing possible with variable bias

## Abstract

Monotonic surfaces spanning finite regions of $Z^d$ arise in many contexts, including DNA-based self-assembly, card-shuffling and lozenge tilings. One method that has been used to uniformly generate these surfaces is a Markov chain that iteratively adds or removes a single cube below the surface during a step. We consider a biased version of the chain, where we are more likely to add a cube than to remove it, thereby favoring surfaces that are "higher" or have more cubes below it. We prove that the chain is rapidly mixing for any uniform bias in $Z^2$ and for bias $\lambda > d$ in $Z^d$ when $d>2$. In $Z^2$ we match the optimal mixing time achieved by Benjamini et al. in the context of biased card shuffling, but using much simpler arguments. The proofs use a geometric distance function and a variant of path coupling in order to handle distances that can be exponentially large. We also provide the first results in the case of fluctuating bias, where the bias can vary depending on the location of the tile. We show that the chain continues to be rapidly mixing if the biases are close to uniform, but that the chain can converge exponentially slowly in the general setting.