• Corpus ID: 244799549

On the stationary distribution of the noisy voter model

@inproceedings{Pymar2021OnTS,
  title={On the stationary distribution of the noisy voter model},
  author={Richard Pymar and Nicol{\'a}s Rivera},
  year={2021}
}
Given a transition matrix P indexed by a finite set V of vertices, the voter model is a discrete-time Markov chain in {0, 1}V where at each time-step a randomly chosen vertex x imitates the opinion of vertex y with probability P (x, y). The noisy voter model is a variation of the voter model in which vertices may change their opinions by the action of an external noise. The strength of this noise is measured by an extra parameter p ∈ [0, 1]. The noisy voter model is a Markov chain with state… 

Global information from local observations of the noisy voter model on a graph

We observe the outcome of the discrete time noisy voter model at a single vertex of a graph. We show that certain pairs of graphs can be distinguished by the frequency of repetitions in the

References

SHOWING 1-10 OF 15 REFERENCES

Cutoff for the Noisy Voter Model

Given a continuous time Markov Chain $\{q(x,y)\}$ on a finite set $S$, the associated noisy voter model is the continuous time Markov chain on $\{0,1\}^S$, which evolves in the following way: (1) for

The noisy voter model

On the coalescence time of reversible random walks

Consider a system of coalescing random walks where each individual performs random walk over a finite graph G, or (more generally) evolves according to some reversible Markov chain generator Q. Let C

On coupling constructions and rates in the CLT for dependent summands with applications to the antivoter model and weighted $U$-statistics

This paper deals with rates of convergence in the CLT for certain types of dependency. The main idea is to combine a modification of a theorem of Stein, requiring a coupling construction, with a

Markov chains and mixing times

For our purposes, a Markov chain is a (finite or countable) collection of states S and transition probabilities pij, where i, j ∈ S. We write P = [pij] for the matrix of transition probabilities.

An Elementary Proof of the Hitting Time Theorem

In this note, we give an elementary proof of the random walk hitting time theorem, which states that, for a left-continuous random walk on Z starting at a nonnegative integer k, the conditional

Mixing of the noisy voter model

We prove that the noisy voter model mixes extremely fast - in time of O(log(n)) on any graph with n vertices - for arbitrarily small values of the "noise parameter". We then explain why, as a result,

Binary Opinion Dynamics with Stubborn Agents

TLDR
It is shown that the presence of stubborn agents with opposing opinions precludes convergence to consensus; instead, opinions converge in distribution with disagreement and fluctuations.

Interacting particle systems

2 The asymmetric simple exclusion process 20 2.1 Stationary measures and conserved quantities . . . . . . . . . . . . . . . . . . . 20 2.2 Currents and conservation laws . . . . . . . . . . . . . . .