Approximating the Spectral Gap of the Pólya-Gamma Gibbs Sampler

04/27/2021
by   Bryant Davis, et al.
0

The self-adjoint, positive Markov operator defined by the Pólya-Gamma Gibbs sampler (under a proper normal prior) is shown to be trace-class, which implies that all non-zero elements of its spectrum are eigenvalues. Consequently, the spectral gap is 1-λ_*, where λ_* ∈ [0,1) is the second largest eigenvalue. A method of constructing an asymptotically valid confidence interval for an upper bound on λ_* is developed by adapting the classical Monte Carlo technique of Qin et al. (2019) to the Pólya-Gamma Gibbs sampler. The results are illustrated using the German credit data. It is also shown that, in general, uniform ergodicity does not imply the trace-class property, nor does the trace-class property imply uniform ergodicity.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/16/2018

Trace class Markov chains for the Normal-Gamma Bayesian shrinkage model

High-dimensional data, where the number of variables exceeds or is compa...
research
11/18/2017

Fast Monte Carlo Markov chains for Bayesian shrinkage models with random effects

When performing Bayesian data analysis using a general linear mixed mode...
research
04/29/2020

On the convergence complexity of Gibbs samplers for a family of simple Bayesian random effects models

The emergence of big data has led to so-called convergence complexity an...
research
03/04/2020

Fast sampling from β-ensembles

We study sampling algorithms for β-ensembles with time complexity less t...
research
08/24/2022

Spectral Telescope: Convergence Rate Bounds for Random-Scan Gibbs Samplers Based on a Hierarchical Structure

Random-scan Gibbs samplers possess a natural hierarchical structure. The...
research
08/09/2020

A New Spatial Count Data Model with Time-varying Parameters

Recent crash frequency studies incorporate spatiotemporal correlations, ...
research
11/15/2021

Amended Gibbs samplers for Cosmic Microwave Background power spectrum estimation

We study different variants of the Gibbs sampler algorithm from the pers...

Please sign up or login with your details

Forgot password? Click here to reset