Estimating drift and minorization coefficients for Gibbs sampling algorithms
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David A. Spade
Abstract
Gibbs samplers are common Markov chain Monte Carlo (MCMC) algorithms that are used to sample from intractable probability distributions when sampling directly from full conditional distributions is possible. These types of MCMC algorithms come up frequently in many applications, and because of their popularity it is important to have a sense of how long it takes for the Gibbs sampler to become close to its stationary distribution. To this end, it is common to rely on the values of drift and minorization coefficients to bound the mixing time of the Gibbs sampler. This manuscript provides a computational method for estimating these coefficients. Herein, we detail the several advantages of the proposed methods, as well as the limitations of this approach. These limitations are primarily related to the “curse of dimensionality”, which for these methods is caused by necessary increases in the numbers of initial states from which chains need be run and the need for an exponentially increasing number of grid points for estimation of minorization coefficients.
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© 2021 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Estimating drift and minorization coefficients for Gibbs sampling algorithms
- A global random walk on grid algorithm for second order elliptic equations
- Neural network regression for Bermudan option pricing
- Optimising Poisson bridge constructions for variance reduction methods
- On the existence of posterior mean for Bayesian logistic regression
Artikel in diesem Heft
- Frontmatter
- Estimating drift and minorization coefficients for Gibbs sampling algorithms
- A global random walk on grid algorithm for second order elliptic equations
- Neural network regression for Bermudan option pricing
- Optimising Poisson bridge constructions for variance reduction methods
- On the existence of posterior mean for Bayesian logistic regression
