# Ergodicity of Approximate MCMC Chains with Applications to Large Data Sets

@inproceedings{Pillai2014ErgodicityOA, title={Ergodicity of Approximate MCMC Chains with Applications to Large Data Sets}, author={Natesh S. Pillai and Aaron Drake Smith}, year={2014} }

In many modern applications, difficulty in evaluating the posterior density makes performing even a single MCMC step slow. This difficulty can be caused by intractable likelihood functions, but also appears for routine problems with large data sets. Many researchers have responded by running approximate versions of MCMC algorithms. In this note, we develop quantitative bounds for showing the ergodicity of these approximate samplers. We then use these bounds to study the bias-variance trade-off… CONTINUE READING

Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

#### Citations

##### Publications citing this paper.

SHOWING 1-10 OF 37 CITATIONS

## Optimal approximating Markov chains for Bayesian inference

VIEW 3 EXCERPTS

CITES RESULTS & BACKGROUND

HIGHLY INFLUENCED

## Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs

VIEW 3 EXCERPTS

CITES METHODS

HIGHLY INFLUENCED

## Quantitative contraction rates for Markov chains on general state spaces

VIEW 6 EXCERPTS

CITES BACKGROUND

HIGHLY INFLUENCED

## Flexible Bayesian Nonlinear Model Configuration

VIEW 2 EXCERPTS

CITES BACKGROUND

## Sketching for Latent Dirichlet-Categorical Models

VIEW 2 EXCERPTS

CITES BACKGROUND

## Approximate Collapsed Gibbs Clustering with Expectation Propagation

VIEW 1 EXCERPT

CITES BACKGROUND

#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 29 REFERENCES