# Scalable Monte Carlo Inference and Rescaled Local Asymptotic Normality

arXiv: https://arxiv.org/abs/2007.00723 · HTML: https://mlsanity.com/paper/2007.00723/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [The mbsts package: Multivariate Bayesian Structural Time Series Models in R](https://mlsanity.com/paper/2106.14045/) (2021)

## Abstract (arXiv)

In this paper, we generalize the property of local asymptotic normality (LAN) to an enlarged neighborhood, under the name of rescaled local asymptotic normality (RLAN). We obtain sufficient conditions for a regular parametric model to satisfy RLAN. We show that RLAN supports the construction of a statistically efficient estimator which maximizes a cubic approximation to the log-likelihood on this enlarged neighborhood. In the context of Monte Carlo inference, we find that this maximum cubic likelihood estimator can maintain its statistical efficiency in the presence of asymptotically increasing Monte Carlo error in likelihood evaluation.