# A New Stochastic Approximation Method for Gradient-based Simulated Parameter Estimation

arXiv: https://arxiv.org/abs/2503.18319 · HTML: https://mlsanity.com/paper/2503.18319/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Black Box Variational Inference](https://mlsanity.com/paper/1401.0118/) (2013)
- [Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows](https://mlsanity.com/paper/1805.07226/) (2018)
- [Automatic Posterior Transformation for Likelihood-Free Inference](https://mlsanity.com/paper/1905.07488/) (2019)
- [Implicit Reparameterization Gradients](https://mlsanity.com/paper/1805.08498/) (2018)
- [Variational methods for simulation-based inference](https://mlsanity.com/paper/2203.04176/) (2022)
- [The Generalized Reparameterization Gradient](https://mlsanity.com/paper/1610.02287/) (2016)
- [Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization](https://mlsanity.com/paper/2408.11974/) (2024)
- [Quantile-Based Policy Optimization for Reinforcement Learning](https://mlsanity.com/paper/2201.11463/) (2022)
- [Quantile Optimization via Multiple Timescale Local Search for Black-box Functions](https://mlsanity.com/paper/2308.07607/) (2023)
- [Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment](https://mlsanity.com/paper/2410.21109/) (2024)

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

- [Optimal low-rank stochastic gradient estimation for LLM training](https://mlsanity.com/paper/2603.20632/) (2026)

## Abstract (arXiv)

This paper tackles the challenge of parameter calibration in stochastic models, particularly in scenarios where the likelihood function is unavailable in an analytical form. We introduce a gradient-based simulated parameter estimation framework, which employs a multi-time scale stochastic approximation algorithm. This approach effectively addresses the ratio bias that arises in both maximum likelihood estimation and posterior density estimation problems. The proposed algorithm enhances estimation accuracy and significantly reduces computational costs, as demonstrated through extensive numerical experiments. Our work extends the GSPE framework to handle complex models such as hidden Markov models and variational inference-based problems, offering a robust solution for parameter estimation in challenging stochastic environments.