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

• URL: http://arxiv.org/abs/2503.18319v1
• Date: Mon, 24 Mar 2025 03:54:50 GMT
• Title: A New Stochastic Approximation Method for Gradient-based Simulated Parameter Estimation
• Authors: Zehao Li, Yijie Peng,
• Abstract summary: We introduce a gradient-based simulated parameter estimation framework, which employs a multi-time scale approximation algorithm.<n>This approach effectively addresses the ratio bias that arises in both maximum likelihood estimation and posterior density estimation problems.<n>Our work extends the GSPE framework to handle complex models such as Markov models and variational inference-based problems.
• Score: 0.7673339435080445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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