Neural composite likelihood estimation: simulation based inference for time series

2026-09-18

AI Summary

本文提出神经复合似然估计（NCLE）方法，通过将长序列分割成小批次并分别估计其似然性来解决高维时间序列的模拟基础推理问题。

Abstract

Simulation based inference (SBI) circumvents the challenge of intractable likelihoods by using a simulator that generates data given parameter values. For instance, neural likelihood estimation (NLE) estimates the likelihood function by training a neural network to perform conditional density estimation on simulated data given corresponding parameters. However such density estimation is only feasible for relatively low dimensional data. We extend the scalability of SBI methods to a higher dimensional problem: long sequences with a complex dependency structure. We introduce Neural Composite Likelihood Estimation (NCLE). This divides the sequence into smaller, equal-sized batches. Instead of training NLE to estimate the likelihood for an entire sequence, we estimate the likelihood for each batch separately. The product of these forms an approximate composite likelihood (CL), and we perform frequentist inference using methods from the CL literature: we get a point estimate from maximising the approximate CL and obtain confidence intervals by estimating the Godambe information matrix. We demonstrate the effectiveness of NCLE with experiments on time series models.

Problem

• Simulation based inference
• high dimensional data
• time series
• complex dependency structure
• scalability

Innovation

• Neural Composite Likelihood Estimation
• Simulation Based Inference
• Composite Likelihood
• High Dimensional Data
• Time Series