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arXiv Statistics ML By Grace Yan, Mark Beaumont, Dennis Prangle

Neural composite likelihood estimation: simulation based inference for time series

Read the original on arXiv Statistics ML →

Neural Composite Likelihood Estimation (NCLE) extends simulation‑based inference to high‑dimensional time series by partitioning long sequences into equal‑sized batches. For each batch, a neural network estimates the likelihood via conditional density estimation, and the product of these batch likelihoods forms an approximate composite likelihood. Frequentist inference is then performed by maximizing this composite likelihood to obtain a point estimate and by estimating the Godambe information matrix to derive confidence intervals.

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