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New T-SNL algorithm enhances inference for state-space models

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a new algorithm called truncated-SNL (T-SNL) to improve parameter inference in state-space models (SSMs). Existing methods like sequential neural likelihood (SNL) struggle with sample efficiency and scalability for long sequences. T-SNL addresses these limitations, offering a more accurate, stable, and amortized approach that outperforms previous methods in sample efficiency and robustness. AI

IMPACT Introduces a more efficient and scalable method for parameter inference in complex time-series models.

RANK_REASON The cluster contains an academic paper detailing a new algorithm for statistical inference.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

COVERAGE [3]

  1. arXiv stat.ML TIER_1 English(EN) · Shiyi Sun, Geoff K. Nicholls, Jeong Eun Lee ·

    Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation

    arXiv:2601.22367v2 Announce Type: replace Abstract: Generalized Bayesian Inference (GBI) tempers a loss with a temperature $\beta > 0$ to mitigate overconfidence and improve robustness under model misspecification, but existing GBI methods typically rely on costly MCMC or SDE-bas…

  2. arXiv stat.ML TIER_1 English(EN) · Kostas Tsampourakis, V\'ictor Elvira ·

    Truncated Neural Likelihood Estimation for Simulation-Based Inference in State-Space Models

    arXiv:2605.21805v1 Announce Type: cross Abstract: State-space models (SSMs) are powerful probabilistic tools for modeling time-varying systems with latent dynamics. Inference in SSMs involves the estimation of latent states and parameters. In this work, we focus on parameter infe…

  3. arXiv stat.ML TIER_1 English(EN) · Víctor Elvira ·

    Truncated Neural Likelihood Estimation for Simulation-Based Inference in State-Space Models

    State-space models (SSMs) are powerful probabilistic tools for modeling time-varying systems with latent dynamics. Inference in SSMs involves the estimation of latent states and parameters. In this work, we focus on parameter inference, which for SSMs is in general a very challen…

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