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New MCMC sampler enhances Bayesian inference for switching stochastic differential equations

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new approximate Markov chain Monte Carlo sampler for switching stochastic differential equations (SSDEs). This method utilizes uniformization and time-conditioned factorized neural likelihood estimation (FNLE) to address the challenges in Bayesian inference for SSDEs. The proposed sampler is broadly applicable, overcoming limitations of previous methods such as requiring noise-free observations or univariate states, and has demonstrated success in recovering regime paths and parameters in synthetic experiments and detecting regime transitions in real-world data. AI

IMPACT This new method for SSDE inference could enable more accurate modeling of complex, dynamic systems in fields ranging from biology to finance.

RANK_REASON The cluster contains an academic paper detailing a new computational method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MCMC sampler enhances Bayesian inference for switching stochastic differential equations

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The cluster contains an academic paper detailing a new computational method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shion Hosoda, Michiaki Hamada ·

    Broadly Applicable Approximate MCMC for Switching Stochastic Differential Equations Using Uniformization and Time-Conditioned Factorized Neural Likelihood Estimation

    arXiv:2610.10194v1 Announce Type: cross Abstract: Switching stochastic differential equations (SSDEs) describe continuous-time dynamics whose parameters switch according to a latent regime process that follows a continuous-time Markov chain (CTMC). By allowing dynamics to change …

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