Broadly Applicable Approximate MCMC for Switching Stochastic Differential Equations Using Uniformization and Time-Conditioned Factorized Neural Likelihood Estimation
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).
Key points
- By allowing dynamics to change between regimes, SSDEs represent heterogeneous system behavior and have been applied across diverse fields.
- In this study, we propose an approximate Markov chain Monte Carlo sampler for SSDEs using uniformization and factorized neural likelihood estimation (FNLE), a simulation-based inference method.
- Uniformization provides an exact representation of the CTMC but requires SDE transition densities over arbitrary time intervals.
- In synthetic-data experiments, our method recovered regime paths and parameters for three SSDE models for which previous methods have limited applicability.
Sources (1)
- [1]Broadly Applicable Approximate MCMC for Switching Stochastic Differential Equations Using Uniformization and Time-Conditioned Factorized Neural Likelihood EstimationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 02:56 PM
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 between regimes, SSDEs represent heterogeneous system behavior and have been applied across diverse fields.
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