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Found 4 papers, 4 papers with code
The Neural Moving Average Model for Scalable Variational Inference of State Space Models
Variational inference has had great success in scaling approximate Bayesian inference to big data by exploiting mini-batch training.
Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms
Stochastic differential equation mixed-effects models (SDEMEMs) are flexible hierarchical models that are able to account for random variability inherent in the underlying time-dynamics, as well as the variability between experimental units and, optionally, account for measurement error.
Computation Methodology
Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter
Particle Markov chain Monte Carlo (pMCMC) is now a popular method for performing Bayesian statistical inference on challenging state space models (SSMs) with unknown static parameters.
Computation Methodology
Black-box Variational Inference for Stochastic Differential Equations
Parameter inference for stochastic differential equations is challenging due to the presence of a latent diffusion process.
