Quasi-Newton particle Metropolis-Hastings

A Java library for State Space Models (SSM).

Bayesian Particle Learning models in R

A Python package to demonstrate ideas from nonlinear dynamical systems toward game theory, neural network models of associative memory, and nonlinear state space models.

Comparing length generalization performance for Mamba, attention-based transformers and Mamba-attention hybrids

Gradient-informed particle MCMC methods

Second-order iterated smoothing algorithms for state estimation

Provides methods for a linear Gaussian State Space model such as filtering (Kalman filter), smoothing (Kalman smoother), forecasting, likelihood evaluation, and estimation of hyperparameters (Maximum Likelihood, Expectation-Maximization (EM), and Expectation-Conditional Maximization (ECM), w/ and w/o penalization)

Fit multivariate state-space autoregressive models in Jags

ForneyLab.jl factor node for generalised filtering with exogenous input.

Switching linear dynamical systems (SLDS) models in JAX

A Fully Quantized SSM Implementation

Zipkin E.F., Thorson J.T., See K., Lynch H.J., Grant E.H.C., Kanno Y., Chandler R.B., Letcher B.H., and Royle J.A. 2014. Modeling structured population dynamics using data from unmarked individuals. Ecology. 95: 22-29.

Create dynamic factor models in R with the dfms package

Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals

Create Multivariate Autoregressive State-Space Models with the MARSS R package

Translating between two sets of notation for Kalman filters

Code for the paper "Backward importance sampling for online estimation of state space models"

Official implementation of the CBF-SSM model

Development of An Automated Conflict Prediction System by State Space ARIMA Methods