Quasi-Newton particle Metropolis-Hastings
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Updated
Nov 29, 2017 - Python
Quasi-Newton particle Metropolis-Hastings
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
Create dynamic factor models in R with the dfms package
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.
an R package implementing the filtering algorithms for the state-space models on the Stiefel manifold
Gradient-informed particle MCMC methods
Code for the paper "Backward importance sampling for online estimation of state space models"
Switching linear dynamical systems (SLDS) models in JAX
ForneyLab.jl factor node for generalised filtering with exogenous input.
A Java library for State Space Models (SSM).
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
Second-order iterated smoothing algorithms for state estimation
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.
Accompanying notebook guides for the deep signal processing notes [TBA].
List of papers related to State Space Models (Mamba) in Vision.
Official implementation of the CBF-SSM model
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