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
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.
an R package implementing the filtering algorithms for the state-space models on the Stiefel manifold
A Python package to demonstrate ideas from nonlinear dynamical systems toward game theory, neural network models of associative memory, and nonlinear state space models.
This repository contains assignments code and reports of CH3050 Process Dynamics and Control course at IIT MADRAS in Autumn 2020 Semester
This repository provides code in R reproducing examples of the states space models presented in book "An Introduction to State Space Time Series Analysis" by J.J.F. Commandeur and S.J. Koopman.
Fit multivariate state-space autoregressive models in Jags
Bayesian Particle Learning models in R
A Java library for State Space Models (SSM).
Official implementation of the CBF-SSM model
ForneyLab.jl factor node for generalised filtering with exogenous input.
Code for the paper "Backward importance sampling for online estimation of state space models"
Official implementation of our ECCV paper "StretchBEV: Stretching Future Instance Prediction Spatially and Temporally"
Julia package for simulating and estimating multi-level/hierarchical dynamic factor models (HDFMs).
Accompanying notebook guides for the deep signal processing notes [TBA].
elmar mertens fortran toolboxes
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)
Julia package for automatically generating Bayesian inference algorithms through message passing on Forney-style factor graphs.
Development of An Automated Conflict Prediction System by State Space ARIMA Methods
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