This module is an efficient and flexible implementation of various Sequential Monte Carlo (SMC) methods. Bayesian updates occur for both latent states and model parameters using joint inference.
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Updated
Apr 18, 2023 - Julia
This module is an efficient and flexible implementation of various Sequential Monte Carlo (SMC) methods. Bayesian updates occur for both latent states and model parameters using joint inference.
Sequential Monte Carlo methods in Julia (experimental)
Building blocks for simple and advanced particle filtering in Gen.
Implementation of advanced Sequential Monte Carlo and particle MCMC algorithms
State estimation, smoothing and parameter estimation using Kalman and particle filters.
Sequential Monte Carlo algorithm for approximation of posterior distributions.
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