This package implements the Sequential Monte Carlo "squared" (SMC^2) algorithm from Chopin, Jacob, and Papaspiliopoulos (2013). This algorithm allows sequential Bayesian inference of both latent variables and parameters in a general non-linear state-space model. If only batch inference on model parameters is needed, there is also an implementation of the Density-tempered algorithm from Duan & Fulop (2015), which offers a more direct pathway from the prior to the posterior.
|Maintainer||Derek Hansen <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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