The bayesSSM package provides implementations of particle filtering, Particle MCMC, and related methods for Bayesian inference in state-space models. It includes tools for simulation, posterior inference, and diagnostics.
Particle filter implementations in this package assume a discrete-time state-space model defined by:
A sequence of latent states \(x_0, x_1, \ldots, x_T\) evolving according to a Markov process.
Observations \(y_1, \ldots, y_T\) that are conditionally independent given the corresponding latent states.
The model is specified as: $$x_0 \sim \mu_\theta$$ $$x_t \sim f_\theta(x_t \mid x_{t-1}), \quad t = 1, \ldots, T$$ $$y_t \sim g_\theta(y_t \mid x_t), \quad t = 1, \ldots, T$$
where \(\theta\) denotes model parameters passed via ....
The user provides the following functions:
init_fn: draws from the initial distribution
\(\mu_\theta\).
transition_fn: generates or evaluates the transition
density \(f_\theta\).
weight_fn: evaluates the observation likelihood
\(g_\theta\).
Maintainer: Bjarke Hautop bjarke.hautop@gmail.com [copyright holder]