Minimal Working Examples for Particle Metropolis-Hastings
State estimation in a linear Gaussian state space model
Parameter estimation in a linear Gaussian state space model
Parameter estimation in a simple stochastic volatility model
Parameter estimation in a simple stochastic volatility model
Parameter estimation in a simple stochastic volatility model
Generates data from a linear Gaussian state space model
Kalman filter for state estimate in a linear Gaussian state space mode...
Make plots for tutorial
Fully-adapted particle filter for state estimate in a linear Gaussian ...
Bootstrap particle filter for state estimate in a simple stochastic vo...
Particle Metropolis-Hastings algorithm for a linear Gaussian state spa...
Particle Metropolis-Hastings algorithm for a stochastic volatility mod...
Particle Metropolis-Hastings algorithm for a stochastic volatility mod...
Routines for state estimate in a linear Gaussian state space model and a simple stochastic volatility model using particle filtering. Parameter inference is also carried out in these models using the particle Metropolis-Hastings algorithm that includes the particle filter to provided an unbiased estimator of the likelihood. This package is a collection of minimal working examples of these algorithms and is only meant for educational use and as a start for learning to them on your own.