This repository contains several illustrations of the use of Biips software via R and MATLAB.
First, in order to familiarize with the software, we provide a tutorial in three parts:
tutorial
: Inference on a standard univariate nonlinear non-Gaussian state-space model (aka hidden Markov model).tutorial1
: Sequential Monte Carlo (SMC), Particle Independent Metropolis Hastings (PIMH)tutorial2
: Sensitivity analysis with SMC, Particle Marginal Metropolis-Hastings (PMMH)tutorial3
: Adding a user-defined function
Three additional, realistic applications are then provided:
stoch_volatility
: (Switching) Stochastic volatilitystoch_volatility
: Stochastic volatility modelswitch_stoch_volatility
: Switching Stochastic volatility modelswitch_stoch_volatility_param
: Switching Stochastic volatility model with parameter estimation
stoch_kinetic
: Stochastic kineticstoch_kinetic
: Stochastic kinetic prey-predator modelstoch_kinetic_gill
: Stochastic kinetic prey-predator model with Gillespie algorithm
object_tracking
: Object trackingstoch_kinetic
: 4-dimensional (2-d position and velicity) radar tracking model