Toward Practical N2 Monte Carlo: the Marginal Particle Filter

Understand

Sequential Monte Carlo techniques are useful for state estimation in non-linear, non-Gaussian dynamic models.

• These methods allow us to approximate the joint posterior distribution using sequential importance sampling.
• In this framework, the dimension of the target distribution grows with each time step, thus it is necessary to introduce some resampling steps to ensure that the estimates provided by the algorithm have a reasonable variance.
• In many applications, we are only interested in the marginal filtering distribution which is defined on a space of fixed dimension.