Tag Archives: state space model
Resources for stochastic differential equation mixed-effects models
[tl;dr here is a collection of resources for SDEMEMs] Mixed-effects models (MEM) are hierarchical models suited for “population inference”, where instead of fitting data from a single experiment, we are interested in learning characteristics common to runs of the same … Continue reading
Sequential Monte Carlo and the bootstrap filter
In the previous post I have outlined three possibilities for approximating the likelihood function of a state space model (SSM). That post is a required read to follow the topics treated here. I concluded with sequential importance sampling (SIS), which … Continue reading
Monte Carlo sampling for likelihood approximation in state space models
In a previous post I have set the problem of estimating parameters for a state-space model (SSM). That post is a required read, also because I set some notation. My final goal is to show how to construct exact Bayesian … Continue reading
State space models and intractable likelihoods
In this first series of posts, I introduce important tools to construct inference methods for the estimation of parameters in stochastic models. Stochastic models are characterized by randomness in their mathematical nature, and since at first I focus on models … Continue reading
Umberto Picchini's research blog 