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

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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

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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

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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

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