Chris

Chris

Associate Research Scientist at Columbia University Irving Medical Center.

Generalized Profiling with Stan

less than 1 minute read

Published:

This post is about implementing the generalized profiling method in Stan. Fitting ODE models to timeseries data can be hard, especially if the data shows signs of process noise that cannot be simply absorbed into the measurement noise. A good example is periodic dynamics, in which the phase drifts with time. The deterministic prediction will in this case get out of phase with the stochastic data.

Even if the data is generated by a deterministic process (plus noisy measurments), fitting an ODE model can be problematic because the likelihood landscale can be highly rugged. In both these scenarios, the generalized profiling method could be of use. In this post, I show how this can be implemented in Stan, and fit a predator-pray model to pseudo data.

The post is hosted on my tbz533 blog

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How to calculate WBIC with Stan

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In Bayesian statistics, model comparison ideally is based on Bayes factors. However, these are not always easy to compute. For this reason, approximate measures have been developed. One of these measures is the WBIC, which has is similar to the BIC, but is not merely based on a point estimate and is valid for a larger class of models. In this post, I show how to compute WBIC with a Stan model.