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Found 3 papers, 2 papers with code
Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis, and prediction in the life sciences
A fundamental and often overlooked choice in this approach involves relating the solution of a mathematical model with noisy and incomplete measurement data.
Likelihood-based estimation and prediction for a measles outbreak in Samoa
Stochastic models can help with misspecification but are even more expensive to simulate and perform inference with.
What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems
Firstly, we show how to translate causal inference problems into an abstract statistical formalism without requiring any structure beyond an arbitrarily-indexed family of probability models.
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