Add overdispersed models to contrib.epidemiology #2498
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This was referenced May 22, 2020
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| if _is_zero(overdispersion): | ||
| return dist.ExtendedBinomial(total_count, probs) | ||
| if getattr(probs, "dtype", torch.float64) != torch.float64: | ||
| warnings.warn("binomial_dist is unstable for dtypes less than torch.float64; " |
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is this really the source?
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Good point. I've moved the warning to CompartmentalModel.fit() and .predict().
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@martinjankowiak thanks for your patience. I've isolated the changes to two new models. I plan to test these on real data in the next couple weeks. |
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martinjankowiak
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May 30, 2020
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Addresses #2426
This aims to improve fit to real data and improve mixing by relaxing all
Binomialdistributions to precisely overdispersedBetaBinomials such that the large population limiting dynamics is an SDE rather than a deterministic ODE. These new distributions are used in two new models:OverdispersedSIRModelandOverdispersedSEIRModel.Questions
overdispersionparameter reasonable?Beta(2, 2)Tested
BetaBinomials results in ~20% slowdown of likelihood computation