BayesBiont.jl
BayesBiont.jl is the Bayesian companion to Kinbiont.jl. It adds calibrated uncertainty quantification, hierarchical pooling across replicates, and principled model comparison to the same growth-curve models the field already uses.
It is additive, not competitive: BayesBiont consumes Kinbiont's GrowthData container and curve definitions directly, and returns posterior distributions where Kinbiont returns point estimates.
At a glance
| Kinbiont | BayesBiont |
|---|---|
| Maximum-likelihood / least-squares point estimates | Posterior distributions (NUTS sampling via Turing) |
| Delta-method or bootstrap CIs | Calibrated credible intervals |
| Replicates fit independently | Hierarchical pooling via group= |
| AICc model selection | LOO / WAIC + compare() for ELPD differences |
| No self-diagnostic | Pareto-k flags when LOO is unreliable |
The same GrowthData(curves, times, labels) you'd build for Kinbiont's kinbiont_fit goes straight into BayesBiont's bayesfit — usually <10 lines of code change.
New to BayesBiont?
Start with Install, then Quick start. The other sections cover specific capabilities:
When to reach for BayesBiont
- You compare strains, treatments, or conditions and want calibrated probability statements about differences (
P(μ_A > μ_B)) rather than ad-hoc t-tests on fitted point estimates. - You have replicates per condition and want to pool information correctly across them (hierarchical Bayes), not average-then-fit.
- You want to know whether a fitted ODE model (aHPM, HPM, etc.) is actually appropriate for your data —
loo()+ Pareto-k tells you when the answer is "no". - You're producing posterior predictive bands on growth-curve fits, not just point fits.
When you don't need BayesBiont
- You want a quick point estimate of growth rate from one curve — stick with Kinbiont, it's faster.
- You don't have replicates and don't care about uncertainty — point estimates are fine.
- You're doing exploratory data inspection — Kinbiont's NL fits return in milliseconds; BayesBiont's NUTS runs take seconds to minutes per curve.
Citing
If you use BayesBiont in a publication, please cite both BayesBiont and the underlying Kinbiont paper:
Angaroni F. et al. Kinbiont.jl: a flexible Julia toolkit for kinetic modelling of microbial systems. (Kinbiont reference.)
License
MIT. Maintained by Fuzue Tech.