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tried: - auto-switching solvers - switching optimizer to `LBFGS` - truncated prior distributions latter seems most promising
# P6 Calibration intro - Slightly add onto intro explanation for MLE vs MAP and MLE/MAP vs MCMC - Updated parameter explanation: we don't directly specify initial values for optimization - Add part explaining priors - Move section "The measurement data" to before prior explanations to better fit flow of the explanation - Add truncation to priors if necessary - Change `InverseGamma` prior to better known `Exponential` distribution - Remove observed output variable `W_meas` from model input and condition it instead (also matching P8) - Remove instantiations of unused `ODEProblems` outside of Turing models - Add chain plots to check convergence - Remove use of `summarize` function to extract mean values from chain Fermenter calibration: - Remove observed output variable from model input and condition it instead (also matching P8) - Remove instantiations of unused `ODEProblems` outside of Turing models - Changed `InverseGamma` priors to `Exponential`s Irrigation - Remove observed output variable from model input and condition it instead (also matching P8) - Changed solver to `AutoTsit5(Rosenbrock23())` - Changed `InverseGamma` priors to `Exponential`s Wastewater treatment - Changed solver to `AutoTsit5(Rosenbrock23())` # P8 Model selection intro - Copied model changes from calibration intro - Changed solution of exercise 2 to MAP estimation, as requested in the exercise - Remove instantiations of unused `ODEProblems` outside of Turing models Friction notebook - Changed to match other updates - Changed `InverseGamma` priors to `Exponential`s - Changed solver to `AutoTsit5(Rosenbrock23())`
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Once the build has completed, you can preview your PR at this URL: https://Kermit-UGent.github.io/ModSim/previews/PR69/ |
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Good additions |
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Approved by @gvhaelew 🎊 |
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P5
Advanced notebook
Turingmodel to prevent world-age issuesPtto be positive to prevent errors when it's used as the mean of a Poisson distributionP6
All
AutoTsit5(Rosenbrock23())Calibration intro
InverseGammaprior to better knownExponentialdistributionW_measfrom model input and condition it instead (also matching P8)ODEProblemsoutside of Turing modelssummarizefunction to extract mean values from chainFermenter calibration:
ODEProblemsoutside of Turing modelsInverseGammapriors toExponentialsIrrigation
InverseGammapriors toExponentialsP8
Model selection intro
ODEProblemsoutside of Turing modelsFriction notebook
InverseGammapriors toExponentialsAutoTsit5(Rosenbrock23())