Closed rjhanes closed 2 years ago
@jwalzberg and @TJTapajyoti A heads up that I'll be going over this PR in our hackathon tomorrow. I've prepared a scenario YAML file to start our stochastic runs on HPC (in OneDrive under uncertainty/stochastic-yamls), and this PR covers changes to the Cost Methods that will enable those runs.
I've identified eight parameters to include in the stochastic runs, based on our OAAT results:
To ensure that we get results worth interpreting, I recommend we use the +/- 50% range to define the triangular probability distributions. How many runs we execute is up for debate - we should probably discuss how the single-run time has changed with adding two states and the pyLCA 2.0 upgrade and go from there. Since we're not trying to do statistical analyses or actual sensitivity analyses I think we can pretty much pick a nice round number.
How the CostMethods worked before
How the CostMethods will work following this PR
Data and config changes to support this PR
cost_uncertainty
dictionary in the scenario YAML file changed format to allow for stochastic capabilities.stochastic-uncertainty
branch in the data repo for an updated scenario YAML file that executes a baseline (MCiC-MC) run using this new format. I'll merge those changes in to all our stable data branches after this PR is merged in.Stochastic scenario YAML for use with tiny data