Roadmap

[simonbyrne]

Functionality

• EKI
• higher-level interface for NEKI
• Gaussian process interpolation
  • Can we use https://github.com/STOR-i/GaussianProcesses.jl or do we need to write our own?
• MCMC functionality (@glwagner, @sandreza)
  • Random-walk MH
  • Slice sampling
  • HMC (https://github.com/tpapp/DynamicHMC.jl)
• Experimental design?
• Multi-fidelity (@odunbar)
• Parameter handling?

Test/Examples

• Linear case (@agarbuno)
  • check it converges to the correct distribution
• Lorenz examples (@dburov190)
• Elliptic / Darcy flow PDE (@agarbuno)
• Simple MCMC examples
  • Gaussian
  • heavy tail
  • Gaussian mixture
  • Double well
• Single column model
  • Can compare direct MCMC with NEKI/GP approximation

For next meeting:

• Lorenz model / GP (@dburov190 )
• NEKI/EKI with simple examples (@agarbuno @odunbar @jinlong83)
• Single column model / MCMC (@glwagner @sandreza)

[dburov190]

I've marked "Gaussian processes" done for now; the implementation is in GPR.jl. It uses ScikitLearn.jl, which is quite bulky and ugly, but good enough for now. Planning to switch to GaussianProcesses.jl in the future.