Sources for the MATLAB implementation of RMHMC using Newton-Raphson steady state finding and linear equation sensitivity analysis
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MCMC
Models
Priors
Results
IDRplots.m
IDSRplots.m
InsulinSetup.m
LICENSE
MAPKplots.m
MAPKplotsOutput.m
MAPKplotsPosterior.m
MAPKsetup.m
MifaSetup.m
MmaSetup.m
README.md
Run_MAPK_using_NRSMMALA.m
Run_MAPK_using_NRsensHMC.m
Run_MAPK_using_NRsensRMHMC.m
Run_MAPK_using_SB_HMC.m
Run_MAPK_using_SB_RMHMC.m
Run_MAPK_using_SB_SMMALA.m
Run_Mifa_using_NRSMMALA.m
Run_Mifa_using_NRsensHMC.m
Run_Mifa_using_NRsensRMHMC.m
Run_Mifa_using_SB_HMC.m
Run_Mifa_using_SB_RMHMC.m
Run_Mifa_using_SB_SMMALA.m
Run_Mma_using_NRsensHMC.m
Run_Mma_using_NRsensRMHMC.m
Run_Mma_using_SB_HMC.m
Run_Mma_using_SB_RMHMC.m
Run_Mma_using_SB_SMMALA.m
Run_Mma_using_SMMALA.m
UWerr.m
gpl.txt
nrsmmala_analysis.m

README.md

RMHMC for steady state ODE models

This collection of matlab and octave scripts was used to perform simulations for the publication «Hamiltonian Monte Carlo Methods for Efficient Parameter Estimation in Steady State Dynamical Systems». You should read the publication first. A link will be provided as soon as the manuscriupt becomes available.

We will improve the documentation and hope that this project will mature to an implementation that can be used more easily by everyone.

This project has a github page

How to replicate the results

The reference implementations of RMHMC, HMC and SMMALA require the matlab toolbox SBPOP to be installed.

The steady state adapted algorithms do not integrate the model and do not require this package.

The model files for the steady state adapted algorithms (NR prefix) have been built using octave forge's symbolic package. However, the symbolic calculations required can be done using any other software capable of symbolic calculations.

The models are named MAPK, Mma and Mifa. You will find setup files for each of them (called MAPKsetup.m, MmaSetup.m and MifaSetup.m). These files set variables that are not algorithm specific. But some of the values may impact an algorithm's performance (such as the step size).

If your SBPOP package is installed correctly you should be able to run the procedures called Run_«model»_using_«algorithm».m.

The only files you need to investigate are the setup files. They contain prior setups, step sizes and sample sizes.

Inspect the simulation results

You have acces to the values of all defined options as used by us during sampling by loading the .mat files in the Results folders and checking the options variable.

How to set up your own example

This is not very convenient yet, since a lot of the model setup is not automated. If you wish to try it out anyway, then inspect the symmodel octave files, e.g. Models/Insulin/Mma_symmodel.m and adjust them to your needs. If you wish you can also inspect the model files themselves, e.g. make_Mma_model.m and produce a similar file for your model using Maple, Maxima, Mathematica or any other symbolic software package.

If you really want to compare performance, you will have to build an SBToolBox2 model file as well. But, keep in mind, that the proposed method (using Newton Raphson and steady state sensitivity analysis) should be strictly superior to the reference implementation in all cases (the drawback beeing the restriction to steady states). So, there is no need to compare RMHMC to the steady state adapted version described in the publication. You will probably want to compare this method to the one you have been using in the past.

Files

All files not mentioned here have been used to process the results and need not necessarily be investigated but are provided nevertheless for the curious.