Open bas-rustenburg opened 9 years ago
Rescaling after about 975000 steps using same settings. Full series of plots
Here is the Metropolis data after 1000000:
So, an old version of the twocomponentmodel was using it correctly. I think a little too much copy paste action from merging several versions of ITC.py and models.py of yore dropped the metropolis step. Opening a separate PR to fix after the current.
CompetitiveBindingModel in it's current form uses both.
Copy-paste from #55 Here is the data, fixing the bug so we're using both Metropolis and RescalingStep at once.
1,000,000 steps, 10000 burn-in, thinning period of 25:
Comparison, for convenience:
Just Metropolis:
Both Metropolis and RescalingStep:
Looks like it isn't worth the added complexity right now. Ditch RescalingStep?
Sure, will switch back to regular Metropolis. Leaving the (fixed) implementation if we decide tweaking something, so will merge once I set things back to using just Metropolis.
Copy-paste from #53
Here is something interesting.
I took a look at the different step methods for MCMC. Here is the host concentration, estimated at 1.0157 mM in this particular experiment (I just picked a host-guest titration at random from one of our previous sets of experiments).
Settings: Using the TwoComponentBindingModel about 320000 iterations (I stopped it at around 16% of 2000000) 10000 burn in iterations and a thinning period of 25. Not sure how good those settings are but there is quite a striking difference.
Using the RescalingStep:
Note that the value on the y-axis is offset by 1.0124
Using a default pymc Metropolis step:
![sample-ls](https://cloud.githubusercontent.com/assets/3977731/6448989/a434d0be-c0eb-11e4-8f59-3027079b75d7.png)