Percentiles Bayes Error Plot for MM

[alexlafleur]

taumeta: 4
eta: 16
scale_window : 16
shift: 64
window_size: 1024
num_estimations: 18
len_trajectory: 2177
num_trajectories: 4
num_trajectorieslen_trajectory: 8708
NAIVE window_size * num_estimations 19456
BAYES window_size + num_estimationsshift 2176

numruns=8
num_trajectories (simulated) = 128
numsims = 32
num_trajs = 4

Deciles_Bayes_MM.pdf

Issue follows up on #31

[greenTara]

In this plot the prediction doesn't look as good because the error at k=0 is somewhat off, and that throws everything else off. If there is time, let's increase the sample from each model: numsims = 64.

If we had all the time in the world to finish this, we would fit the constant by taking the ratio of the mean error[k] divided by the formula (without the constant) and then average to get the best-fit value for the constant. But other things are higher priority now.

[alexlafleur]

Deciles_Bayes_MM.pdf
evaluate_deciles_mm.txt