I have been thinking about an idea that will be surely clumsy: using 'perftmc' command of GNU 5.07.173b (which gives different values in each try), could I do a good estimate averaging results? Here is a typical output:
Code: Select all
m=6.188431e+019 sd=5.191844e+015 ci(99%)=[6.187093e+019,6.189768e+019] n=2001154
971 sdn=2.322533e+020 t=37391.12s
My question is the following: if I run N MonteCarlo samples of the same position and I stop all of them at the same point (with the same number of nodes ± a very little amount), can I average the results in this way?
Code: Select all
m_1, m_2, ..., m_N
(Average m) = <m> = (1/N)·(m_1 + m_2 + ... + m_N)
sd_1, sd_2, ... sd_N
(Average standard deviation) = <sd> = sqrt{(1/N)·[(sd_1)² + (sd_2)² + ... + (sd_N)²]}
Code: Select all
<m> = [(n_1)·(m_1) + (n_2)·(m_2) + ... + (n_N)·(m_N)]/(n_1 + n_2 + ... n_N)
<sd> = sqrt{[(sdn_1)² + (sdn_2)² + ... + (sdn_N)²]/(n_1 + n_2 + ... n_N)}
Are my assumptions correct or I have failure concepts? In the case that I am right (very unlikely), the estimate would be good/accurate or is it a waste of time because of the size of the standard deviation (or other issues)? Thanks in advance.
Regards from Spain.
Ajedrecita.