Dear all:
I want to find the best fit Log(Like) value of a given cosmological model using some datasets.I set action = 2 in params.ini since MCMC cannot provide accurate best fit values. Testing with \Lambda CDM model using WMAPonly data , I found the same result using two different initial seeds as follows:
Random seeds: 4321, 9373 rand_inst: 0
Random seeds: 12345, 9373 rand_inst: 0
Computing tensors: F
Doing CMB lensing: F
lmax = 2100
Number of C_ls = 3
Varying 6 parameters ( 2 fast)
reading: params_CMB.covmat
Finding maxlike point
reading WMAP data
WMAP read
Change in F is less than 0.10000000 after 4 iterations
Found minimum value of Log(Like) = 717.2668
Converged sucessfully (loglike changes by less than 0.1000000 )
after 4 iterations
Best fit parameters values:
1 : 2.3866521E02
2 : 0.1122643
3 : 1.049735
4 : 0.1119891
8 : 0.9966581
11 : 3.118499
Have estimated the minimum, now exiting since action=2
Wrote the minimum to file chains/test2.minimum
I used all the default setting in params.ini except
action=2
delta_loglike = 0.1
use_CMB = T
use_HST = F
use_mpk = F
use_clusters = F
use_BBN = F
use_Age_Tophat_Prior = F
use_SN = F
use_min_zre = 0
The bestfit Log(Like)=717.2668 is larger than the other published fitting results ( around 714 ),I have tested by setting delta_loglike = 1,2 ,the bestfit Log(Like) got larger. Did I make any mistakes ? Any comments and suggestion on that ?
Thanks in advance .
Cheers
Gongbo Zhao
CosmoMC: Finding the best fit Log likelihood

 Posts: 70
 Joined: January 04 2005
 Affiliation: ICG, Portsmouth
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 Posts: 144
 Joined: September 24 2004
 Affiliation: University College London (UCL)
 Contact:
CosmoMC: Finding the best fit Log likelihood
Hi,
The minimiser in fact does not use the random seed generator.
The minimiser finds the gradient at the initial values of the cosmological parameters, as set in the params.ini file, and then uses the conjugate gradient algorithm as in Numerical Recipes (using bracketing around gradient=0 to find the minimum in each direction).
It chooses to stop refining the search when the change in the likelihood is less than delta_loglike.
Therefore for most problems, the log likelihood at the best fit point will be correct to very roughly +/ delta_loglike, but this depends on the geometry of the probability surface around the best fit point.
Decreasing delta_loglike to e.g. 0.1 would continue the search for longer, and therefore get closer to the true local minimum.
You could also consider changing the starting position for the search, by changing the cosmological parameter values in the first column in params.ini.
Hope this makes more sense than my previous message,
Sarah
The minimiser in fact does not use the random seed generator.
The minimiser finds the gradient at the initial values of the cosmological parameters, as set in the params.ini file, and then uses the conjugate gradient algorithm as in Numerical Recipes (using bracketing around gradient=0 to find the minimum in each direction).
It chooses to stop refining the search when the change in the likelihood is less than delta_loglike.
Therefore for most problems, the log likelihood at the best fit point will be correct to very roughly +/ delta_loglike, but this depends on the geometry of the probability surface around the best fit point.
Decreasing delta_loglike to e.g. 0.1 would continue the search for longer, and therefore get closer to the true local minimum.
You could also consider changing the starting position for the search, by changing the cosmological parameter values in the first column in params.ini.
Hope this makes more sense than my previous message,
Sarah