+------------------------------
 |
 |     Running with BAT
 |      Version 0.9.rc3
 |
 | http://www.mppmu.mpg.de/bat
 +------------------------------
Summary : Opening logfile log.txt
Error   : BCModel::SetPriorGauss : Index out of range.
Summary : Running MCMC for model 'BCEfficiencyFitter with f1'
Summary : Pre-run Metropolis MCMC...
Summary :  --> Perform MCMC pre-run with 5 chains, each with maximum 1000000 iterations
Detail  :  --> Iteration 1000
Detail  :  --> Iteration 2000
Detail  :  --> Iteration 3000
Detail  :  --> Iteration 4000
Detail  :      * Convergence status: Set of 5 Markov chains converged within 4000 iterations.
Detail  :      * Efficiency status: Efficiencies not within pre-defined range.
Detail  :        - Efficiencies:
Detail  :          Efficiency of parameter 0 dropped below 15.00% (eps = 8.12%) in chain 0. Set scale to 0.5
Detail  :          Efficiency of parameter 1 dropped below 15.00% (eps = 14.32%) in chain 0. Set scale to 0.5
Detail  :          Efficiency of parameter 0 dropped below 15.00% (eps = 7.88%) in chain 1. Set scale to 0.5
Detail  :          Efficiency of parameter 1 dropped below 15.00% (eps = 14.80%) in chain 1. Set scale to 0.5
Detail  :          Efficiency of parameter 0 dropped below 15.00% (eps = 7.22%) in chain 2. Set scale to 0.25
Detail  :          Efficiency of parameter 0 dropped below 15.00% (eps = 7.17%) in chain 3. Set scale to 0.25
Detail  :          Efficiency of parameter 0 dropped below 15.00% (eps = 7.67%) in chain 4. Set scale to 0.5
Detail  :          Efficiency of parameter 1 dropped below 15.00% (eps = 13.12%) in chain 4. Set scale to 0.5
Detail  :  --> Iteration 5000
Detail  :  --> Iteration 6000
Detail  :  --> Iteration 7000
Detail  :  --> Iteration 8000
Detail  :      * Convergence status: Set of 5 Markov chains converged within 8000 iterations.
Detail  :      * Efficiency status: Efficiencies not within pre-defined range.
Detail  :        - Efficiencies:
Detail  :          Efficiency of parameter 0 dropped below 15.00% (eps = 14.60%) in chain 0. Set scale to 0.25
Detail  :          Efficiency of parameter 0 dropped below 15.00% (eps = 14.88%) in chain 1. Set scale to 0.25
Detail  :          Efficiency of parameter 1 dropped below 15.00% (eps = 14.50%) in chain 2. Set scale to 0.5
Detail  :          Efficiency of parameter 1 dropped below 15.00% (eps = 14.27%) in chain 3. Set scale to 0.5
Detail  :          Efficiency of parameter 0 dropped below 15.00% (eps = 13.63%) in chain 4. Set scale to 0.25
Detail  :  --> Iteration 9000
Detail  :  --> Iteration 10000
Detail  :  --> Iteration 11000
Detail  :  --> Iteration 12000
Detail  :      * Convergence status: Set of 5 Markov chains converged within 12000 iterations.
Detail  :      * Efficiency status: Efficiencies within pre-defined ranges.
Summary :  --> Set of 5 Markov chains converged within 12000 iterations and all scales are adjusted.
Summary :  --> Markov chains ran for 12001 iterations.
Detail  :  --> Average efficiencies:
Detail  :  -->      parameter    0 :  26.90%
Detail  :  -->      parameter    1 :  26.43%
Detail  :  -->      parameter    2 :  17.60%
Detail  :  -->      parameter    3 :  15.78%
Detail  :  --> Average scale factors:
Detail  :  -->      parameter    0 :  25.00%
Detail  :  -->      parameter    1 :  50.00%
Detail  :  -->      parameter    2 :  100.00%
Detail  :  -->      parameter    3 :  100.00%
Summary : Run Metropolis MCMC...
Summary :  --> Perform MCMC run with 5 chains, each with 200000 iterations.
Detail  :  --> iteration number 10000 (5.00%)
Detail  :  --> iteration number 20000 (10.00%)
Detail  :  --> iteration number 30000 (15.00%)
Detail  :  --> iteration number 40000 (20.00%)
Detail  :  --> iteration number 50000 (25.00%)
Detail  :  --> iteration number 60000 (30.00%)
Detail  :  --> iteration number 70000 (35.00%)
Detail  :  --> iteration number 80000 (40.00%)
Detail  :  --> iteration number 90000 (45.00%)
Detail  :  --> iteration number 100000 (50.00%)
Detail  :  --> iteration number 110000 (55.00%)
Detail  :  --> iteration number 120000 (60.00%)
Detail  :  --> iteration number 130000 (65.00%)
Detail  :  --> iteration number 140000 (70.00%)
Detail  :  --> iteration number 150000 (75.00%)
Detail  :  --> iteration number 160000 (80.00%)
Detail  :  --> iteration number 170000 (85.00%)
Detail  :  --> iteration number 180000 (90.00%)
Detail  :  --> iteration number 190000 (95.00%)
Detail  :  --> iteration number 200000 (100.00%)
Summary :  --> Markov chains ran for 200000 iterations.
Detail  :  --> Global mode from MCMC:
Detail  :  -->      parameter 0:   37.99
Detail  :  -->      parameter 1:   20.14
Detail  :  -->      parameter 2:   8.015e-05
Detail  :  -->      parameter 3:   0.9286
Summary : ---------------------------------------------------
Summary : Fit summary for model 'BCEfficiencyFitter with f1':
Summary :    Number of parameters:  Npar  = 4
Summary :    Number of data points: Ndata = 20
Summary :    Number of degrees of freedom:
Summary :       NDoF = Ndata - Npar = 16
Summary :    Best fit parameters (global):
Summary :       p0 = 38
Summary :       p1 = 20.2
Summary :       p2 = 9.48e-11
Summary :       p3 = 0.929
Summary :    Goodness-of-fit test:
Summary :       p-value = 0
Summary : ---------------------------------------------------
Summary : Printing all marginalized distributions (4 x 1D + 6 x 2D = 10) into file distributions.ps