+------------------------------ | | Running with BAT | Version 0.9.rc3 | | http://www.mppmu.mpg.de/bat +------------------------------ Summary : Opening logfile log.txt 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 = 7.62%) in chain 0. Set scale to 0.5 Detail : Efficiency of parameter 1 dropped below 15.00% (eps = 13.75%) in chain 0. Set scale to 0.5 Detail : Efficiency of parameter 0 dropped below 15.00% (eps = 7.67%) in chain 1. Set scale to 0.5 Detail : Efficiency of parameter 1 dropped below 15.00% (eps = 13.98%) in chain 1. Set scale to 0.5 Detail : Efficiency of parameter 0 dropped below 15.00% (eps = 7.75%) in chain 2. Set scale to 0.5 Detail : Efficiency of parameter 1 dropped below 15.00% (eps = 13.60%) in chain 2. Set scale to 0.5 Detail : Efficiency of parameter 0 dropped below 15.00% (eps = 8.35%) in chain 3. 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 = 6.53%) in chain 4. Set scale to 0.25 Detail : Efficiency of parameter 1 dropped below 15.00% (eps = 14.30%) 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 = 14.03%) in chain 0. Set scale to 0.25 Detail : Efficiency of parameter 0 dropped below 15.00% (eps = 14.47%) in chain 1. Set scale to 0.25 Detail : Efficiency of parameter 0 dropped below 15.00% (eps = 14.32%) in chain 3. 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 not within pre-defined range. Detail : - Efficiencies: Detail : Efficiency of parameter 0 dropped below 15.00% (eps = 14.30%) in chain 2. Set scale to 0.25 Detail : --> Iteration 13000 Detail : --> Iteration 14000 Detail : --> Iteration 15000 Detail : --> Iteration 16000 Detail : * Convergence status: Set of 5 Markov chains converged within 16000 iterations. Detail : * Efficiency status: Efficiencies not within pre-defined range. Detail : - Efficiencies: Detail : Efficiency of parameter 3 dropped below 15.00% (eps = 14.85%) in chain 3. Set scale to 0.5 Detail : --> Iteration 17000 Detail : --> Iteration 18000 Detail : --> Iteration 19000 Detail : --> Iteration 20000 Detail : * Convergence status: Set of 5 Markov chains converged within 20000 iterations. Detail : * Efficiency status: Efficiencies within pre-defined ranges. Summary : --> Set of 5 Markov chains converged within 20000 iterations and all scales are adjusted. Summary : --> Markov chains ran for 20001 iterations. Detail : --> Average efficiencies: Detail : --> parameter 0 : 26.05% Detail : --> parameter 1 : 25.57% Detail : --> parameter 2 : 17.89% Detail : --> parameter 3 : 18.41% Detail : --> Average scale factors: Detail : --> parameter 0 : 25.00% Detail : --> parameter 1 : 50.00% Detail : --> parameter 2 : 100.00% Detail : --> parameter 3 : 90.00% 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: 38 Detail : --> parameter 1: 20.35 Detail : --> parameter 2: 3.754e-05 Detail : --> parameter 3: 0.9299 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 = 3.28e-10 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