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Results of performance testing for BAT version 0.4.2

Back to | overview for 0.4.2 | all versions |

Test "1d_poisson_13"

Results
Status good
CPU time 72.63 s
Real time 72.74 s
Plots 1d_poisson_13.ps
Log 1d_poisson_13.log

Settings
N chains 10
N lag 10
Convergence true
N iterations (pre-run) 1000
N iterations (run) 10000000

Plots
Auto-correlation for the parameter.Distribution from MCMC and analytic function.Distribution from MCMC and analytic function in log-scale.
Difference between the distribution from MCMC and the analytic function. The one, two and three sigma uncertainty bands are colored green, yellow and red, respectively.Pull between the distribution from MCMC and the analytic function. The Gaussian has a mean value of 0 and a standard deviation of 1 (not fitted).Summary of subtest values.

Subtest Status Target Test Uncertainty Deviation [%] Deviation [sigma] Tol. (Good) Tol. (Flawed) Tol. (Bad)
correlation par 0 off 0 0.1181 0.01176 - -10.05 0.3 0.5 0.7
chi2 good 93 126.5 13.64 36.07 -2.46 40.91 68.19 95.47
KS good 1 0.8907 0.95 -10.93 0.1151 0.95 0.99 0.9999
mean good 14 14 0.001182 -3.646e-05 0.004317 0.003547 0.005912 0.008277
mode good 13 12.8 0.2002 -1.541 1.001 0.6005 1.001 1.401
variance good 13.99 14.28 2.184 2.042 -0.1308 6.553 10.92 15.29
quantile10 good 9.465 9.464 0.2002 -0.007516 0.003554 0.6005 1.001 1.401
quantile20 good 10.79 10.79 0.2002 -0.006084 0.003281 0.6005 1.001 1.401
quantile30 good 11.82 11.82 0.2002 -0.0083 0.004903 0.6005 1.001 1.401
quantile40 good 12.75 12.75 0.2002 5.621e-05 -3.582e-05 0.6005 1.001 1.401
quantile50 good 13.67 13.67 0.2002 0.004965 -0.003391 0.6005 1.001 1.401
quantile60 good 14.63 14.63 0.2002 0.004972 -0.003633 0.6005 1.001 1.401
quantile70 good 15.7 15.7 0.2002 0.01664 -0.01305 0.6005 1.001 1.401
quantile80 good 17.02 17.02 0.2002 0.0003471 -0.0002951 0.6005 1.001 1.401
quantile90 good 18.96 18.96 0.2002 0.003604 -0.003414 0.6005 1.001 1.401

Subtest Description
correlation par 0 Calculate the auto-correlation among the points.
chi2 Calculate χ2 and compare with prediction for dof=number of bins with an expectation >= 10.
Tolerance good: |χ2-E[χ2]| < 3 · (2 dof)1/2,
Tolerance acceptable: |χ2-E[χ2]| < 5 · (2 dof)1/2,
Tolerance bad: |χ2-E[χ2]| < 7 · (2 dof)1/2.
KS Calculate the Kolmogorov-Smirnov probability based on the ROOT implemention.
Tolerance good: KS prob > 0.05,
Tolerance acceptable: KS prob > 0.01
Tolerance bad: KS prob > 0.0001.
mean Compare sample mean, <x>, with expectation value of function, E[x].
Tolerance good: |<x> -E[x]| < 3 · (V[x]/n)1/2,
Tolerance acceptable: |<x> -E[x]| < 5 · (V[x]/n)1/2,
Tolerance bad: |<x> -E[x]| < 7 · (V[x]/n)1/2.
mode Compare mode of distribution with mode of the analytic function.
Tolerance good: |x*-mode| < 3 · V[mode]1/2,
Tolerance acceptable: |x*-mode| < 5 · V[mode]1/2 bin widths,
Tolerance bad: |x*-mode| < 7 · V[mode]1/2.
variance Compare sample variance s2 of distribution with variance of function.
Tolerance good: 3 · V[s2]1/2,
Tolerance acceptable: 5 · V[s2]1/2,
Tolerance bad: 7 · V[s2]1/2.
quantile10 Compare quantile of distribution from MCMC with the quantile of analytic function.
Tolerance good: |q_{X}-E[q_{X}]|<3·V[q]1/2,
Tolerance acceptable: |q_{X}-E[q_{X}]|<5·V[q]1/2,
Tolerance bad: |q_{X}-E[q_{X}]|<7·V[q]1/2.
quantile20 Compare quantile of distribution from MCMC with the quantile of analytic function.
Tolerance good: |q_{X}-E[q_{X}]|<3·V[q]1/2,
Tolerance acceptable: |q_{X}-E[q_{X}]|<5·V[q]1/2,
Tolerance bad: |q_{X}-E[q_{X}]|<7·V[q]1/2.
quantile30 Compare quantile of distribution from MCMC with the quantile of analytic function.
Tolerance good: |q_{X}-E[q_{X}]|<3·V[q]1/2,
Tolerance acceptable: |q_{X}-E[q_{X}]|<5·V[q]1/2,
Tolerance bad: |q_{X}-E[q_{X}]|<7·V[q]1/2.
quantile40 Compare quantile of distribution from MCMC with the quantile of analytic function.
Tolerance good: |q_{X}-E[q_{X}]|<3·V[q]1/2,
Tolerance acceptable: |q_{X}-E[q_{X}]|<5·V[q]1/2,
Tolerance bad: |q_{X}-E[q_{X}]|<7·V[q]1/2.
quantile50 Compare quantile of distribution from MCMC with the quantile of analytic function.
Tolerance good: |q_{X}-E[q_{X}]|<3·V[q]1/2,
Tolerance acceptable: |q_{X}-E[q_{X}]|<5·V[q]1/2,
Tolerance bad: |q_{X}-E[q_{X}]|<7·V[q]1/2.
quantile60 Compare quantile of distribution from MCMC with the quantile of analytic function.
Tolerance good: |q_{X}-E[q_{X}]|<3·V[q]1/2,
Tolerance acceptable: |q_{X}-E[q_{X}]|<5·V[q]1/2,
Tolerance bad: |q_{X}-E[q_{X}]|<7·V[q]1/2.
quantile70 Compare quantile of distribution from MCMC with the quantile of analytic function.
Tolerance good: |q_{X}-E[q_{X}]|<3·V[q]1/2,
Tolerance acceptable: |q_{X}-E[q_{X}]|<5·V[q]1/2,
Tolerance bad: |q_{X}-E[q_{X}]|<7·V[q]1/2.
quantile80 Compare quantile of distribution from MCMC with the quantile of analytic function.
Tolerance good: |q_{X}-E[q_{X}]|<3·V[q]1/2,
Tolerance acceptable: |q_{X}-E[q_{X}]|<5·V[q]1/2,
Tolerance bad: |q_{X}-E[q_{X}]|<7·V[q]1/2.
quantile90 Compare quantile of distribution from MCMC with the quantile of analytic function.
Tolerance good: |q_{X}-E[q_{X}]|<3·V[q]1/2,
Tolerance acceptable: |q_{X}-E[q_{X}]|<5·V[q]1/2,
Tolerance bad: |q_{X}-E[q_{X}]|<7·V[q]1/2.