This C++ version of BAT is still being maintained, but addition of new features is unlikely. Check out our new incarnation, BAT.jl, the Bayesian analysis toolkit in Julia. In addition to Metropolis-Hastings sampling, BAT.jl supports Hamiltonian Monte Carlo (HMC) with automatic differentiation, automatic prior-based parameter space transformations, and much more. See the BAT.jl documentation.
Results of performance testing for BAT version 0.9
Back to | overview for 0.9 | all versions |
Test "1d_binomial_1_7"
Results | |
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Status | good |
CPU time | 124.3 s |
Real time | 124.6 s |
Plots | 1d_binomial_1_7.ps |
Log | 1d_binomial_1_7.log |
Settings | |
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N chains | 10 |
N lag | 10 |
Convergence | true |
N iterations (pre-run) | 1000 |
N iterations (run) | 10000000 |
Subtest | Status | Target | Test | Uncertainty | Deviation [%] | Deviation [sigma] | Tol. (Good) | Tol. (Acceptable) | Tol. (Bad) |
---|---|---|---|---|---|---|---|---|---|
correlation par 0 | off | 0 | 0.1297 | 0.01286 | - | -10.09 | 0.3 | 0.5 | 0.7 |
chi2 | good | 89 | 95.43 | 13.34 | 7.227 | -0.4821 | 40.02 | 66.71 | 93.39 |
KS | good | 1 | 0.9257 | 0.95 | -7.433 | 0.07824 | 0.95 | 0.99 | 0.9999 |
mean | good | 0.2222 | 0.2222 | 4.134e-05 | -0.01929 | 1.037 | 0.000124 | 0.0002067 | 0.0002894 |
mode | good | 0.1429 | 0.135 | 0.03333 | -5.5 | 0.2357 | 0.1 | 0.1667 | 0.2333 |
variance | good | 0.01728 | 0.01764 | 0.002639 | 2.061 | -0.135 | 0.007918 | 0.0132 | 0.01848 |
quantile10 | good | 0.06857 | 0.06848 | 0.03333 | -0.1418 | 0.002917 | 0.1 | 0.1667 | 0.2333 |
quantile20 | good | 0.1043 | 0.1043 | 0.03333 | -0.06933 | 0.00217 | 0.1 | 0.1667 | 0.2333 |
quantile30 | good | 0.1365 | 0.1364 | 0.03333 | -0.04441 | 0.001819 | 0.1 | 0.1667 | 0.2333 |
quantile40 | good | 0.1682 | 0.1681 | 0.03333 | -0.02751 | 0.001388 | 0.1 | 0.1667 | 0.2333 |
quantile50 | good | 0.2011 | 0.2011 | 0.03333 | -0.01921 | 0.001159 | 0.1 | 0.1667 | 0.2333 |
quantile60 | good | 0.2372 | 0.2372 | 0.03333 | -0.0008057 | 5.732e-05 | 0.1 | 0.1667 | 0.2333 |
quantile70 | good | 0.2786 | 0.2785 | 0.03333 | -0.03012 | 0.002517 | 0.1 | 0.1667 | 0.2333 |
quantile80 | good | 0.3304 | 0.3303 | 0.03333 | -0.02984 | 0.002958 | 0.1 | 0.1667 | 0.2333 |
quantile90 | good | 0.4063 | 0.4064 | 0.03333 | 0.005407 | -0.0006591 | 0.1 | 0.1667 | 0.2333 |
Subtest | Description |
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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. |