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 0.9.4
Back to | overview for 0.9.4 | all versions |
Test "1d_squared"
Results | |
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Status | good |
CPU time | 11.13 s |
Real time | 11.14 s |
Plots | 1d_squared.pdf |
Log | 1d_squared.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 | 0.04122 | 0.004141 | - | -9.954 | 0.3 | 0.5 | 0.7 |
chi2 | good | 100 | 107.3 | 14.14 | 7.276 | -0.5145 | 42.43 | 70.71 | 98.99 |
KS | good | 1 | 0.9992 | 0.95 | -0.08462 | 0.0008907 | 0.95 | 0.99 | 0.9999 |
mean | good | 0 | 0.0004939 | 0.002861 | - | -0.1727 | 0.008582 | 0.0143 | 0.02002 |
mode | good | 1.602e-07 | 0.6 | 0.5418 | 3.746e+08 | -1.107 | 1.625 | 2.709 | 3.793 |
variance | good | 80 | 81.63 | 8.628 | 2.038 | -0.189 | 25.88 | 43.14 | 60.39 |
quantile10 | good | -12.17 | -12.17 | 0.2108 | -0.007136 | -0.00412 | 0.6325 | 1.054 | 1.476 |
quantile20 | good | -8.515 | -8.513 | 0.2108 | -0.02849 | -0.01151 | 0.6325 | 1.054 | 1.476 |
quantile30 | good | -5.47 | -5.469 | 0.2108 | -0.02461 | -0.006386 | 0.6325 | 1.054 | 1.476 |
quantile40 | good | -2.683 | -2.681 | 0.2108 | -0.07999 | -0.01018 | 0.6325 | 1.054 | 1.476 |
quantile50 | good | 1.036e-14 | -9.069e-05 | 0.2108 | -8.751e+11 | 0.0004302 | 0.6325 | 1.054 | 1.476 |
quantile60 | good | 2.683 | 2.683 | 0.2108 | 0.003112 | -0.000396 | 0.6325 | 1.054 | 1.476 |
quantile70 | good | 5.47 | 5.467 | 0.2108 | -0.05216 | 0.01354 | 0.6325 | 1.054 | 1.476 |
quantile80 | good | 8.515 | 8.516 | 0.2108 | 0.004725 | -0.001908 | 0.6325 | 1.054 | 1.476 |
quantile90 | good | 12.17 | 12.17 | 0.2108 | 0.02692 | -0.01554 | 0.6325 | 1.054 | 1.476 |
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. |