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 0.4.3
Back to | overview for 0.4.3 | all versions |
Test "1d_poisson_20"
Results | |
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Status | good |
CPU time | 59.12 s |
Real time | 59.14 s |
Plots | 1d_poisson_20.ps |
Log | 1d_poisson_20.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 | 0.1141 | 0.01132 | - | -10.08 | 0.3 | 0.5 | 0.7 |
chi2 | good | 87 | 86.21 | 13.19 | -0.9038 | 0.05961 | 39.57 | 65.95 | 92.34 |
KS | good | 1 | 0.9934 | 0.95 | -0.6569 | 0.006915 | 0.95 | 0.99 | 0.9999 |
mean | good | 21 | 21 | 0.00144 | 0.007943 | -1.158 | 0.004321 | 0.007201 | 0.01008 |
mode | good | 20 | 19.9 | 0.2229 | -0.495 | 0.4441 | 0.6687 | 1.115 | 1.56 |
variance | good | 20.98 | 21.42 | 3.171 | 2.077 | -0.1374 | 9.514 | 15.86 | 22.2 |
quantile10 | good | 15.38 | 15.38 | 0.2229 | 0.002611 | -0.001801 | 0.6687 | 1.115 | 1.56 |
quantile20 | good | 17.08 | 17.08 | 0.2229 | 0.009538 | -0.007306 | 0.6687 | 1.115 | 1.56 |
quantile30 | good | 18.38 | 18.38 | 0.2229 | 0.004171 | -0.003438 | 0.6687 | 1.115 | 1.56 |
quantile40 | good | 19.54 | 19.54 | 0.2229 | 0.005849 | -0.005126 | 0.6687 | 1.115 | 1.56 |
quantile50 | good | 20.67 | 20.67 | 0.2229 | 0.01001 | -0.009279 | 0.6687 | 1.115 | 1.56 |
quantile60 | good | 21.84 | 21.84 | 0.2229 | 0.004424 | -0.004334 | 0.6687 | 1.115 | 1.56 |
quantile70 | good | 23.14 | 23.14 | 0.2229 | 0.004344 | -0.00451 | 0.6687 | 1.115 | 1.56 |
quantile80 | good | 24.73 | 24.73 | 0.2229 | 0.004535 | -0.005031 | 0.6687 | 1.115 | 1.56 |
quantile90 | good | 27.05 | 27.05 | 0.2229 | 0.009432 | -0.01145 | 0.6687 | 1.115 | 1.56 |
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. |