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_gaus"
Results | |
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Status | good |
CPU time | 14.77 s |
Real time | 14.78 s |
Plots | 1d_gaus.pdf |
Log | 1d_gaus.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 | 0.1058 | 0.0105 | - | -10.07 | 0.3 | 0.5 | 0.7 |
chi2 | good | 92 | 116.7 | 13.56 | 26.82 | -1.819 | 40.69 | 67.82 | 94.95 |
KS | good | 1 | 0.7805 | 0.95 | -21.95 | 0.2311 | 0.95 | 0.99 | 0.9999 |
mean | good | 0 | -0.0007827 | 0.001585 | - | 0.4938 | 0.004755 | 0.007925 | 0.01109 |
mode | good | -9.937e-09 | -0.25 | 0.2357 | 2.516e+09 | 1.061 | 0.7071 | 1.179 | 1.65 |
variance | good | 25 | 25.52 | 3.553 | 2.099 | -0.1477 | 10.66 | 17.77 | 24.87 |
quantile10 | good | -6.412 | -6.415 | 0.2357 | 0.03516 | 0.009566 | 0.7071 | 1.179 | 1.65 |
quantile20 | good | -4.213 | -4.216 | 0.2357 | 0.05739 | 0.01026 | 0.7071 | 1.179 | 1.65 |
quantile30 | good | -2.625 | -2.626 | 0.2357 | 0.06903 | 0.007686 | 0.7071 | 1.179 | 1.65 |
quantile40 | good | -1.268 | -1.269 | 0.2357 | 0.01867 | 0.001005 | 0.7071 | 1.179 | 1.65 |
quantile50 | good | 3.484e-15 | -0.001086 | 0.2357 | -3.116e+13 | 0.004607 | 0.7071 | 1.179 | 1.65 |
quantile60 | good | 1.268 | 1.27 | 0.2357 | 0.1474 | -0.007934 | 0.7071 | 1.179 | 1.65 |
quantile70 | good | 2.625 | 2.626 | 0.2357 | 0.04761 | -0.005301 | 0.7071 | 1.179 | 1.65 |
quantile80 | good | 4.213 | 4.214 | 0.2357 | 0.01361 | -0.002433 | 0.7071 | 1.179 | 1.65 |
quantile90 | good | 6.412 | 6.409 | 0.2357 | -0.05153 | 0.01402 | 0.7071 | 1.179 | 1.65 |
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. |