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 0.4.1
Back to | overview for 0.4.1 | all versions |
Test "1d_poisson_14"
Results | |
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Status | acceptable |
CPU time | 71.12 s |
Real time | 71.29 s |
Plots | 1d_poisson_14.ps |
Log | 1d_poisson_14.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. (Flawed) | Tol. (Bad) |
---|---|---|---|---|---|---|---|---|---|
correlation par 0 | off | 0 | 0.1172 | 0.01158 | - | -10.12 | 0.3 | 0.5 | 0.7 |
chi2 | acceptable | 92 | 134.8 | 13.56 | 46.55 | -3.157 | 40.69 | 67.82 | 94.95 |
KS | good | 1 | 0.9666 | 0.95 | -3.341 | 0.03517 | 0.95 | 0.99 | 0.9999 |
mean | good | 15 | 15 | 0.001227 | -0.0041 | 0.5013 | 0.003681 | 0.006135 | 0.008589 |
mode | good | 14 | 14.03 | 0.2039 | 0.223 | -0.1531 | 0.6117 | 1.019 | 1.427 |
variance | good | 14.99 | 15.28 | 2.326 | 1.922 | -0.1239 | 6.979 | 11.63 | 16.28 |
quantile10 | good | 10.29 | 10.29 | 0.2039 | -0.00236 | 0.001192 | 0.6117 | 1.019 | 1.427 |
quantile20 | good | 11.68 | 11.68 | 0.2039 | 0.01485 | -0.008509 | 0.6117 | 1.019 | 1.427 |
quantile30 | good | 12.75 | 12.75 | 0.2039 | 0.006099 | -0.003815 | 0.6117 | 1.019 | 1.427 |
quantile40 | good | 13.72 | 13.72 | 0.2039 | -0.003172 | 0.002135 | 0.6117 | 1.019 | 1.427 |
quantile50 | good | 14.67 | 14.67 | 0.2039 | -0.005591 | 0.004022 | 0.6117 | 1.019 | 1.427 |
quantile60 | good | 15.66 | 15.66 | 0.2039 | -0.008842 | 0.00679 | 0.6117 | 1.019 | 1.427 |
quantile70 | good | 16.77 | 16.77 | 0.2039 | -0.004397 | 0.003615 | 0.6117 | 1.019 | 1.427 |
quantile80 | good | 18.13 | 18.13 | 0.2039 | -0.007131 | 0.00634 | 0.6117 | 1.019 | 1.427 |
quantile90 | good | 20.13 | 20.13 | 0.2039 | 0.004657 | -0.004598 | 0.6117 | 1.019 | 1.427 |
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. |