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_binomial_0_2"
Results | |
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Status | good |
CPU time | 292.6 s |
Real time | 295.9 s |
Plots | 1d_binomial_0_2.ps |
Log | 1d_binomial_0_2.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 | 0.1597 | 0.01582 | - | -10.09 | 0.3 | 0.5 | 0.7 |
chi2 | good | 100 | 126.6 | 14.14 | 26.58 | -1.879 | 42.43 | 70.71 | 98.99 |
KS | good | 1 | 0.9293 | 0.95 | -7.068 | 0.0744 | 0.95 | 0.99 | 0.9999 |
mean | good | 0.25 | 0.25 | 6.19e-05 | 0.004295 | -0.1735 | 0.0001857 | 0.0003095 | 0.0004333 |
mode | good | 0 | 0.005 | 0.03333 | - | -0.15 | 0.1 | 0.1667 | 0.2333 |
variance | good | 0.0375 | 0.03829 | 0.005454 | 2.096 | -0.1441 | 0.01636 | 0.02727 | 0.03818 |
quantile10 | good | 0.03454 | 0.03455 | 0.03333 | 0.04363 | -0.0004521 | 0.1 | 0.1667 | 0.2333 |
quantile20 | good | 0.0717 | 0.07173 | 0.03333 | 0.0495 | -0.001065 | 0.1 | 0.1667 | 0.2333 |
quantile30 | good | 0.1121 | 0.1121 | 0.03333 | 3.847e-05 | -1.294e-06 | 0.1 | 0.1667 | 0.2333 |
quantile40 | good | 0.1566 | 0.1566 | 0.03333 | -0.01377 | 0.000647 | 0.1 | 0.1667 | 0.2333 |
quantile50 | good | 0.2063 | 0.2063 | 0.03333 | -0.03436 | 0.002127 | 0.1 | 0.1667 | 0.2333 |
quantile60 | good | 0.2632 | 0.2631 | 0.03333 | -0.04933 | 0.003895 | 0.1 | 0.1667 | 0.2333 |
quantile70 | good | 0.3306 | 0.3304 | 0.03333 | -0.04824 | 0.004784 | 0.1 | 0.1667 | 0.2333 |
quantile80 | good | 0.4152 | 0.4153 | 0.03333 | 0.02338 | -0.002912 | 0.1 | 0.1667 | 0.2333 |
quantile90 | good | 0.5359 | 0.5361 | 0.03333 | 0.04158 | -0.006685 | 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. |