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_6"
| Results | |
|---|---|
| Status | good |
| CPU time | 54.84 s |
| Real time | 54.87 s |
| Plots | 1d_poisson_6.ps |
| Log | 1d_poisson_6.log |
| Settings | |
|---|---|
| N chains | 10 |
| N lag | 10 |
| Convergence | true |
| N iterations (pre-run) | 1000 |
| N iterations (run) | 10000000 |
 |  |  |
| Auto-correlation for the parameter. | Distribution from MCMC and analytic function. | Distribution from MCMC and analytic function in log-scale. |
 |  |  |
| Difference between the distribution from MCMC and the analytic function. The one, two and three sigma uncertainty bands are colored green, yellow and red, respectively. | Pull between the distribution from MCMC and the analytic function. The Gaussian has a mean value of 0 and a standard deviation of 1 (not fitted). | Summary of subtest values. |
| Subtest | Status | Target | Test | Uncertainty | Deviation [%] | Deviation [sigma] | Tol. (Good) | Tol. (Acceptable) | Tol. (Bad) |
|---|---|---|---|---|---|---|---|---|---|
| correlation par 0 | off | 0 | 0.1297 | 0.01284 | - | -10.11 | 0.3 | 0.5 | 0.7 |
| chi2 | good | 98 | 96.43 | 14 | -1.598 | 0.1119 | 42 | 70 | 98 |
| KS | good | 1 | 0.821 | 0.95 | -17.9 | 0.1885 | 0.95 | 0.99 | 0.9999 |
| mean | good | 7 | 7.001 | 0.0008478 | 0.01812 | -1.496 | 0.002543 | 0.004239 | 0.005934 |
| mode | good | 6 | 6.001 | 0.165 | 0.02083 | -0.007576 | 0.4949 | 0.8249 | 1.155 |
| variance | good | 6.997 | 7.144 | 1.183 | 2.107 | -0.1246 | 3.548 | 5.914 | 8.279 |
| quantile10 | good | 3.893 | 3.894 | 0.165 | 0.008125 | -0.001917 | 0.4949 | 0.8249 | 1.155 |
| quantile20 | good | 4.732 | 4.732 | 0.165 | 0.004337 | -0.001244 | 0.4949 | 0.8249 | 1.155 |
| quantile30 | good | 5.41 | 5.412 | 0.165 | 0.02772 | -0.009091 | 0.4949 | 0.8249 | 1.155 |
| quantile40 | good | 6.039 | 6.041 | 0.165 | 0.02849 | -0.01043 | 0.4949 | 0.8249 | 1.155 |
| quantile50 | good | 6.67 | 6.672 | 0.165 | 0.0224 | -0.009055 | 0.4949 | 0.8249 | 1.155 |
| quantile60 | good | 7.343 | 7.345 | 0.165 | 0.02659 | -0.01183 | 0.4949 | 0.8249 | 1.155 |
| quantile70 | good | 8.112 | 8.113 | 0.165 | 0.01242 | -0.006109 | 0.4949 | 0.8249 | 1.155 |
| quantile80 | good | 9.076 | 9.077 | 0.165 | 0.01007 | -0.00554 | 0.4949 | 0.8249 | 1.155 |
| quantile90 | good | 10.53 | 10.53 | 0.165 | 0.01854 | -0.01183 | 0.4949 | 0.8249 | 1.155 |
| Subtest | Description |
|---|---|
| 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. |