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_2gaus"
Results | |
---|---|
Status | acceptable |
CPU time | 104.2 s |
Real time | 104.3 s |
Plots | 1d_2gaus.ps |
Log | 1d_2gaus.log |
Settings | |
---|---|
N chains | 10 |
N lag | 10 |
Convergence | true |
N iterations (pre-run) | 4000 |
N iterations (run) | 10000000 |
Subtest | Status | Target | Test | Uncertainty | Deviation [%] | Deviation [sigma] | Tol. (Good) | Tol. (acceptable) | Tol. (Bad) |
---|---|---|---|---|---|---|---|---|---|
correlation par 0 | off | 0 | 0.8922 | 0.08835 | - | -10.1 | 0.3 | 0.5 | 0.7 |
chi2 | good | 75 | 86.87 | 12.25 | 15.82 | -0.9689 | 36.74 | 61.24 | 85.73 |
KS | good | 1 | 0.08424 | 0.95 | -91.58 | 0.964 | 0.95 | 0.99 | 0.9999 |
mean | acceptable | 10 | 9.978 | 0.00626 | -0.2197 | 3.509 | 0.01878 | 0.0313 | 0.04382 |
mode | off | 30 | 29.94 | 0.2041 | -0.2083 | 0.3062 | 0.6124 | 1.021 | 1.429 |
variance | good | 402.5 | 406.6 | 5.314 | 1.007 | -0.7625 | 15.94 | 26.57 | 37.2 |
quantile10 | good | -11.69 | -11.69 | 0.2041 | 0.006337 | 0.003629 | 0.6124 | 1.021 | 1.429 |
quantile20 | good | -10.51 | -10.51 | 0.2041 | 0.0226 | 0.01163 | 0.6124 | 1.021 | 1.429 |
quantile30 | good | -9.491 | -9.496 | 0.2041 | 0.0506 | 0.02353 | 0.6124 | 1.021 | 1.429 |
quantile40 | good | -8.309 | -8.317 | 0.2041 | 0.09411 | 0.03831 | 0.6124 | 1.021 | 1.429 |
quantile50 | good | 22.31 | -3.846 | 14.61 | -117.2 | 1.79 | 43.83 | 73.04 | 102.3 |
quantile60 | good | 29.14 | 29.14 | 0.2041 | -0.01247 | 0.0178 | 0.6124 | 1.021 | 1.429 |
quantile70 | good | 29.75 | 29.74 | 0.2041 | -0.004926 | 0.007179 | 0.6124 | 1.021 | 1.429 |
quantile80 | good | 30.26 | 30.26 | 0.2041 | -0.005338 | 0.007913 | 0.6124 | 1.021 | 1.429 |
quantile90 | good | 30.85 | 30.84 | 0.2041 | -0.003461 | 0.005229 | 0.6124 | 1.021 | 1.429 |
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. |