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_2"
Results | |
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Status | good |
CPU time | 52.6 s |
Real time | 52.75 s |
Plots | 1d_poisson_2.ps |
Log | 1d_poisson_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. (Acceptable) | Tol. (Bad) |
---|---|---|---|---|---|---|---|---|---|
correlation par 0 | off | 0 | 0.1704 | 0.01687 | - | -10.1 | 0.3 | 0.5 | 0.7 |
chi2 | good | 100 | 111.2 | 14.14 | 11.17 | -0.7901 | 42.43 | 70.71 | 98.99 |
KS | good | 1 | 0.9468 | 0.95 | -5.324 | 0.05604 | 0.95 | 0.99 | 0.9999 |
mean | good | 2.999 | 2.999 | 0.0005487 | -0.002843 | 0.1554 | 0.001646 | 0.002744 | 0.003841 |
mode | good | 2 | 2.025 | 0.1291 | 1.25 | -0.1936 | 0.3873 | 0.6455 | 0.9037 |
variance | good | 2.993 | 3.051 | 0.5917 | 1.932 | -0.09775 | 1.775 | 2.958 | 4.142 |
quantile10 | good | 1.1 | 1.101 | 0.1291 | 0.04989 | -0.004251 | 0.3873 | 0.6455 | 0.9037 |
quantile20 | good | 1.534 | 1.535 | 0.1291 | 0.01884 | -0.002239 | 0.3873 | 0.6455 | 0.9037 |
quantile30 | good | 1.914 | 1.914 | 0.1291 | 0.0049 | -0.0007263 | 0.3873 | 0.6455 | 0.9037 |
quantile40 | good | 2.285 | 2.286 | 0.1291 | 0.03351 | -0.005933 | 0.3873 | 0.6455 | 0.9037 |
quantile50 | good | 2.674 | 2.674 | 0.1291 | -0.002475 | 0.0005127 | 0.3873 | 0.6455 | 0.9037 |
quantile60 | good | 3.106 | 3.106 | 0.1291 | 0.0007715 | -0.0001856 | 0.3873 | 0.6455 | 0.9037 |
quantile70 | good | 3.616 | 3.616 | 0.1291 | 0.015 | -0.004201 | 0.3873 | 0.6455 | 0.9037 |
quantile80 | good | 4.28 | 4.28 | 0.1291 | 0.004407 | -0.001461 | 0.3873 | 0.6455 | 0.9037 |
quantile90 | good | 5.324 | 5.322 | 0.1291 | -0.02437 | 0.01005 | 0.3873 | 0.6455 | 0.9037 |
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. |