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_11"
Results | |
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Status | good |
CPU time | 69.26 s |
Real time | 69.35 s |
Plots | 1d_poisson_11.ps |
Log | 1d_poisson_11.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 | 0.1203 | 0.01192 | - | -10.1 | 0.3 | 0.5 | 0.7 |
chi2 | good | 95 | 102.9 | 13.78 | 8.34 | -0.5748 | 41.35 | 68.92 | 96.49 |
KS | good | 1 | 0.9899 | 0.95 | -1.012 | 0.01065 | 0.95 | 0.99 | 0.9999 |
mean | good | 12 | 12 | 0.001086 | 0.00167 | -0.1845 | 0.003258 | 0.005431 | 0.007603 |
mode | good | 11 | 11.11 | 0.192 | 1.006 | -0.5766 | 0.5759 | 0.9598 | 1.344 |
variance | good | 12 | 12.24 | 1.9 | 1.997 | -0.1261 | 5.7 | 9.499 | 13.3 |
quantile10 | good | 7.824 | 7.827 | 0.192 | 0.03536 | -0.01441 | 0.5759 | 0.9598 | 1.344 |
quantile20 | good | 9.029 | 9.029 | 0.192 | 0.004861 | -0.002286 | 0.5759 | 0.9598 | 1.344 |
quantile30 | good | 9.971 | 9.97 | 0.192 | -0.01452 | 0.007541 | 0.5759 | 0.9598 | 1.344 |
quantile40 | good | 10.83 | 10.83 | 0.192 | -0.006322 | 0.003565 | 0.5759 | 0.9598 | 1.344 |
quantile50 | good | 11.67 | 11.67 | 0.192 | 0.0004271 | -0.0002596 | 0.5759 | 0.9598 | 1.344 |
quantile60 | good | 12.55 | 12.55 | 0.192 | 0.002262 | -0.001479 | 0.5759 | 0.9598 | 1.344 |
quantile70 | good | 13.55 | 13.55 | 0.192 | 0.0005467 | -0.0003859 | 0.5759 | 0.9598 | 1.344 |
quantile80 | good | 14.78 | 14.78 | 0.192 | -0.009299 | 0.00716 | 0.5759 | 0.9598 | 1.344 |
quantile90 | good | 16.6 | 16.6 | 0.192 | -0.0001531 | 0.0001324 | 0.5759 | 0.9598 | 1.344 |
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. |