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 0.4.2
Back to | overview for 0.4.2 | all versions |
Test "1d_poisson_18"
Results | |
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Status | good |
CPU time | 73.11 s |
Real time | 73.3 s |
Plots | 1d_poisson_18.ps |
Log | 1d_poisson_18.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 | 0.1143 | 0.01139 | - | -10.03 | 0.3 | 0.5 | 0.7 |
chi2 | good | 89 | 78.44 | 13.34 | -11.87 | 0.7918 | 40.02 | 66.71 | 93.39 |
KS | good | 1 | 0.746 | 0.95 | -25.4 | 0.2674 | 0.95 | 0.99 | 0.9999 |
mean | good | 19 | 19 | 0.001389 | 0.008321 | -1.139 | 0.004166 | 0.006943 | 0.00972 |
mode | good | 18 | 18.03 | 0.2171 | 0.1735 | -0.1438 | 0.6514 | 1.086 | 1.52 |
variance | good | 18.99 | 19.39 | 2.891 | 2.096 | -0.1376 | 8.673 | 14.46 | 20.24 |
quantile10 | good | 13.67 | 13.67 | 0.2171 | -0.01143 | 0.007194 | 0.6514 | 1.086 | 1.52 |
quantile20 | good | 15.27 | 15.27 | 0.2171 | 0.008225 | -0.005784 | 0.6514 | 1.086 | 1.52 |
quantile30 | good | 16.49 | 16.5 | 0.2171 | 0.0121 | -0.009196 | 0.6514 | 1.086 | 1.52 |
quantile40 | good | 17.6 | 17.6 | 0.2171 | 0.01566 | -0.01269 | 0.6514 | 1.086 | 1.52 |
quantile50 | good | 18.67 | 18.67 | 0.2171 | 0.009313 | -0.008007 | 0.6514 | 1.086 | 1.52 |
quantile60 | good | 19.78 | 19.79 | 0.2171 | 0.01269 | -0.01156 | 0.6514 | 1.086 | 1.52 |
quantile70 | good | 21.03 | 21.03 | 0.2171 | 0.007708 | -0.007465 | 0.6514 | 1.086 | 1.52 |
quantile80 | good | 22.54 | 22.54 | 0.2171 | 0.009277 | -0.009631 | 0.6514 | 1.086 | 1.52 |
quantile90 | good | 24.76 | 24.76 | 0.2171 | 0.01275 | -0.01454 | 0.6514 | 1.086 | 1.52 |
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. |