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_13"
Results | |
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Status | good |
CPU time | 59.66 s |
Real time | 59.92 s |
Plots | 1d_poisson_13.ps |
Log | 1d_poisson_13.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 | 0.118 | 0.01173 | - | -10.07 | 0.3 | 0.5 | 0.7 |
chi2 | good | 93 | 115.4 | 13.64 | 24.05 | -1.64 | 40.91 | 68.19 | 95.47 |
KS | good | 1 | 1 | 0.95 | -0.0005863 | 6.171e-06 | 0.95 | 0.99 | 0.9999 |
mean | good | 14 | 14 | 0.001188 | -0.002529 | 0.2979 | 0.003565 | 0.005941 | 0.008318 |
mode | good | 13 | 13.16 | 0.2002 | 1.233 | -0.8007 | 0.6005 | 1.001 | 1.401 |
variance | good | 13.99 | 14.27 | 2.184 | 1.988 | -0.1274 | 6.553 | 10.92 | 15.29 |
quantile10 | good | 9.465 | 9.465 | 0.2002 | -0.0009694 | 0.0004584 | 0.6005 | 1.001 | 1.401 |
quantile20 | good | 10.79 | 10.79 | 0.2002 | -0.001265 | 0.000682 | 0.6005 | 1.001 | 1.401 |
quantile30 | good | 11.82 | 11.82 | 0.2002 | -0.000893 | 0.0005275 | 0.6005 | 1.001 | 1.401 |
quantile40 | good | 12.75 | 12.75 | 0.2002 | 0.001628 | -0.001037 | 0.6005 | 1.001 | 1.401 |
quantile50 | good | 13.67 | 13.67 | 0.2002 | -0.004942 | 0.003375 | 0.6005 | 1.001 | 1.401 |
quantile60 | good | 14.63 | 14.62 | 0.2002 | -0.00725 | 0.005298 | 0.6005 | 1.001 | 1.401 |
quantile70 | good | 15.7 | 15.7 | 0.2002 | 0.007293 | -0.00572 | 0.6005 | 1.001 | 1.401 |
quantile80 | good | 17.02 | 17.02 | 0.2002 | 0.003046 | -0.002589 | 0.6005 | 1.001 | 1.401 |
quantile90 | good | 18.96 | 18.96 | 0.2002 | -0.0108 | 0.01023 | 0.6005 | 1.001 | 1.401 |
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. |