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_16"
Results | |
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Status | good |
CPU time | 71.33 s |
Real time | 71.96 s |
Plots | 1d_poisson_16.ps |
Log | 1d_poisson_16.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 | 0.1153 | 0.01143 | - | -10.08 | 0.3 | 0.5 | 0.7 |
chi2 | good | 90 | 114.9 | 13.42 | 27.72 | -1.859 | 40.25 | 67.08 | 93.91 |
KS | good | 1 | 0.5956 | 0.95 | -40.44 | 0.4257 | 0.95 | 0.99 | 0.9999 |
mean | good | 17 | 17 | 0.001299 | 0.006437 | -0.8425 | 0.003896 | 0.006494 | 0.009092 |
mode | good | 16 | 16.2 | 0.2108 | 1.25 | -0.9487 | 0.6325 | 1.054 | 1.476 |
variance | good | 16.99 | 17.35 | 2.609 | 2.114 | -0.1377 | 7.828 | 13.05 | 18.27 |
quantile10 | good | 11.97 | 11.97 | 0.2108 | -0.02405 | 0.01366 | 0.6325 | 1.054 | 1.476 |
quantile20 | good | 13.47 | 13.46 | 0.2108 | -0.007673 | 0.004901 | 0.6325 | 1.054 | 1.476 |
quantile30 | good | 14.62 | 14.62 | 0.2108 | 0.01372 | -0.009511 | 0.6325 | 1.054 | 1.476 |
quantile40 | good | 15.66 | 15.66 | 0.2108 | 0.01874 | -0.01392 | 0.6325 | 1.054 | 1.476 |
quantile50 | good | 16.67 | 16.67 | 0.2108 | 0.01308 | -0.01034 | 0.6325 | 1.054 | 1.476 |
quantile60 | good | 17.72 | 17.72 | 0.2108 | 0.00702 | -0.005902 | 0.6325 | 1.054 | 1.476 |
quantile70 | good | 18.9 | 18.9 | 0.2108 | 0.005815 | -0.005213 | 0.6325 | 1.054 | 1.476 |
quantile80 | good | 20.34 | 20.34 | 0.2108 | -0.00235 | 0.002267 | 0.6325 | 1.054 | 1.476 |
quantile90 | good | 22.45 | 22.46 | 0.2108 | 0.009057 | -0.009646 | 0.6325 | 1.054 | 1.476 |
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. |