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_lognormal"
Results | |
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Status | good |
CPU time | 74.72 s |
Real time | 74.73 s |
Plots | 1d_lognormal.ps |
Log | 1d_lognormal.log |
Settings | |
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N chains | 10 |
N lag | 10 |
Convergence | true |
N iterations (pre-run) | 4000 |
N iterations (run) | 10000000 |
Subtest | Status | Target | Test | Uncertainty | Deviation [%] | Deviation [sigma] | Tol. (Good) | Tol. (Acceptable) | Tol. (Bad) |
---|---|---|---|---|---|---|---|---|---|
correlation par 0 | off | 0 | 0.2902 | 0.02873 | - | -10.1 | 0.3 | 0.5 | 0.7 |
chi2 | good | 100 | 118.2 | 14.14 | 18.18 | -1.286 | 42.43 | 70.71 | 98.99 |
KS | good | 1 | 0.9047 | 0.95 | -9.531 | 0.1003 | 0.95 | 0.99 | 0.9999 |
mean | good | 1.506 | 1.506 | 0.0004812 | -0.01939 | 0.6066 | 0.001444 | 0.002406 | 0.003368 |
mode | good | 0.3679 | 0.35 | 0.1054 | -4.86 | 0.1696 | 0.3162 | 0.527 | 0.7379 |
variance | good | 2.355 | 2.401 | 0.6633 | 1.983 | -0.0704 | 1.99 | 3.316 | 4.643 |
quantile10 | good | 0.2746 | 0.2747 | 0.1054 | 0.04555 | -0.001187 | 0.3162 | 0.527 | 0.7379 |
quantile20 | good | 0.4281 | 0.4281 | 0.1054 | 0.0003615 | -1.468e-05 | 0.3162 | 0.527 | 0.7379 |
quantile30 | good | 0.5869 | 0.5872 | 0.1054 | 0.05478 | -0.00305 | 0.3162 | 0.527 | 0.7379 |
quantile40 | good | 0.7687 | 0.7687 | 0.1054 | 0.005078 | -0.0003703 | 0.3162 | 0.527 | 0.7379 |
quantile50 | good | 0.9873 | 0.9872 | 0.1054 | -0.0129 | 0.001208 | 0.3162 | 0.527 | 0.7379 |
quantile60 | good | 1.268 | 1.268 | 0.1054 | -0.05628 | 0.006772 | 0.3162 | 0.527 | 0.7379 |
quantile70 | good | 1.655 | 1.654 | 0.1054 | -0.03485 | 0.005472 | 0.3162 | 0.527 | 0.7379 |
quantile80 | good | 2.252 | 2.252 | 0.1054 | -0.02786 | 0.005953 | 0.3162 | 0.527 | 0.7379 |
quantile90 | good | 3.417 | 3.416 | 0.1054 | -0.03009 | 0.009755 | 0.3162 | 0.527 | 0.7379 |
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. |