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_0"
Results | |
---|---|
Status | good |
CPU time | 68.86 s |
Real time | 69.01 s |
Plots | 1d_poisson_0.ps |
Log | 1d_poisson_0.log |
Settings | |
---|---|
N chains | 10 |
N lag | 10 |
Convergence | true |
N iterations (pre-run) | 7000 |
N iterations (run) | 10000000 |
Subtest | Status | Target | Test | Uncertainty | Deviation [%] | Deviation [sigma] | Tol. (Good) | Tol. (Flawed) | Tol. (Bad) |
---|---|---|---|---|---|---|---|---|---|
correlation par 0 | off | 0 | 0.2516 | 0.02495 | - | -10.08 | 0.3 | 0.5 | 0.7 |
chi2 | good | 79 | 106.8 | 12.57 | 35.18 | -2.211 | 37.71 | 62.85 | 87.99 |
KS | good | 1 | 0.9095 | 0.95 | -9.055 | 0.09531 | 0.95 | 0.99 | 0.9999 |
mean | good | 1 | 1 | 0.0003198 | 0.01615 | -0.5048 | 0.0009595 | 0.001599 | 0.002239 |
mode | good | 0 | 0.075 | 0.1291 | - | -0.5809 | 0.3873 | 0.6455 | 0.9037 |
variance | good | 0.9999 | 1.02 | 0.2829 | 1.992 | -0.07039 | 0.8488 | 1.415 | 1.98 |
quantile10 | good | 0.1077 | 0.1079 | 0.1291 | 0.181 | -0.00151 | 0.3873 | 0.6455 | 0.9037 |
quantile20 | good | 0.226 | 0.2262 | 0.1291 | 0.09978 | -0.001746 | 0.3873 | 0.6455 | 0.9037 |
quantile30 | good | 0.3593 | 0.3594 | 0.1291 | 0.02855 | -0.0007947 | 0.3873 | 0.6455 | 0.9037 |
quantile40 | good | 0.5135 | 0.5135 | 0.1291 | -0.008418 | 0.0003349 | 0.3873 | 0.6455 | 0.9037 |
quantile50 | good | 0.6958 | 0.6959 | 0.1291 | 0.01059 | -0.0005709 | 0.3873 | 0.6455 | 0.9037 |
quantile60 | good | 0.9174 | 0.9176 | 0.1291 | 0.02609 | -0.001854 | 0.3873 | 0.6455 | 0.9037 |
quantile70 | good | 1.204 | 1.205 | 0.1291 | 0.0633 | -0.005905 | 0.3873 | 0.6455 | 0.9037 |
quantile80 | good | 1.612 | 1.612 | 0.1291 | 0.01901 | -0.002373 | 0.3873 | 0.6455 | 0.9037 |
quantile90 | good | 2.305 | 2.305 | 0.1291 | -0.0101 | 0.001804 | 0.3873 | 0.6455 | 0.9037 |
Subtest | Description |
---|---|
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. |