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_binomial_7_8"
Results | |
---|---|
Status | good |
CPU time | 295.4 s |
Real time | 295.4 s |
Plots | 1d_binomial_7_8.ps |
Log | 1d_binomial_7_8.log |
Settings | |
---|---|
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 | 0.1481 | 0.01468 | - | -10.09 | 0.3 | 0.5 | 0.7 |
chi2 | good | 85 | 87.95 | 13.04 | 3.47 | -0.2262 | 39.12 | 65.19 | 91.27 |
KS | good | 1 | 0.8774 | 0.95 | -12.26 | 0.129 | 0.95 | 0.99 | 0.9999 |
mean | good | 0.8 | 0.7999 | 3.804e-05 | -0.006273 | 1.319 | 0.0001141 | 0.0001902 | 0.0002663 |
mode | good | 0.875 | 0.875 | 0.03333 | -1.204e-07 | 3.16e-08 | 0.1 | 0.1667 | 0.2333 |
variance | good | 0.01455 | 0.01485 | 0.002305 | 2.076 | -0.131 | 0.006914 | 0.01152 | 0.01613 |
quantile10 | good | 0.6316 | 0.6315 | 0.03333 | -0.01871 | 0.003546 | 0.1 | 0.1667 | 0.2333 |
quantile20 | good | 0.7022 | 0.7021 | 0.03333 | -0.01373 | 0.002892 | 0.1 | 0.1667 | 0.2333 |
quantile30 | good | 0.7499 | 0.7498 | 0.03333 | -0.008203 | 0.001845 | 0.1 | 0.1667 | 0.2333 |
quantile40 | good | 0.7877 | 0.7877 | 0.03333 | -0.0001701 | 4.019e-05 | 0.1 | 0.1667 | 0.2333 |
quantile50 | good | 0.8204 | 0.8203 | 0.03333 | -0.004003 | 0.0009851 | 0.1 | 0.1667 | 0.2333 |
quantile60 | good | 0.8501 | 0.8501 | 0.03333 | -0.004817 | 0.001228 | 0.1 | 0.1667 | 0.2333 |
quantile70 | good | 0.8786 | 0.8786 | 0.03333 | -0.003019 | 0.0007959 | 0.1 | 0.1667 | 0.2333 |
quantile80 | good | 0.9074 | 0.9074 | 0.03333 | -0.003323 | 0.0009046 | 0.1 | 0.1667 | 0.2333 |
quantile90 | good | 0.9393 | 0.9393 | 0.03333 | 0.0007625 | -0.0002149 | 0.1 | 0.1667 | 0.2333 |
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. |