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 0.4.2
Back to | overview for 0.4.2 | all versions |
Test "1d_binomial_1_9"
Results | |
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Status | good |
CPU time | 318.7 s |
Real time | 318.7 s |
Plots | 1d_binomial_1_9.ps |
Log | 1d_binomial_1_9.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 | 0.169 | 0.01673 | - | -10.11 | 0.3 | 0.5 | 0.7 |
chi2 | good | 82 | 106.5 | 12.81 | 29.84 | -1.911 | 38.42 | 64.03 | 89.64 |
KS | good | 1 | 0.9691 | 0.95 | -3.092 | 0.03255 | 0.95 | 0.99 | 0.9999 |
mean | good | 0.1818 | 0.1818 | 3.576e-05 | 0.01113 | -0.566 | 0.0001073 | 0.0001788 | 0.0002503 |
mode | good | 0.1111 | 0.115 | 0.03333 | 3.5 | -0.1167 | 0.1 | 0.1667 | 0.2333 |
variance | good | 0.0124 | 0.01266 | 0.002025 | 2.152 | -0.1317 | 0.006075 | 0.01013 | 0.01418 |
quantile10 | good | 0.05441 | 0.05442 | 0.03333 | 0.01632 | -0.0002664 | 0.1 | 0.1667 | 0.2333 |
quantile20 | good | 0.08323 | 0.08325 | 0.03333 | 0.02314 | -0.0005777 | 0.1 | 0.1667 | 0.2333 |
quantile30 | good | 0.1093 | 0.1093 | 0.03333 | -0.006661 | 0.0002184 | 0.1 | 0.1667 | 0.2333 |
quantile40 | good | 0.1352 | 0.1351 | 0.03333 | -0.01839 | 0.0007458 | 0.1 | 0.1667 | 0.2333 |
quantile50 | good | 0.1623 | 0.1623 | 0.03333 | -0.008534 | 0.0004155 | 0.1 | 0.1667 | 0.2333 |
quantile60 | good | 0.1922 | 0.1921 | 0.03333 | -0.03308 | 0.001907 | 0.1 | 0.1667 | 0.2333 |
quantile70 | good | 0.227 | 0.227 | 0.03333 | -0.01442 | 0.0009822 | 0.1 | 0.1667 | 0.2333 |
quantile80 | good | 0.271 | 0.2711 | 0.03333 | 0.01168 | -0.0009499 | 0.1 | 0.1667 | 0.2333 |
quantile90 | good | 0.3369 | 0.337 | 0.03333 | 0.018 | -0.001819 | 0.1 | 0.1667 | 0.2333 |
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. |