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_7_9"
Results | |
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Status | good |
CPU time | 330.4 s |
Real time | 330.4 s |
Plots | 1d_binomial_7_9.ps |
Log | 1d_binomial_7_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 | 0.09953 | 0.009875 | - | -10.08 | 0.3 | 0.5 | 0.7 |
chi2 | good | 88 | 80.33 | 13.27 | -8.713 | 0.5779 | 39.8 | 66.33 | 92.87 |
KS | good | 1 | 0.9145 | 0.95 | -8.553 | 0.09003 | 0.95 | 0.99 | 0.9999 |
mean | good | 0.7273 | 0.7273 | 4.043e-05 | 0.0009796 | -0.1762 | 0.0001213 | 0.0002021 | 0.000283 |
mode | good | 0.7778 | 0.775 | 0.03333 | -0.3571 | 0.08333 | 0.1 | 0.1667 | 0.2333 |
variance | good | 0.01653 | 0.01686 | 0.00234 | 2.019 | -0.1426 | 0.007019 | 0.0117 | 0.01638 |
quantile10 | good | 0.5504 | 0.5505 | 0.03333 | 0.02112 | -0.003487 | 0.1 | 0.1667 | 0.2333 |
quantile20 | good | 0.619 | 0.6191 | 0.03333 | 0.004 | -0.0007429 | 0.1 | 0.1667 | 0.2333 |
quantile30 | good | 0.667 | 0.6669 | 0.03333 | -0.007062 | 0.001413 | 0.1 | 0.1667 | 0.2333 |
quantile40 | good | 0.7063 | 0.7063 | 0.03333 | -0.01016 | 0.002154 | 0.1 | 0.1667 | 0.2333 |
quantile50 | good | 0.7414 | 0.7414 | 0.03333 | 0.001335 | -0.0002969 | 0.1 | 0.1667 | 0.2333 |
quantile60 | good | 0.7745 | 0.7745 | 0.03333 | 0.0009038 | -0.00021 | 0.1 | 0.1667 | 0.2333 |
quantile70 | good | 0.8074 | 0.8074 | 0.03333 | 0.003744 | -0.0009069 | 0.1 | 0.1667 | 0.2333 |
quantile80 | good | 0.8424 | 0.8424 | 0.03333 | 0.00281 | -0.0007102 | 0.1 | 0.1667 | 0.2333 |
quantile90 | good | 0.8843 | 0.8843 | 0.03333 | 0.003525 | -0.0009351 | 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. |