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_5_9"
Results | |
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Status | good |
CPU time | 359.9 s |
Real time | 360.6 s |
Plots | 1d_binomial_5_9.ps |
Log | 1d_binomial_5_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 | 0.06509 | 0.006527 | - | -9.972 | 0.3 | 0.5 | 0.7 |
chi2 | good | 94 | 101.7 | 13.71 | 8.235 | -0.5646 | 41.13 | 68.56 | 95.98 |
KS | good | 1 | 0.647 | 0.95 | -35.3 | 0.3716 | 0.95 | 0.99 | 0.9999 |
mean | good | 0.5455 | 0.5454 | 4.567e-05 | -0.002184 | 0.2608 | 0.000137 | 0.0002284 | 0.0003197 |
mode | good | 0.5556 | 0.555 | 0.03333 | -0.1 | 0.01667 | 0.1 | 0.1667 | 0.2333 |
variance | good | 0.02066 | 0.02109 | 0.002617 | 2.089 | -0.1649 | 0.007852 | 0.01309 | 0.01832 |
quantile10 | good | 0.3541 | 0.3541 | 0.03333 | -0.006222 | 0.000661 | 0.1 | 0.1667 | 0.2333 |
quantile20 | good | 0.4191 | 0.4191 | 0.03333 | 0.00301 | -0.0003784 | 0.1 | 0.1667 | 0.2333 |
quantile30 | good | 0.4675 | 0.4674 | 0.03333 | -0.00982 | 0.001377 | 0.1 | 0.1667 | 0.2333 |
quantile40 | good | 0.5093 | 0.5092 | 0.03333 | -0.01949 | 0.002978 | 0.1 | 0.1667 | 0.2333 |
quantile50 | good | 0.5483 | 0.5482 | 0.03333 | -0.01522 | 0.002503 | 0.1 | 0.1667 | 0.2333 |
quantile60 | good | 0.5869 | 0.5869 | 0.03333 | -0.008607 | 0.001516 | 0.1 | 0.1667 | 0.2333 |
quantile70 | good | 0.6274 | 0.6275 | 0.03333 | 0.001288 | -0.0002424 | 0.1 | 0.1667 | 0.2333 |
quantile80 | good | 0.6732 | 0.6733 | 0.03333 | 0.004128 | -0.0008338 | 0.1 | 0.1667 | 0.2333 |
quantile90 | good | 0.7328 | 0.7328 | 0.03333 | 0.006053 | -0.001331 | 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. |