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_1_5"
Results | |
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Status | good |
CPU time | 298.5 s |
Real time | 298.6 s |
Plots | 1d_binomial_1_5.ps |
Log | 1d_binomial_1_5.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. (Acceptable) | Tol. (Bad) |
---|---|---|---|---|---|---|---|---|---|
correlation par 0 | off | 0 | 0.08873 | 0.008786 | - | -10.1 | 0.3 | 0.5 | 0.7 |
chi2 | good | 96 | 103.7 | 13.86 | 8.068 | -0.559 | 41.57 | 69.28 | 96.99 |
KS | good | 1 | 0.2194 | 0.95 | -78.06 | 0.8217 | 0.95 | 0.99 | 0.9999 |
mean | good | 0.2857 | 0.2858 | 5.079e-05 | 0.04156 | -2.338 | 0.0001524 | 0.0002539 | 0.0003555 |
mode | good | 0.2 | 0.205 | 0.03333 | 2.5 | -0.15 | 0.1 | 0.1667 | 0.2333 |
variance | good | 0.02551 | 0.02603 | 0.003517 | 2.037 | -0.1478 | 0.01055 | 0.01758 | 0.02462 |
quantile10 | good | 0.09254 | 0.0926 | 0.03333 | 0.06385 | -0.001773 | 0.1 | 0.1667 | 0.2333 |
quantile20 | good | 0.1399 | 0.14 | 0.03333 | 0.08309 | -0.003487 | 0.1 | 0.1667 | 0.2333 |
quantile30 | good | 0.1818 | 0.1819 | 0.03333 | 0.04876 | -0.002659 | 0.1 | 0.1667 | 0.2333 |
quantile40 | good | 0.2226 | 0.2227 | 0.03333 | 0.05523 | -0.003688 | 0.1 | 0.1667 | 0.2333 |
quantile50 | good | 0.2645 | 0.2646 | 0.03333 | 0.05557 | -0.004409 | 0.1 | 0.1667 | 0.2333 |
quantile60 | good | 0.3095 | 0.3096 | 0.03333 | 0.03279 | -0.003044 | 0.1 | 0.1667 | 0.2333 |
quantile70 | good | 0.3604 | 0.3606 | 0.03333 | 0.06875 | -0.007432 | 0.1 | 0.1667 | 0.2333 |
quantile80 | good | 0.4225 | 0.4226 | 0.03333 | 0.02272 | -0.002879 | 0.1 | 0.1667 | 0.2333 |
quantile90 | good | 0.5103 | 0.5105 | 0.03333 | 0.02964 | -0.004537 | 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. |