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_9_9"
Results | |
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Status | good |
CPU time | 375.1 s |
Real time | 376.2 s |
Plots | 1d_binomial_9_9.ps |
Log | 1d_binomial_9_9.log |
Settings | |
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N chains | 10 |
N lag | 10 |
Convergence | true |
N iterations (pre-run) | 2000 |
N iterations (run) | 10000000 |
Subtest | Status | Target | Test | Uncertainty | Deviation [%] | Deviation [sigma] | Tol. (Good) | Tol. (Flawed) | Tol. (Bad) |
---|---|---|---|---|---|---|---|---|---|
correlation par 0 | off | 0 | 0.2534 | 0.02509 | - | -10.1 | 0.3 | 0.5 | 0.7 |
chi2 | good | 72 | 69.22 | 12 | -3.865 | 0.2319 | 36 | 60 | 84 |
KS | good | 1 | 0.8711 | 0.95 | -12.89 | 0.1357 | 0.95 | 0.99 | 0.9999 |
mean | good | 0.9091 | 0.9091 | 2.642e-05 | 0.003756 | -1.292 | 7.926e-05 | 0.0001321 | 0.0001849 |
mode | good | 1 | 0.995 | 0.03333 | -0.5 | 0.15 | 0.1 | 0.1667 | 0.2333 |
variance | good | 0.006887 | 0.007021 | 0.001508 | 1.95 | -0.08906 | 0.004525 | 0.007541 | 0.01056 |
quantile10 | good | 0.7942 | 0.7943 | 0.03333 | 0.01322 | -0.003149 | 0.1 | 0.1667 | 0.2333 |
quantile20 | good | 0.8513 | 0.8514 | 0.03333 | 0.0125 | -0.003191 | 0.1 | 0.1667 | 0.2333 |
quantile30 | good | 0.8865 | 0.8865 | 0.03333 | 0.003555 | -0.0009455 | 0.1 | 0.1667 | 0.2333 |
quantile40 | good | 0.9124 | 0.9124 | 0.03333 | 0.003124 | -0.0008551 | 0.1 | 0.1667 | 0.2333 |
quantile50 | good | 0.9329 | 0.9329 | 0.03333 | -2.267e-05 | 6.345e-06 | 0.1 | 0.1667 | 0.2333 |
quantile60 | good | 0.9502 | 0.9502 | 0.03333 | 0.0008534 | -0.0002433 | 0.1 | 0.1667 | 0.2333 |
quantile70 | good | 0.9648 | 0.9649 | 0.03333 | 0.002076 | -0.0006008 | 0.1 | 0.1667 | 0.2333 |
quantile80 | good | 0.9779 | 0.9779 | 0.03333 | 0.001051 | -0.0003084 | 0.1 | 0.1667 | 0.2333 |
quantile90 | good | 0.9895 | 0.9895 | 0.03333 | 0.0006411 | -0.0001903 | 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. |