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 0.4.1
Back to | overview for 0.4.1 | all versions |
Test "1d_binomial_1_1"
Results | |
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Status | good |
CPU time | 297.7 s |
Real time | 299.5 s |
Plots | 1d_binomial_1_1.ps |
Log | 1d_binomial_1_1.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 | 0.1058 | 0.01049 | - | -10.09 | 0.3 | 0.5 | 0.7 |
chi2 | good | 100 | 78.49 | 14.14 | -21.51 | 1.521 | 42.43 | 70.71 | 98.99 |
KS | good | 1 | 0.9998 | 0.95 | -0.01523 | 0.0001603 | 0.95 | 0.99 | 0.9999 |
mean | good | 0.6667 | 0.6667 | 7.463e-05 | 0.005735 | -0.5122 | 0.0002239 | 0.0003732 | 0.0005224 |
mode | good | 1 | 0.995 | 0.03333 | -0.5 | 0.15 | 0.1 | 0.1667 | 0.2333 |
variance | good | 0.05556 | 0.05666 | 0.006621 | 1.982 | -0.1663 | 0.01986 | 0.0331 | 0.04634 |
quantile10 | good | 0.3162 | 0.3163 | 0.03333 | 0.04743 | -0.004499 | 0.1 | 0.1667 | 0.2333 |
quantile20 | good | 0.4472 | 0.4473 | 0.03333 | 0.03164 | -0.004245 | 0.1 | 0.1667 | 0.2333 |
quantile30 | good | 0.5477 | 0.5478 | 0.03333 | 0.008443 | -0.001387 | 0.1 | 0.1667 | 0.2333 |
quantile40 | good | 0.6324 | 0.6325 | 0.03333 | 0.01099 | -0.002086 | 0.1 | 0.1667 | 0.2333 |
quantile50 | good | 0.7071 | 0.7072 | 0.03333 | 0.008557 | -0.001815 | 0.1 | 0.1667 | 0.2333 |
quantile60 | good | 0.7746 | 0.7746 | 0.03333 | 0.002395 | -0.0005566 | 0.1 | 0.1667 | 0.2333 |
quantile70 | good | 0.8366 | 0.8366 | 0.03333 | -0.001635 | 0.0004104 | 0.1 | 0.1667 | 0.2333 |
quantile80 | good | 0.8944 | 0.8944 | 0.03333 | -0.003743 | 0.001004 | 0.1 | 0.1667 | 0.2333 |
quantile90 | good | 0.9487 | 0.9486 | 0.03333 | -0.003984 | 0.001134 | 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. |