MATLAB and Python 3 software for Bayesian inference of engineering models using an adaptive version of the BUS approach (aBUS). In the standard version of BUS the choice of a constant c is required such that 1/c is not smaller than the maximum of the likelihood function. The aBUS method combines the BUS approach with the SuS method in such a way that the constant c is no longer required as input. The aBUS approach is well-suited for problems with many uncertain parameters and for problems where it is computationally demanding to evaluate the likelihood function.
- Number of samples per subset level
- Intermediate conditional probability
- Natural logarithm of the likelihood function passed as a handle to a user-defined MATLAB function
- Prior distribution defined as an object of the ERANataf class
The software returns:
- Samples from the posterior distribution in the standard space
- Samples from the posterior distribution in the original space
- An estimate of the model evidence
- Constant c
- Scaling of the proposal at the last level
MATLAB, incl. Statistical toolbox, ERADist and ERANataf probability distribution classes
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Au S.-K., Beck J.L. (2001): Estimation of small failure probabilities in high dimensions by subset simulation. Probabilistic Engineering Mechanics, ASCE, 16: 263-277.
Papaioannou I., Betz W., Zwirglmaier K., Straub D. (2015): MCMC algorithms for subset simulation. Probabilistic Engineering Mechanics, 41: 89-103.