Jasper Behrensdorf

Happy to accept a pull request :).

Implemented right now are - Metropolis Hastings `mh()` - Gibbs Sampler `gibbssample()` - Transitional MCMC `tmcmc()` - Sequential Monte Carlo `smc()` along with some helper methods. To clean this up...

I need to find a few more examples to check hermite polynomials and also hybrid bases.

> Looks great to me, a very nice contribution and implementation. Only (minor) suggestion is that evaluate! could allow for raw samples as well as a DF. But we would...

Ideally, the method signature would include the arguments that all of the methods have (if they exist). Like ```julia function bayesian_model_updating(likelihood::Function, prior::Function, bmu::AbstractBayesianModelUpdating) ... end ``` where the `bmu` includes...

@AnderGray This is still very much in progress, but if you have time and want to provide some feedback please go ahead. I think the base idea is sound and...

@AnderGray Thoughts on the second method with inner optimization?

Based on the feedback from the QMC.jl devs we need to restrict lattice rules to shifting and allows scrambling only for digital nets (sobol, faure (to be added)). Then we...

I think this might be the algorithm. Adaptive: https://doi.org/10.1016/j.strusafe.2007.10.002 Original: https://doi.org/10.1016/0167-4730(86)90012-3

# Proposal Implement all analyses like `probability_of_failure` with *imprecise* simulation structs that contain the internal simulation. For example: ```julia pf, cov, samples = probability_of_failure(inputs, model, performance, DoubleLoop(100, MonteCarlo(10000)) ``` Then...