Abstract We present an improved version of the nested sampling algorithm nessai in which core is modified to use importance weights. In algorithm, samples are drawn from a mixture normalising flows and requirement for be independently identically distributed (i.i.d.) according prior relaxed. Furthermore, it allows added any order, likelihood constraint, evidence updated with batches samples. ca...

Simulating many-body quantum systems is a promising task for computers. However, the depth of most algorithms, such as product formulas, scales with number terms in Hamiltonian, and can therefore be challenging to implement on near-term, well early fault-tolerant devices. An efficient solution given by stochastic compilation protocol known qDrift, which builds random formulas sampling from Hami...

When observations are incomplete or data are missing, approximate inference methods based on importance sampling are often used. Unfortunately, when the target and proposal distributions are dissimilar, the sampling procedure leads to biased estimates or requires a prohibitive number of samples. Our method approximates a multivariate target distribution by sampling from an existing, sequential ...

The paper focuses on developing effective importance sampling algorithms for mixed probabilistic and deterministic graphical models. The use of importance sampling in such graphical models is problematic because it generates many useless zero weight samples which are rejected yielding an inefficient sampling process. To address this rejection problem, we propose the SampleSearch scheme that aug...

This report contains a tutorial introduction to the method of importance sampling. The use of this method is illustrated for simulations of the noise-induced energy jitter of return-to-zero pulses in optical communication systems.

The variance minimization (VM) and cross-entropy (CE) methods are two versatile adaptive importance sampling procedures that have been successfully applied to a wide variety of difficult rare-event estimation problems. We compare these two methods via various examples where the optimal VM and CE importance densities can be obtained analytically. We find that in the cases studied both VM and CE ...