  In many real-world engineering systems, the performance or reliability of the
system is characterised by a scalar parameter. The distribution of this
performance parameter is important in many uncertainty quantification problems,
ranging from risk management to utility optimisation. In practice, this
distribution usually cannot be derived analytically and has to be obtained
numerically by simulations. To this end, standard Monte Carlo simulations are
often used, however, they cannot efficiently reconstruct the tail of the
distribution which is essential in many applications. One possible remedy is to
use the Multicanonical Monte Carlo method, an adaptive importance sampling
scheme. In this method, one draws samples from an importance sampling
distribution in a nonstandard form in each iteration, which is usually done via
Markov chain Monte Carlo (MCMC). MCMC is inherently serial and therefore
struggles with parallelism. In this paper, we present a new approach, which
uses the Sequential Monte Carlo sampler to draw from the importance sampling
distribution, which is particularly suited for parallel implementation. With
both mathematical and practical examples, we demonstrate the competitive
performance of the proposed method.
