Monte Carlo simulation provides the benchmark for computational methods in reliability assessment in terms of both accuracy and robustness. However, Monte Carlo methods are well-known to be computationally expensive and, in many cases, intractable. Several improvements have been proposed to increase their computational efficiency for reliability analysis including Latin hypercube sampling; see ...

Considering the problem of sampling from the output photon-counting probability distribution of a linear-optical network for input Gaussian states, we obtain results that are of interest from both quantum theory and the computational complexity theory point of view. We derive a general formula for calculating the output probabilities, and by considering input thermal states, we show that the ou...

The most widely used algorithm for Monte Carlo sampling of electronic transitions in trajectory surface hopping (TSH) calculations is the so-called anteater algorithm, which is inefficient for sampling low-probability nonadiabatic events. We present a new sampling scheme (called the army ants algorithm) for carrying out TSH calculations that is applicable to systems with any strength of couplin...

Rejection sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. The adaptive rejection sampling ...

We study the classic problem of estimating the sum of n variables. The traditional uniform sampling approach requires a linear number of samples to provide any non-trivial guarantees on the estimated sum. In this paper we consider various sampling methods besides uniform sampling, in particular sampling a variable with probability proportional to its value, referred to as linear weighted sampli...

The AIS-BN algorithm [2] is a successful importance sampling-based algorithm for Bayesian networks that relies on two heuristic methods to obtain an initial importance function: 2-cutoff , replacing small probabilities in the conditional probability tables by a larger 2, and setting the probability distributions of the parents of evidence nodes to uniform. However, why the simple heuristics are...

We examine how different types of risk presentation numerical descriptions, experience sampling, graphical displays, and a combination of these formats in a ‘risk tool simulation’influence asset allocations in an experimental investment portfolio. Participants viewed information about a risky and a risk free fund and made a portfolio allocation. Risky allocations were elevated in the risk tool ...

According to prospect theory, people overweight low probability events and underweight high probability events. Several recent papers (notably, Hertwig, Barron, Weber & Erev, 2004) have argued that although this pattern holds for “description-based” decisions, in which people are explicitly provided with probability distributions over potential outcomes, it is actually reversed in “experience-b...