Evaluation of failure probability of a given system requires sampling of the system response and can be computationally expensive. Therefore it is desirable to construct an accurate surrogate model for the system response and subsequently to sample the surrogate model. In this paper we discuss the properties of this approach. We demonstrate that the straightforward sampling of a surrogate model...

An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique. A key idea of the proposed method is to use extreme values of acquisition functions by Gaussian processes for the next training phase, which should be located near a local maximum or a global maximum of the probability distribut...

Although importance sampling is an established and effective sampling and estimation technique, it becomes unstable and unreliable for highdimensional problems. The main reason is that the likelihood ratio in the importance sampling estimator degenerates when the dimension of the problem becomes large. Various remedies to this problem have been suggested, including heuristics such as resampling...

Although importance sampling is an established and effective sampling and estimation technique, it becomes unstable and unreliable for highdimensional problems. The main reason is that the likelihood ratio in the importance sampling estimator degenerates when the dimension of the problem becomes large. Various remedies to this problem have been suggested, including heuristics such as resampling...

When estimating how much better a classifier is than random allocation in Q-class ROC analysis, we need to sample from a particular region of the unit hypercube: specifically the region, in the unit hypercube, which lies between the Q− 1 simplex in Q(Q− 1) space and the origin. This report introduces a fast method for randomly sampling this volume, and is compared to rejection sampling of unifo...

We present a system for generating suggestions from highly-constrained, continuous design spaces. We formulate suggestion as sampling from a probability distribution; constraints are represented as factors that concentrate probability mass around sub-manifolds of the design space. These sampling problems are intractable using typical random walk MCMC techniques, so we adopt Hamiltonian Monte Ca...

—Determining whether a species has gone extinct is a central problem in both paleobiology and conservation biology. Past literature has mostly employed equations that yield confidence intervals around the endpoints of temporal ranges. These frequentist methods calculate the chance of not having seen a species lately given that it is still alive (a conditional probability). However, any reasonab...

This paper is the report of a Master’s Degree project carried out at Royal Institute of Technology and in this paper we mainly apply the estimators and methods derived by P. Glasserman and J. Li (2003, 2005) of importance sampling methods in portfolio credit risk models. By using the exponential twisting method we will be able to compute the probability beyond one certain loss level (P(L>X)). W...

We propose a new technique called node sampling to speed up the probability-based power estimation methods. I t samples and processes only a small portion of total nodes to estimate the power consumption of a circuit. It is different from the previous speed-up techniques for probability-based methods in that the previous techniques reduce the processing time for each node while our method reduc...

Recently, in the forensic biometric community, there is a growing interest to compute a metric called "likelihood-ratio" when a pair of biometric specimens is compared using a biometric recognition system. Generally, a biometric recognition system outputs a score and therefore a likelihood-ratio computation method is used to convert the score to a likelihood-ratio. The likelihood-ratio is the p...