Two econometric issues arise in the structural estimation of consumer or producer demand systems in the presence of many binding non-negativity constraints. First, most existing methods entail the evaluation of multivariate probability integrals. Second, the issue of statistical coherency must be addressed. We circumvent both of these issues using Gibbs Sampling, along with data augmentation an...

We consider statistical (sampling-based) solution methods for verifying probabilistic properties with unbounded until. Statistical solution methods for probabilistic verification use sample execution trajectories for a system to verify properties with some level of confidence. The main challenge with properties that are expressed using unbounded until is to ensure termination in the face of pot...

This paper presents a Bayesian combination scheme for combining multiple neural networks. Instead of using fixed combination weights, the estimated probability of a particular network being the true model under a given process operating condition is used as the combination weight for combining that network. A nearest neighbour method is used in estimating the network error for a given input dat...

For reliability analysis of implicit limit state function, an improved line sampling method is presented on the basis of sample simulation in failure region. In the presented method, Markov Chain is employed to simulate the samples located at failure region, and the important direction of line sampling is obtained from these simulated samples. Simultaneously, the simulated samples can be used a...

In many practical applications of structural reliability analysis, one is interested in knowing the sensitivity of the probability of failure to design parameters that enter the definition of the limit-state function. This information is required for example in reliability-based design optimization. Parameter sensitivities are obtained by FORM/SORM, in terms of the sensitivity of the respective...

This paper presents a Bayesian combination scheme for combining multiple neural networks. Instead of using fixed combination weights, the estimated probability of a particular network being the true model under a given process operating condition is used as the combination weight for combining that network. A nearest neighbour method is used in estimating the network error for a given input dat...

For more than two decades, there has been a growing of interest in fast simulation techniques for estimating probabilities of rare events in queuing networks. Importance sampling is a variance reduction method for simulating rare events. The present paper carries out strict deadlines to the paper by Dupuis et al for a two node tandem network with feedback whose arrival and service rates are mod...

Conditional random fields are among the state-of-the art approaches to structured output prediction, and the model has been adopted for various real-world problems. The supervised classification is expensive, since it is usually expensive to produce labelled data. Unlabeled data are relatively cheap, but how to use it? Unlabeled data can be used to estimate marginal probability of observations,...