Detector simulation in high energy physics experiments is a key yet computationally expensive step the event process. There has been much recent interest using deep generative models as faster alternative to full Monte Carlo process situations which utmost accuracy not necessary. In this work we investigate use of conditional Wasserstein Generative Adversarial Networks simulate both hadronizati...

Based on two procedures for efficiently generating conditional samples, i.e. Markov chain Monte Carlo (MCMC) simulation and importance sampling (IS), two reliability sensitivity (RS) algorithms are presented. On the basis of reliability analysis of Subset simulation (Subsim), the RS of the failure probability with respect to the distribution parameter of the basic variable is transformed as a s...

We present an algorithm to generate samples from probability distributions on the space of curves. Traditional curve evolution methods use gradient descent to find a local minimum of a specified energy functional. Here, we view the energy functional as a negative log probability distribution and sample from it using a Markov chain Monte Carlo (MCMC) algorithm. We define a proposal distribution ...

Using motion capture data has nowadays utterly been adopted by video game creators or virtual reality applications. In a context of interactive applications, adapting those data to new situations or producing variants of those motions are known as non trivial tasks. We propose an original method that produces motions that preserve the statistical properties of a reference motion while ensuring ...

Gaussian random fields (GRF) conditional simulation is a key ingredient in many spatial statistics problems for computing Monte-Carlo estimators and quantifying uncertainties on non-linear functionals of GRFs conditional on data. Conditional simulations are known to often be computer intensive, especially when appealing to matrix decomposition approaches with a large number of simulation points...