Parcourir par sujet "Importance sampling"

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Bayesian annealed sequential importance sampling: An unbiased version of transitional Markov chain Monte Carlo

Wu S., Angelikopoulos P., Papadimitriou C., Koumoutsakos P. (2018)

The transitional Markov chain Monte Carlo (TMCMC) is one of the efficient algorithms for performing Markov chain Monte Carlo (MCMC) in the context of Bayesian uncertainty quantification in parallel computing architectures. ...
Path Planning for Autonomous Robotic Platform based on Created Sampling Maps

Asiminari G., Kateris D., Moysiadis V., Menexes I., Tagarakis A.C., Bochtis D. (2022)

Soil properties are of great importance in crop management, as they highly affect plant growth, crop production and product quality. In order to examine these properties, soil samples must be collected from the entire ...
Sequential importance sampling for structural reliability analysis

Papaioannou I., Papadimitriou C., Straub D. (2016)

This paper proposes the application of sequential importance sampling (SIS) to the estimation of the probability of failure in structural reliability. SIS was developed originally in the statistical community for exploring ...