First-order methods to recover the measurement operators and states of bipartite quantum correlations

In this thesis, we start with giving a mathematical description of bipartite quantum correlations and how they are built up in the Tensor model. This is needed because we want to recover the state and the operators when only the bipartite quantum correlation is known. In the literature, there are see-saw algorithms to...

Probabilistic analysis of slope stability in spatially variable soils using Monte Carlo simulation

Soil properties are spatially variable due to the natural deposition process. Because of this inherent spatial variability, a slope can actually fail along any potential slip surface. A single value of Factor of safety cannot account for this variation dominated the slope stability problem. Probabilistic analysis considering the spatial...

A maximum likelihood ensemble filter via a modified cholesky decomposition for non-gaussian data assimilation

This paper proposes an efficient and practical implementation of the Maximum Likelihood Ensemble Filter via a Modified Cholesky decomposition (MLEF-MC). The method works as follows: via an ensemble of model realizations, a well-conditioned and full-rank square-root approximation of the background error covariance matrix is obtained. This...

Correcting Non Positive Definite Correlation Matrices