Publications related to Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

Riemannian Optimization For High-Dimensional Tensor Completion

Tensor completion aims to reconstruct a high-dimensional data set where the vast majority of entries is missing. The assumption of low-rank structure in the underlying original data allows us to cast the completion problem into an optimization problem rest ...

Siam Publications2016

Detection over diffusion networks: Asymptotic tools for performance prediction and simulation

Ali H. Sayed

Exploiting recent progress [1]-[4] in the characterization of the detection performance of diffusion strategies over adaptive multi-agent networks: i) we present two theoretical approximations, one based on asymptotic normality and the other based on the t ...

IEEE2016

MATHICSE Technical Report : Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

Fabio Nobile, Giovanni Migliorati

We analyze the accuracy of the discrete least-squares approximation of a function u in multivariate polynomial spaces

P_\Lambda := span\{y \mapsto y^\nu | \nu \in \Lambda\}

with

\Lambda\subset N_0^d

over the domain

\Gamma := [-1,1]^d

, based on the sa ...

MATHICSE2015

A Probabilistic Kernel Method for Human Mobility Prediction with Smartphones

Daniel Gatica-Perez

Human mobility prediction is an important problem which has a large num- ber of applications, especially in context-aware services. This paper presents a study on location prediction using smartphone data, in which we address mod- eling and application asp ...

2015

Approximate Message Passing With Consistent Parameter Estimation and Applications to Sparse Learning

Michaël Unser, Ulugbek Kamilov

We consider the estimation of an independent and identically distributed (i.i.d.) (possibly non-Gaussian) vector x is an element of R-n from measurements y is an element of R-m obtained by a general cascade model consisting of a known linear transform foll ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Efficient inference for spatial extreme value processes associated to log-Gaussian random functions

Jonathan A. Tawn, Jennifer Lynne Wadsworth

Max-stable processes arise as the only possible nontrivial limits for maxima of affinely normalized identically distributed stochastic processes, and thus form an important class of models for the extreme values of spatial processes. Until recently, infere ...

Oxford University Press2014

Role of sampling in evaluating classical time autocorrelation functions

Jiri Vanicek, Tomas Zimmermann

We analyze how the choice of the sampling weight affects efficiency of the Monte Carlo evaluation of classical time autocorrelation functions. Assuming uncorrelated sampling or sampling with constant correlation length, we propose a sampling weight for whi ...

American Institute of Physics2013

Multisensor Change Detection with Nonlinear Canonical Correlation

Devis Tuia, Frank Grégoire Jean de Morsier

The analysis of multi-modal and multi-sensor images is nowadays of paramount importance for Earth Observation (EO) applications. There exist a variety of methods that aim at fusing the different sources of information to obtain a compact representation of ...

2013

Simulation-based uncertainty quantification of human arterial network hemodynamics

Alfio Quarteroni, Gianluigi Rozza, Peng Chen

This work aims at identifying and quantifying uncertainties from various sources in human cardiovascular system based on stochastic simulation of a one dimensional arterial network. A general analysis of different uncertainties and probability characteriza ...

Wiley-Blackwell2013

Robust Data-Driven Dynamic Programming

Daniel Kuhn, Grani Adiwena Hanasusanto

In stochastic optimal control the distribution of the exogenous noise is typically unknown and must be inferred from limited data before dynamic programming (DP)-based solution schemes can be applied. If the conditional expectations in the DP recursions ar ...

2013

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

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Riemannian Optimization For High-Dimensional Tensor Completion

Detection over diffusion networks: Asymptotic tools for performance prediction and simulation

MATHICSE Technical Report : Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension

A Probabilistic Kernel Method for Human Mobility Prediction with Smartphones

Approximate Message Passing With Consistent Parameter Estimation and Applications to Sparse Learning

Efficient inference for spatial extreme value processes associated to log-Gaussian random functions

Role of sampling in evaluating classical time autocorrelation functions

Multisensor Change Detection with Nonlinear Canonical Correlation

Simulation-based uncertainty quantification of human arterial network hemodynamics

Robust Data-Driven Dynamic Programming

Riemannian Optimization For High-Dimensional Tensor Completion

Efficient inference for spatial extreme value processes associated to log-Gaussian random functions

Multisensor Change Detection with Nonlinear Canonical Correlation

Detection over diffusion networks: Asymptotic tools for performance prediction and simulation

A Probabilistic Kernel Method for Human Mobility Prediction with Smartphones

Approximate Message Passing With Consistent Parameter Estimation and Applications to Sparse Learning

Simulation-based uncertainty quantification of human arterial network hemodynamics

Robust Data-Driven Dynamic Programming

Role of sampling in evaluating classical time autocorrelation functions

MATHICSE Technical Report : Discrete least-squares approximations over optimized downward closed polynomial spaces in arbitrary dimension