Plain Page Content

Content Type

First Letter

1 (2) | 2 (21) | A (12) | C (2) | E (1) | G (2) | H (2) | I (1) | K (3) | M (5) | N (2) | P (2) | S (3) | T (1) | W (1)

	1998
	1999
	2001
	2002
	2003
	2004
	2005
	2006
	2007
	2008
	2009
	2010
	2011
	2012
	2013
	2014
	2015
	2016
	2017
	2018
	2019
	2020
	2021
	A Derivative Tracking Model for Wind Power Forecast Error
	A simple approach to proving the existence, uniqueness, and strong and weak convergence rates for a broad class of McKean--Vlasov equations
	A Universal Splitting Estimator for the Performance Evaluation of Wireless Communications Systems
	A Wasserstein Coupled Particle Filter for Multilevel Estimation
	Alexander Litvinenko - Courses
	AMCS 301 Numerical methods for random partial differential equations: hierarchical approximation and machine learning approaches	Bayesian Inversion, Bayesian optimal experimental design, hierarchical and sparse approximation, Hierarchical regression, Low-rank approximation, Monte carlo methods, Multi-index, Multilevel Monte Carlo, Quasi-Monte Carlo, Random PDEs, stochastic algorithms, Stochastic collocation, Teaching
	AMCS 307 - Advanced Statistical Inference by Prof. Marco Scavino	Teaching
	AMCS 308 - Stochastic Numerics with Application in Simulation and Data Science by Prof. Raul Tempone	Teaching
	AMCS 308 Stochastic Numerics with Application in Simulation and Data Science	data assimilation, Filtering theory, Importance sampling, Monte carlo methods, stochastic algorithms, Stochastic Methods, Stochastic Modeling, Stochastic Optimal Control, Stochastic processes, Variance Reduction, Teaching
	AMCS 336 - Fall 2022 - Numerical Methods for Stochastic Differential Equations by Prof. Raul Tempone	Teaching
	AMCS 336 Numerical Methods for Stochastic Differential Equations with connections to Machine Learning	Fokker-Planck equations, Hamilton-Jabobi-Bellman, Importance sampling, Ito integral, Kolmogorov Backward Equation, Monte Carlo, Multilevel Monte Carlo, stochastic differential equations, Stochastic Optimal Control, Variance Reduction, Teaching
	An Accurate Sample Rejection Estimator for the Estimation of Outage Probability With Equal Gain Combining
	Course overview	Teaching
	Courses	Teaching
	Efficient Importance Sampling for the Left Tail of Positive Gaussian Quadratic Forms
	Gallery	Media
	Graduation Project Reports from Stochastic Numerics Research Group
	Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model
	Hit Counter
	Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks
	KAUST - AMCS 309 - 2015 - Computational Multivariate Statistics by Prof. Marco Scavino
	KAUST Master Theses from Stochastic Numerics Research Group	Publications
	KAUST PhD Theses from Stochastic Numerics Research Group	Publications
	Master Thesis from Stochastic Numerics Research Group
	Multilevel double loop Monte Carlo and stochastic collocation methods with importance sampling for Bayesian optimal experimental design
	Multilevel Ensemble Kalman filtering for spatially extended models
	Multilevel Ensemble Kalman Filtering with local-level Kalman Gains
	Multilevel weighted least squares polynomial approximation
	Nesterov-aided stochastic gradient methods using Laplace approximation for Bayesian design optimization
	Numerical smoothing and hierarchical approximations for efficient option pricing and density estimation
	PhD Thesis from Stochastic Numerics Research Group
	Pricing American Options by Exercise Rate Optimization
	Smaller generalization error derived for deep compared to shallow residual neural networks
	Solution of the 3D density-driven groundwater flow problem with uncertain porosity and permeability
	Some probabilistic properties of fractional point processes
	The UQ Hybrid Seminar	uncertainty quantification, Info
	Weak error rates for option pricing under the rough Bergomi model