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Constructing adaptive generalized polynomial chaos method to measure the uncertainty in continuous models: A computational approach
12 references·1 citing·31 second hop
Constructing adaptive generalized polynomial chaos method to measure the uncertainty in continuous models: A computational approach (2014)Constructing adaptive generalized polynom…Numerical Solution of Stochastic Differential Equations (2016)Numerical Solution of Stochas…Numerical solution of stochastic differential equations (2016)Numerical solution of stochas…On the convergence of generalized polynomial chaos expansions (2011)On the convergence of general…Time-dependent generalized polynomial chaos (2010)Time-dependent generalized po…Stochastic Differential Equations (2006)Stochastic Differential Equat…Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes (2004)Numerical Challenges in the U…The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)The Wiener--Askey Polynomial …Stochastic Finite Elements: A Spectral Approach (1991)Stochastic Finite Elements: A…Application of Wiener-Hermite Expansion to Nonstationary Random Vibration of a Duffing Oscillator (1983)Application of Wiener-Hermite…Stochastic Differential Equations: Theory and Applications. (1976)Stochastic Differential Equat…STOCHASTIC DIFFERENTIAL EQUATIONS: THEORY AND APPLICATIONS (1976)STOCHASTIC DIFFERENTIAL EQUAT…Stochastic Differential Equations (1974)Stochastic Differential Equat…Computing the density function of complex models with randomness by using polynomial expansions and the RVT technique. Application to the SIR epidemic model (2020)Computing the density functio…Stochastic spectral methods for efficient Bayesian solution of inverse problems (2006)The Malliavin Calculus and Related Topics (2006)Simulation-based optimal Bayesian experimental design for nonlinear systems (2012)High-Order Collocation Methods for Differential Equations with Random Inputs (2005)Sparse tensor discretizations of high-dimensional parametric and stochastic PDEs (2011)Adaptive Smolyak Pseudospectral Approximations (2013)Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark (2021)The Pricing of Options and Corporate Liabilities (1973)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Generalized autoregressive conditional heteroskedasticity (1986)Parameter estimation and bias correction for diffusion processes (2008)Weak Approximation of Stochastic Differential Equations and Application to Derivative Pricing (2008)Numerical schemes for dynamically orthogonal equations of stochastic fluid and ocean flows (2012)Global sensitivity analysis using polynomial chaos expansions (2007)AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation (2011)The Distribution of a Perpetuity, with Applications to Risk Theory and Pension Funding (1990)Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed-form Approximation Approach (2002)The Statistical and Economic Role of Jumps in Continuous‐Time Interest Rate Models (2004)Martingale Methods in Financial Modelling (1997)Alternative models for stock price dynamics (2003)Multilevel Monte Carlo Path Simulation (2008)Blocking When Service Is Required From Several Facilities Simultaneously (1985)Minimizing the Probability of Ruin When Claims Follow Brownian Motion with Drift (2005)Continuous Time Structural Equation Modeling with R Package ctsem (2017)Adaptive sparse polynomial chaos expansion based on least angle regression (2010)Option Pricing when the Variance Changes Randomly: Theory, Estimation, and an Application (1987)ARCH models as diffusion approximations (1990)A Model of Intertemporal Asset Prices Under Asymmetric Information (1993)The Malliavin calculus, a functional analytic approach (1981)Discrete-time approximation of decoupled Forward–Backward SDE with jumps (2007)Linear quadratic regulation of systems with stochastic parameter uncertainties (2009)
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References

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  • 2020Computing the density function of complex models with randomness by using polynomial expansions and the RVT technique. Application to the SIR epidemic model14 cited

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