Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization

Sequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering

On sequential Monte Carlo sampling methods for Bayesian filtering

In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and nonGaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several ...

Nested Sequential Monte Carlo Methods

We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. Furthermore, NSMC can in itself be used to pro...

Sequential data assimilation for streamflow forecasting using a distributed hydrologic model: particle filtering and ensemble Kalman filtering

Accurate streamflow predictions are crucial for mitigating flood damage and addressing operational flood scenarios. In recent years, sequential data assimilation methods have drawn attention due to their potential to handle explicitly the various sources of uncertainty in hydrologic models. In this study, we implement two ensemble-based sequential data assimilation methods for streamflow foreca...

Hydrologic forecasting using artificial neural networks: a Bayesian sequential Monte Carlo approach

Kuo-Lin Hsu Center for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California Irvine, Irvine CA 92697-2175, USA Tel.: +1 949 824 8826 Fax: +1 949 824 8831 E-mail: [email protected] Sequential Monte Carlo (SMC) methods are known to be very effective for the state and parameter estimation of nonlinear and non-Gaussian systems. In this study,...