196 citations · 620 across the 9 of their papers we have counts for
10 papers
Fusion of Information in Multiple Particle Filtering in the Presence of Unknown Static Parameters
Xiaokun Zhao, Marija Iloska, Yousef El-Laham +1
An important and often overlooked aspect of particle filtering methods is the estimation of unknown static parameters. A simple approach for addressing this problem is to augment t…
A Survey of Monte Carlo Methods for Parameter Estimation
D. Luengo, L. Martino, M. Bugallo +2
Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either…
Particle Filtering Under General Regime Switching
Yousef El-Laham, Liu Yang, Petar M. Djuric +1
In this paper, we consider a new framework for particle filtering under model uncertainty that operates beyond the scope of Markovian switching systems. Specifically, we develop a…
Streaming Variational Monte Carlo
Yuan Zhao, Josue Nassar, Ian Jordan +2
Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series. In a streaming setting where data are processed one sample at a time, simul…
Tree-Structured Recurrent Switching Linear Dynamical Systems for Multi-Scale Modeling
Josue Nassar, Scott W. Linderman, Monica Bugallo +1
Many real-world systems studied are governed by complex, nonlinear dynamics. By modeling these dynamics, we can gain insight into how these systems work, make predictions about how…
Robust Covariance Adaptation in Adaptive Importance Sampling
Yousef El-Laham, Victor Elvira, Monica F. Bugallo
Importance sampling (IS) is a Monte Carlo methodology that allows for approximation of a target distribution using weighted samples generated from another proposal distribution. Ad…