Robust Covariance Adaptation in Adaptive Importance Sampling

Importance sampling (IS) is a Monte Carlo methodology that allows for approximation of a target distribution using weighted samples generated from another proposal distribution. Adaptive importance sampling (AIS) implements an iterative version of IS which adapts the parameters of the proposal distribution in order to improve estimation of the target. While the adaptation of the location (mean) of the proposals has been largely studied, an important challenge of AIS relates to the difficulty of adapting the scale parameter (covariance matrix). In the case of weight degeneracy, adapting the covariance matrix using the empirical covariance results in a singular matrix, which leads to poor performance in subsequent iterations of the algorithm. In this paper, we propose a novel scheme which exploits recent advances in the IS literature to prevent the so-called weight degeneracy. The method efficiently adapts the covariance matrix of a population of proposal distributions and achieves a significant performance improvement in high-dimensional scenarios. We validate the new method through computer simulations

Particle Filtering Under General Regime Switching

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 novel particle filtering algorithm that applies to general regime switching systems, where the model index is augmented as an unknown time-varying parameter in the system. The proposed approach does not require the use of multiple filters and can maintain a diverse set of particles for each considered model through appropriate choice of the particle filtering proposal distribution. The flexibility of the proposed approach allows for long-term dependencies between the models, which enables its use to a wider variety of real-world applications. We validate the method on a synthetic data experiment and show that it outperforms state-of-the-art multiple model particle filtering approaches that require the use of multiple filters.Comment: Accepted to EUSIPCO 202

Improving population Monte Carlo : Alternative weighting and resampling schemes

Population Monte Carlo (PMC) sampling methods are powerful tools for approximating distributions of static unknowns given a set of observations. These methods are iterative in nature: at each step they generate samples from a proposal distribution and assign them weights according to the importance sampling principle. Critical issues in applying PMC methods are the choice of the generating functions for the samples and the avoidance of the sample degeneracy. In this paper, we propose three new schemes that considerably improve the performance of the original PMC formulation by allowing for better exploration of the space of unknowns and by selecting more adequately the surviving samples. A theoretical analysis is performed, proving the superiority of the novel schemes in terms of variance of the associated estimators and preservation of the sample diversity. Furthermore, we show that they outperform other state of the art algorithms (both in terms of mean square error and robustness w.r.t. initialization) through extensive numerical simulations. (C) 2016 Elsevier B.V. All rights reserved.Peer reviewe

In Search for Improved Auxiliary Particle Filters

International audienc

Early exposure to STEM research as a foundational experience for STEM careers

The benefits of undergraduate research to student persistence and success has been established in the literature. Less studied, however, is the long term impact of early exposure to research among students from underserved backgrounds.This qualitative study of undergraduates participating in a unique summer research program uncovers the deeper meaning of the overall experience on the students: from the lab itself, to the mentors, peers, professional development, socials, and the impact of the program staff. Three major themes emerged: (1) Early exposure to research as a foundation for career direction; (2) Relationships with peers and mentors as highly valued; and (3) Development of skills leads to personal and professional growth, and confidence. Additionally, underrepresented students described the value of having minority role models and peers, and the excitement of continuing their research throughout their undergraduate careers. A full compensation package of stipend and housing made a practical difference for several of the participants. This qualitative study offers a deeper understanding of these impacts through the voices of the participants.&nbsp