9 citations · 10 across the 4 of their papers we have counts for
4 papers
Recursive Learning of Asymptotic Variational Objectives
Alessandro Mastrototaro, Mathias Müller, Jimmy Olsson
General state-space models (SSMs) are widely used in statistical machine learning and are among the most classical generative models for sequential time-series data. SSMs, comprisi…
Online Variational Sequential Monte Carlo
Alessandro Mastrototaro, Jimmy Olsson
Being the most classical generative model for serial data, state-space models (SSM) are fundamental in AI and statistical machine learning. In SSM, any form of parameter learning o…
Adaptive online variance estimation in particle filters: the ALVar estimator
Alessandro Mastrototaro, Jimmy Olsson
We present a new approach-the ALVar estimator-to estimation of asymptotic variance in sequential Monte Carlo methods, or, particle filters. The method, which adjusts adaptively the…
Fast and numerically stable particle-based online additive smoothing: the AdaSmooth algorithm
Alessandro Mastrototaro, Jimmy Olsson, Johan Alenlöv
We present a novel sequential Monte Carlo approach to online smoothing of additive functionals in a very general class of path-space models. Hitherto, the solutions proposed in the…