882 citations · 2.6k across the 89 of their papers we have counts for
9 papers · 1 filter
A new structure exploiting derivation of recursive direct weight optimization
Liang Dai, Thomas B. Schön
The recursive direct weight optimization method is used to solve challenging nonlinear system identification problems. This note provides a new derivation and a new interpretation…
On the exponential convergence of the Kaczmarz algorithm
Liang Dai, Thomas Schön
The Kaczmarz algorithm (KA) is a popular method for solving a system of linear equations. In this note we derive a new exponential convergence result for the KA. The key allowing u…
Learning deep dynamical models from image pixels
Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth
Modeling dynamical systems is important in many disciplines, e.g., control, robotics, or neurotechnology. Commonly the state of these systems is not directly observed, but only ava…
Identification of jump Markov linear models using particle filters
Andreas Svensson, Thomas B. Schön, Fredrik Lindsten
Jump Markov linear models consists of a finite number of linear state space models and a discrete variable encoding the jumps (or switches) between the different linear models. Ide…
Divide-and-Conquer with Sequential Monte Carlo
Fredrik Lindsten, Adam M. Johansen, Christian A. Naesseth +4
We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models. This class of algorithms adopts a divide-and-conqu…
Capacity estimation of two-dimensional channels using Sequential Monte Carlo
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
We derive a new Sequential-Monte-Carlo-based algorithm to estimate the capacity of two-dimensional channel models. The focus is on computing the noiseless capacity of the 2-D one-i…
A graph/particle-based method for experiment design in nonlinear systems
Patricio E. Valenzuela, Johan Dahlin, Cristian R. Rojas +1
We propose an extended method for experiment design in nonlinear state space models. The proposed input design technique optimizes a scalar cost function of the information matrix,…
Sequential Monte Carlo for Graphical Models
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM w…
Particle Gibbs with Ancestor Sampling
Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön
Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and Markov ch…