Learning nonlinear state-space models using smooth particle-filter-based
likelihood approximations

Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations

29 November 2017

Andreas Svensson

Fredrik Lindsten

Thomas B. Schon

Papers citing "Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations"

6 / 6 papers shown

Title
Filtering Variational Objectives Chris J. Maddison Dieterich Lawson George Tucker N. Heess Mohammad Norouzi A. Mnih Arnaud Doucet Yee Whye Teh FedML 111 210 0 25 May 2017
On the construction of probabilistic Newton-type algorithms A. Wills Thomas B. Schon 33 13 0 05 Apr 2017
Learning of state-space models with highly informative observations: a tempered Sequential Monte Carlo solution Andreas Svensson Thomas B. Schon Fredrik Lindsten 39 17 0 06 Feb 2017
Probabilistic Line Searches for Stochastic Optimization Maren Mahsereci Philipp Hennig ODL 45 126 0 10 Feb 2015
Nested Sequential Monte Carlo Methods C. A. Naesseth Fredrik Lindsten Thomas B. Schon 398 84 0 09 Feb 2015
Particle filter-based Gaussian process optimisation for parameter inference J. Dahlin Fredrik Lindsten GP 48 20 0 04 Nov 2013