Zobrazeno 1 - 10
of 243
pro vyhledávání: '"Ninness, Brett"'
Publikováno v:
In Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena April 2024 181
Autor:
Hendriks, Johannes N., Holdsworth, James R. Z., Wills, Adrian G., Schon, Thomas B., Ninness, Brett
This paper considers the problem of determining an optimal control action based on observed data. We formulate the problem assuming that the system can be modelled by a nonlinear state-space model, but where the model parameters, state and future dis
Externí odkaz:
http://arxiv.org/abs/2103.08782
This paper considers the problem of computing Bayesian estimates of both states and model parameters for nonlinear state-space models. Generally, this problem does not have a tractable solution and approximations must be utilised. In this work, a var
Externí odkaz:
http://arxiv.org/abs/2012.07269
This paper considers parameter estimation for nonlinear state-space models, which is an important but challenging problem. We address this challenge by employing a variational inference (VI) approach, which is a principled method that has deep connec
Externí odkaz:
http://arxiv.org/abs/2012.05072
This paper considers Bayesian parameter estimation of dynamic systems using a Markov Chain Monte Carlo (MCMC) approach. The Metroplis-Hastings (MH) algorithm is employed, and the main contribution of the paper is to examine and illustrate the efficac
Externí odkaz:
http://arxiv.org/abs/2011.04117
Jump Markov linear systems (JMLS) are a useful class which can be used to model processes which exhibit random changes in behavior during operation. This paper presents a numerically stable method for learning the parameters of jump Markov linear sys
Externí odkaz:
http://arxiv.org/abs/2004.08564
This paper presents a Bayesian method for identification of jump Markov linear system parameters. A primary motivation is to provide accurate quantification of parameter uncertainty without relying on asymptotic in data-length arguments. To achieve t
Externí odkaz:
http://arxiv.org/abs/2004.08565
This paper presents a method for calculating the smoothed state distribution for Jump Markov Linear Systems. More specifically, the paper details a novel two-filter smoother that provides closed-form expressions for the smoothed hybrid state distribu
Externí odkaz:
http://arxiv.org/abs/2004.08561
Publikováno v:
In IFAC PapersOnLine 2023 56(1):67-72
Pseudo-marginal Metropolis-Hastings (pmMH) is a versatile algorithm for sampling from target distributions which are not easy to evaluate point-wise. However, pmMH requires good proposal distributions to sample efficiently from the target, which can
Externí odkaz:
http://arxiv.org/abs/1806.09780