Zobrazeno 1 - 10
of 304
pro vyhledávání: '"Wills, Adrian"'
Decision trees are commonly used predictive models due to their flexibility and interpretability. This paper is directed at quantifying the uncertainty of decision tree predictions by employing a Bayesian inference approach. This is challenging becau
Externí odkaz:
http://arxiv.org/abs/2403.18147
Decision trees have found widespread application within the machine learning community due to their flexibility and interpretability. This paper is directed towards learning decision trees from data using a Bayesian approach, which is challenging due
Externí odkaz:
http://arxiv.org/abs/2312.01577
Publikováno v:
IEEE Control Systems Magazine, Volume 42, Issue 1, pages 75 - 102, February 2022
Identification of nonlinear systems is a challenging problem. Physical knowledge of the system can be used in the identification process to significantly improve the predictive performance by restricting the space of possible mappings from the input
Externí odkaz:
http://arxiv.org/abs/2210.14684
This paper proposes an improved prediction update for extended target tracking with the random matrix model. A key innovation is to employ a generalised non-central inverse Wishart distribution to model the state transition density of the target exte
Externí odkaz:
http://arxiv.org/abs/2105.12299
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
Machine learning practitioners invest significant manual and computational resources in finding suitable learning rates for optimization algorithms. We provide a probabilistic motivation, in terms of Gaussian inference, for popular stochastic first-o
Externí odkaz:
http://arxiv.org/abs/2102.10880
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
System identification aims to build models of dynamical systems from data. Traditionally, choosing the model requires the designer to balance between two goals of conflicting nature; the model must be rich enough to capture the system dynamics, but n
Externí odkaz:
http://arxiv.org/abs/2012.06341
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
Autor:
Hendriks, Johannes N., Gustafsson, Fredrik K., Ribeiro, Antônio H., Wills, Adrian G., Schön, Thomas B.
This paper is directed towards the problem of learning nonlinear ARX models based on system input--output data. In particular, our interest is in learning a conditional distribution of the current output based on a finite window of past inputs and ou
Externí odkaz:
http://arxiv.org/abs/2012.04136