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11/09/2020
Practical Bayesian System Identification using Hamiltonian Monte Carlo
This paper addresses Bayesian system identification using a Markov Chain...
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03/25/2019
Real-Time Robotic Search using Hierarchical Spatial Point Processes
Aerial robots hold great potential for aiding Search and Rescue (SAR) ef...
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06/26/2018
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
Pseudo-marginal Metropolis-Hastings (pmMH) is a versatile algorithm for ...
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01/04/2018
Constructing Metropolis-Hastings proposals using damped BFGS updates
This paper considers the problem of computing Bayesian estimates of syst...
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01/04/2018
Sparse Bayesian ARX models with flexible noise distributions
This paper considers the problem of estimating linear dynamic system mod...
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11/17/2015
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
Pseudo-marginal Metropolis-Hastings (pmMH) is a powerful method for Baye...
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11/05/2015
Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models
This tutorial provides a gentle introduction to the particle Metropolis-...
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06/23/2015
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
We consider the problem of approximate Bayesian parameter inference in n...
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03/20/2015
Sequential Monte Carlo Methods for System Identification
One of the key challenges in identifying nonlinear and possibly non-Gaus...
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02/12/2015
Quasi-Newton particle Metropolis-Hastings
Particle Metropolis-Hastings enables Bayesian parameter inference in gen...
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02/12/2015
Newton-based maximum likelihood estimation in nonlinear state space models
Maximum likelihood (ML) estimation using Newton's method in nonlinear st...
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02/06/2015
Marginalizing Gaussian Process Hyperparameters using Sequential Monte Carlo
Gaussian process regression is a popular method for non-parametric proba...
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09/01/2013