Identification of nonlinear systems is a challenging problem. Physical
k...
Machine learning practitioners invest significant manual and computation...
This paper considers the problem of computing Bayesian estimates of both...
This paper considers parameter estimation for nonlinear state-space mode...
This paper addresses Bayesian system identification using a Markov Chain...
Machine vision is an important sensing technology used in mobile robotic...
Energy resolved neutron transmission techniques can provide high-resolut...
We consider the problem of maximum likelihood parameter estimation for
n...
We present an approach to designing neural network based models that wil...
We present a new method of learning a continuous occupancy field for use...
Deep kernel learning refers to a Gaussian process that incorporates neur...
In this paper we present a novel quasi-Newton algorithm for use in stoch...
During recent years there has been an increased interest in stochastic
a...
Pseudo-marginal Metropolis-Hastings (pmMH) is a versatile algorithm for
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We provide a numerically robust and fast method capable of exploiting th...
This paper considers the problem of computing Bayesian estimates of syst...
This paper considers the problem of estimating linear dynamic system mod...
We consider a modification of the covariance function in Gaussian proces...
Maximum likelihood (ML) estimation using Newton's method in nonlinear st...