The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling
methods defined on the Riemann manifold to resolve the shortcomings of existing Monte …

This work brings together two powerful concepts in Gaussian processes: the variational
approach to sparse approximation and the spectral representation of Gaussian processes …

An approach to the problem of estimating the size of inhomogeneous crowds, which are
composed of pedestrians that travel in different directions, without using explicit object …

Poisson regression models the noisy output of a counting function as a Poisson random
variable, with a log-mean parameter that is a linear function of the input vector. In this work …

GPstuff: Bayesian Modeling with Gaussian Processes Page 1 Journal of Machine Learning
Research 14 (2013) 1175-1179 Submitted 6/12; Revised 10/12; Published 4/13 GPstuff …

Gaussian process (GP) models form a core part of probabilistic machine learning.
Considerable research effort has been made into attacking three issues with GP models …

This paper considers the robust and efficient implementation of Gaussian process
regression with a Student-t observation model, which has a non-log-concave likelihood. The …

Introducing inequality constraints in Gaussian processes can lead to more realistic
uncertainties in learning a great variety of real-world problems. We consider the finite …

Neutron scattering experiments at three-axes spectrometers (TAS) investigate magnetic and
lattice excitations by measuring intensity distributions to understand the origins of materials …

We introduce non-stationary Mat\'ern field priors with stochastic partial differential equations,
and construct correlation length-scaling with hyperpriors. We model both the hyperprior and …