MCMC using Hamiltonian dynamics
RM Neal - arxiv preprint arxiv:1206.1901, 2012 - arxiv.org
Hamiltonian dynamics can be used to produce distant proposals for the Metropolis
algorithm, thereby avoiding the slow exploration of the state space that results from the …
algorithm, thereby avoiding the slow exploration of the state space that results from the …
An overview of informed audio source separation
Audio source separation consists in recovering different unknown signals called sources by
filtering their observed mixtures. In music processing, most mixtures are stereophonic songs …
filtering their observed mixtures. In music processing, most mixtures are stereophonic songs …
Backward simulation methods for Monte Carlo statistical inference
Monte Carlo methods, in particular those based on Markov chains and on interacting particle
systems, are by now tools that are routinely used in machine learning. These methods have …
systems, are by now tools that are routinely used in machine learning. These methods have …
Kernelized Bayesian matrix factorization
We extend kernelized matrix factorization with a fully Bayesian treatment and with an ability
to work with multiple side information sources expressed as different kernels. Kernel …
to work with multiple side information sources expressed as different kernels. Kernel …
Bayesian warped Gaussian processes
M Lázaro-Gredilla - Advances in Neural Information …, 2012 - proceedings.neurips.cc
Abstract Warped Gaussian processes (WGP)[1] model output observations in regression
tasks as a parametric nonlinear transformation of a Gaussian process (GP). The use of this …
tasks as a parametric nonlinear transformation of a Gaussian process (GP). The use of this …
Unsupervised post-nonlinear unmixing of hyperspectral images using a Hamiltonian Monte Carlo algorithm
This paper presents a nonlinear mixing model for hyperspectral image unmixing. The
proposed model assumes that the pixel reflectances are post-nonlinear functions of …
proposed model assumes that the pixel reflectances are post-nonlinear functions of …
Variational Gaussian-process factor analysis for modeling spatio-temporal data
We present a probabilistic latent factor model which can be used for studying spatio-
temporal datasets. The spatial and temporal structure is modeled by using Gaussian …
temporal datasets. The spatial and temporal structure is modeled by using Gaussian …
Improving quadrature for constrained integrands
We present an improved Bayesian framework for performing inference of affine
transformations of constrained functions. We focus on quadrature with nonnegative …
transformations of constrained functions. We focus on quadrature with nonnegative …
Sampling from a multivariate Gaussian distribution truncated on a simplex: a review
In many Bayesian models, the posterior distribution of interest is a multivariate Gaussian
distribution restricted to a specific domain. In particular, when the unknown parameters to be …
distribution restricted to a specific domain. In particular, when the unknown parameters to be …
Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison
Background Complete transcriptional regulatory network inference is a huge challenge
because of the complexity of the network and sparsity of available data. One approach to …
because of the complexity of the network and sparsity of available data. One approach to …