Ding, Shifei Jia, Hongjie Zhang, Liwen Jin, Fengxiang
Published in
Neural Computing and Applications

Clustering is often considered as an unsupervised data analysis method, but making full use of the prior information in the process of clustering will significantly improve the performance of the clustering algorithm. Spectral clustering algorithm can well use the prior pairwise constraint information to cluster and has become a new hot spot of mac...

van den Berg, Stéphanie M. Hjelmborg, Jacob vB.
Published in
Behavior Genetics

Twin concordance rates provide insight into the possibility of a genetic background for a disease. These concordance rates are usually estimated within a frequentistic framework. Here we take a Bayesian approach. For rare diseases, estimation methods based on asymptotic theory cannot be applied due to very low cell probabilities. Moreover, a Bayesi...

Hoque, Zahirul Hossain, Shahadut
Published in
Journal of Statistical Theory and Practice

This article considers the estimation of the intercept parameter of a simple linear regression model under asymmetric linex loss. The least-squares estimator (LSE) and the preliminary test estimator (PTE) are defined. The risk functions of the estimators are derived. The moment-generating function (MGF) and the first two moments of the PTE are show...

Hansen, Thomas Mejer Cordua, Knud Skou Mosegaard, Klaus
Published in
Computational Geosciences

Markov chain Monte Carlo methods such as the Gibbs sampler and the Metropolis algorithm can be used to sample solutions to non-linear inverse problems. In principle, these methods allow incorporation of prior information of arbitrary complexity. If an analytical closed form description of the prior is available, which is the case when the prior can...

Hansen, Thomas Mejer Cordua, Knud Skou Mosegaard, Klaus

Markov chain Monte Carlo methods such as the Gibbs sampler and the Metropolis algorithm can be used to sample solutions to non-linear inverse problems. In principle, these methods allow incorporation of prior information of arbitrary complexity. If an analytical closed form description of the prior is available, which is the case when the prior can...

Hansen, Thomas Mejer Cordua, Knud Skou Mosegaard, Klaus

Markov chain Monte Carlo methods such as the Gibbs sampler and the Metropolis algorithm can be used to sample solutions to non-linear inverse problems. In principle, these methods allow incorporation of prior information of arbitrary complexity. If an analytical closed form description of the prior is available, which is the case when the prior can...

Hansen, Thomas Mejer Cordua, Knud Skou Mosegaard, Klaus

Markov chain Monte Carlo methods such as the Gibbs sampler and the Metropolis algorithm can be used to sample solutions to non-linear inverse problems. In principle, these methods allow incorporation of prior information of arbitrary complexity. If an analytical closed form description of the prior is available, which is the case when the prior can...

Hansen, Thomas Mejer Cordua, Knud Skou Mosegaard, Klaus

Markov chain Monte Carlo methods such as the Gibbs sampler and the Metropolis algorithm can be used to sample solutions to non-linear inverse problems. In principle, these methods allow incorporation of prior information of arbitrary complexity. If an analytical closed form description of the prior is available, which is the case when the prior can...

Hansen, Thomas Mejer Cordua, Knud Skou Mosegaard, Klaus

Markov chain Monte Carlo methods such as the Gibbs sampler and the Metropolis algorithm can be used to sample solutions to non-linear inverse problems. In principle, these methods allow incorporation of prior information of arbitrary complexity. If an analytical closed form description of the prior is available, which is the case when the prior can...

Hansen, Thomas Mejer Cordua, Knud Skou Mosegaard, Klaus

Markov chain Monte Carlo methods such as the Gibbs sampler and the Metropolis algorithm can be used to sample solutions to non-linear inverse problems. In principle, these methods allow incorporation of prior information of arbitrary complexity. If an analytical closed form description of the prior is available, which is the case when the prior can...