Generalised Gibbs Ensemble for spherically constrained harmonic models

Gibbs Ensemble Techniques

Generalised Gibbs sampler and multigrid Monte Carlo for Bayesian computation

Although Monte Carlo methods have frequently been applied with success, indiscriminate use of Markov chain Monte Carlo leads to unsatisfactory performances in numerous applications. We present a generalised version of the Gibbs sampler that is based on conditional moves along the traces of groups of transformations in the sample space. We explore its connection with the multigrid Monte Carlo me...

Constrained generalised principal component analysis

Generalised Principal Component Analysis (GPCA) is a recently devised technique for fitting a multicomponent, piecewise-linear structure to data that has found strong utility in computer vision. Unlike other methods which intertwine the processes of estimating structure components and segmenting data points into clusters associated with putative components, GPCA estimates a multi-component stru...

The Gibbs ensemble of a vortex filament

Certain investigations of turbulent 3-D fluids seem to indicate that the vorticity field of the fluid is strongly concentrated along thin structures, called vortex filaments. A. Chorin has developed a statistical-mechanics theory of vortex filaments to describe the statistics of turbulent flows, see [4]. In his investigations vortex filaments are modelled by trajectories of self-avoiding walks ...

Bayesian Generalised Ensemble Markov Chain Monte Carlo

Bayesian generalised ensemble (BayesGE) is a new method that addresses two major drawbacks of standard Markov chain Monte Carlo algorithms for inference in highdimensional probability models: inapplicability to estimate the partition function and poor mixing properties. BayesGE uses a Bayesian approach to iteratively update the belief about the density of states (distribution of the log likelih...