Reversible jump sampler for autoregressive time series

We use reversible jump Markov chain Monte Carlo (MCMC) methods to address the problem of model order uncertainty in autoregressive (AR) time series within a Bayesian framework. Efficient model jumping is achieved by proposing model space moves from the full conditional density for the AR parameters, which is obtained analytically. This is compared with an alternative method, for which the moves are cheaper to compute, in which proposals are made only for new parameters in each move. Results are presented for both synthetic and audio time series.

Bayesian separation and recovery of convolutively mixed autoregressive sources

In this paper we address the problem of the separation and recovery of convolutively mixed autoregressive processes in a Bayesian framework. Solving this problem requires the ability to solve integration and/or optimization problems of complicated posterior distributions. We thus propose efficient stochastic algorithms based on Markov chain Monte Carlo (MCMC) methods. We present three algorithms. The first one is a classical Gibbs sampler that generates samples from the posterior distribution. The two other algorithms are stochastic optimization algorithms that allow to optimize either the marginal distribution of the sources, or the marginal distribution of the parameters of the sources and mixing filters, conditional upon the observation. Simulations are presented.

Early Functional Deficit and Microglial Disturbances in a Mouse Model of Amyotrophic Lateral Sclerosis

11 p.

Vortex dislocations in wake-type flow induced by spanwise disturbances

Vortex dislocations in wake-type flow induced by three types of spanwise disturbances superimposed on an upstream velocity profile are investigated by direct numerical simulations. Three distinct modes of vortex dislocations and flow transitions have been found. A local spanwise exponential decay disturbance leads to the appearance of a twisted chainlike mode of vortex dislocation. A stepped spanwise disturbance causes a streamwise periodic spotlike mode of vortex dislocation. A spanwise sinusoidal wavy disturbance with a moderate waviness causes a strong unsteadiness of wake behavior. This unsteadiness starts with a systematic periodic mode of vortex dislocation in the spanwise direction followed by the spanwise vortex shedding suppressed completely with increased time and the near wake becoming a steady shear flow. Characteristics of these modes of vortex dislocation and complex vortex linkages over the dislocation, as well as the corresponding dynamic processes related to the appearance of dislocations, are described by examining the variations of vortex lines and vorticity distribution. The nature of the vortex dislocation is demonstrated by the substantial vorticity modification of the spanwise vortex from the original spanwise direction to streamwise and vertical directions, accompanied by the appearance of noticeable vortex branching and complex vortex linking, all of which are produced at the locations with the biggest phase difference or with a frequency discontinuity between shedding cells. The effect of vortex dislocation on flow transition, either to an unsteady irregular vortex flow or suppression of the Kaacutermaacuten vortex shedding making the wake flow steady state, is analyzed. Distinct similarities are found in the mechanism and main flow phenomena between the present numerical results obtained in wake-type flows and the experimental-numerical results of cylinder wakes reported in previous studies.