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.

Sources of contention and the emerging reality concerning Qohelet's Carpe Diem advice

Abstract: When Qohelet declares “there is nothing new under the sun,” his own words are no exception. It has been known for a century now that not all of Qohelet’s material is original to his own genius, and the idea that Qohelet is directly dependent on a literary source(s) is standard fare. The hallmark example continues to be Siduri the alewife’s advice to Gilgamesh which displays remarkable correspondence with Ecclesiastes 9: 7-9. However, what may have been construed as an instance of clear literary dependency a century ago cannot be maintained in light of the data that continues to emerge from the ancient Near East. New sources have risen that contend with the Gilgamesh Epic, and there has yet to emerge a definitive victor. This paper calls into question the very idea that Qohelet was directly dependent on a literary precursor and joins with a few select voices both past and present in suggesting an alternate interpretation of the data.