Small-noise analysis and symmetrization of implicit Monte Carlo samplers

Implicit samplers are algorithms for producing independent, weighted samples from multi-variate probability distributions. These are often applied in Bayesian data assimilation algorithms. We use Laplace asymptotic expansions to analyze two implicit samplers in the small noise regime. Our analysis suggests a symmetrization of the algo- rithms that leads to improved (implicit) sampling schemes at a rel- atively small additional cost. Computational experiments confirm the theory and show that symmetrization is effective for small noise sampling problems

The \u3cem\u3eDeath of a Princess\u3c/em\u3e Cases: Television Programming by State-Owned Public Broadcasters and Viewers\u27 First Amendment Rights

The United States Court of Appeals for the Fifth Circuit consolidated and reheard en banc two cases in which stateowned public television stations cancelled scheduled broadcasts because of the program\u27s content. After examining the first amendment issues that arise when the government exercises editorial discretion in selecting programs, the author concludes that the Fifth Circuit\u27s opinion does not sufficiently protect viewers\u27 interests

The \u3cem\u3eDeath of a Princess\u3c/em\u3e Cases: Television Programming by State-Owned Public Broadcasters and Viewers\u27 First Amendment Rights

The United States Court of Appeals for the Fifth Circuit consolidated and reheard en banc two cases in which stateowned public television stations cancelled scheduled broadcasts because of the program\u27s content. After examining the first amendment issues that arise when the government exercises editorial discretion in selecting programs, the author concludes that the Fifth Circuit\u27s opinion does not sufficiently protect viewers\u27 interests