A decision-theoretic approach for segmental classification

This paper is concerned with statistical methods for the segmental classification of linear sequence data where the task is to segment and classify the data according to an underlying hidden discrete state sequence. Such analysis is commonplace in the empirical sciences including genomics, finance and speech processing. In particular, we are interested in answering the following question: given data $y$ and a statistical model $\pi(x,y)$ of the hidden states $x$, what should we report as the prediction $\hat{x}$ under the posterior distribution $\pi (x|y)$? That is, how should you make a prediction of the underlying states? We demonstrate that traditional approaches such as reporting the most probable state sequence or most probable set of marginal predictions can give undesirable classification artefacts and offer limited control over the properties of the prediction. We propose a decision theoretic approach using a novel class of Markov loss functions and report $\hat{x}$ via the principle of minimum expected loss (maximum expected utility). We demonstrate that the sequence of minimum expected loss under the Markov loss function can be enumerated exactly using dynamic programming methods and that it offers flexibility and performance improvements over existing techniques. The result is generic and applicable to any probabilistic model on a sequence, such as Hidden Markov models, change point or product partition models.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS657 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Higgs boson coupling sensitivity at the LHC using H->tau tau decays

We investigate the potential for measuring the relative couplings of a low-mass Higgs boson at the Large Hadron Collider using WH, ZH, and ttbarH production, where the Higgs boson decays to tau-lepton pairs. With 100/fb of sqrt(s) = 14 TeV pp collision data we find that these modes can improve sensitivity to coupling-ratio measurements of a Higgs boson with a mass of about 125 GeV/c^2.Comment: 10 pages, 5 figures, 12 table

Magnetic Fields and Non-Local Transport in Laser Plasmas

The first Vlasov-Fokker-Planck simulations of nanosecond laser-plasma interactions – including the effects of self-consistent magnetic fields and hydrodynamic plasma expansion – will be presented. The coupling between non-locality and magnetic field advection is elucidated. For the largest (initially uniform) magnetic fields externally imposed in recent long-pulse laser gas-jet plasma experiments (12T) a significant degree of cavitation of the B-field will be shown to occur (> 40%) in under 500ps. This is due to the Nernst effect and leads to the re-emergence of non-locality even if the initial value of the magnetic field strength is sufficient to localize transport. Classical transport theory may also break down in such interactions as a result of inverse bremsstrahlung heating. Although non-locality may be suppressed by a large B-field, inverse bremsstrahlung still leads to a highly distorted distribution. Indeed the best fit for a 12T applied field (after 440ps of laser heating) is found to be a super- Gaussian distribution – f0 α e−vm – with m = 3.4. The effects of such a distribution on the transport properties under the influence of magnetic fields are elucidated in the context of laser-plasmas for the first time. In long pulse laser-plasma interactions magnetic fields generated by the thermoelectric (‘∇ne × ∇Te’) mechanism are generally considered dominant. The strength of B-fields generated by this mechanism are affected, and new generation mechanisms are expected, when non-locality is important. Non-local B-field generation is found to be dominant in the interaction of an elliptical laser spot with a nitrogen gas-jet