Generalised Mixability, Constant Regret, and Bayesian Updating

Mixability of a loss is known to characterise when constant regret bounds are achievable in games of prediction with expert advice through the use of Vovk's aggregating algorithm. We provide a new interpretation of mixability via convex analysis that highlights the role of the Kullback-Leibler divergence in its definition. This naturally generalises to what we call $\Phi$-mixability where the Bregman divergence $D_\Phi$ replaces the KL divergence. We prove that losses that are $\Phi$-mixable also enjoy constant regret bounds via a generalised aggregating algorithm that is similar to mirror descent.Comment: 12 page

Generalized Mixability via Entropic Duality

Mixability is a property of a loss which characterizes when fast convergence is possible in the game of prediction with expert advice. We show that a key property of mixability generalizes, and the exp and log operations present in the usual theory are not as special as one might have thought. In doing this we introduce a more general notion of $\Phi$-mixability where $\Phi$ is a general entropy (\ie, any convex function on probabilities). We show how a property shared by the convex dual of any such entropy yields a natural algorithm (the minimizer of a regret bound) which, analogous to the classical aggregating algorithm, is guaranteed a constant regret when used with $\Phi$-mixable losses. We characterize precisely which $\Phi$ have $\Phi$-mixable losses and put forward a number of conjectures about the optimality and relationships between different choices of entropy.Comment: 20 pages, 1 figure. Supersedes the work in arXiv:1403.2433 [cs.LG

Non‐Rayleigh Statistics of Ultrasonic Backscattered Echo from Tissues

The envelope of the backscattered signal from tissues can exhibit non‐Rayleigh statistics if the number density of scatterers is small or if the variations in the scattering cross sections are random. The K distribution which has been used extensively in radar, is introduced to model this non‐Rayleigh behavior. The generalized K distribution is extremely useful since it encompasses a wide range of distributions such as Rayleigh, Lognormal, and Rician. Computer simulations were conducted using a simple one‐dimensional discrete scatteringmodel to investigate the properties of the echo envelope. In addition to cases of low number densities, significant departures from Rayleigh statistics were seen as the scattering cross sections of the scatterers become random. The validity of this model was also tested using data from tissue mimicking phantoms. Results indicate that the density function of the envelope can be modeled by the K distribution and the parameters of the K distribution can provide information on the nature of the scattering region in terms of the number density of the scatterers as well as the scattering cross sections of the scatterers in the range cell. [Work was supported by NSF Grant No. BCS‐9207385.

The DSM-5 changes and ADHD: More than a tweak of terms

A recent commentary reignited discussion within this journal about the access by clinicians to the updated Australian National Guidelines on Attention Deficit Hyperactivity Disorder (ADHD) (The Royal Australian College of Physicians, 2009). We seek to extend this debate by noting that the new Diagnostic and Statistical Manual of Mental Disorders (DSM-5) included a number of changes to the definition of ADHD. Given the reliance of these draft national guidelines on the DSM-IV, it can be expected that the DSM-5 changes will also have significant impact on the future form of guidelines, research and clinical practice (Al-Yagon et al., 2013). Further, the high prevalence of ADHD makes it important for clinicians to understand these recent changes and their implications for diagnosis and treatment (Bell, 2011). Close analysis shows that the changes in the DSM-5 are more than just a tweak in terminology. The result is likely to be a growth in levels of diagnosis and increased demands on clinical, health and education professionals

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