A quantum framework for likelihood ratios

The ability to calculate precise likelihood ratios is fundamental to many STEM areas, such as decision-making theory, biomedical science, and engineering. However, there is no assumption-free statistical methodology to achieve this. For instance, in the absence of data relating to covariate overlap, the widely used Bayes’ theorem either defaults to the marginal probability driven “naive Bayes’ classifier”, or requires the use of compensatory expectation-maximization techniques. Equally, the use of alternative statistical approaches, such as multivariate logistic regression, may be confounded by other axiomatic conditions, e.g., low levels of co-linearity. This article takes an information-theoretic approach in developing a new statistical formula for the calculation of likelihood ratios based on the principles of quantum entanglement. In doing so, it is argued that this quantum approach demonstrates: that the likelihood ratio is a real quality of statistical systems; that the naive Bayes’ classifier is a special case of a more general quantum mechanical expression; and that only a quantum mechanical approach can overcome the axiomatic limitations of classical statistics

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Inhibition in multiclass classification

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches

Discovery of the Barium Isotopes

Thirty-eight barium isotopes have so far been observed; the discovery of these isotopes is discussed. For each isotope a brief summary of the first refereed publication, including the production and identification method, is presented.Comment: to be published in At. Data Nucl. Data Table

Stability analysis of mixtures of mutagenetic trees

<p>Abstract</p> <p>Background</p> <p>Mixture models of mutagenetic trees are evolutionary models that capture several pathways of ordered accumulation of genetic events observed in different subsets of patients. They were used to model HIV progression by accumulation of resistance mutations in the viral genome under drug pressure and cancer progression by accumulation of chromosomal aberrations in tumor cells. From the mixture models a genetic progression score (GPS) can be derived that estimates the genetic status of single patients according to the corresponding progression along the tree models. GPS values were shown to have predictive power for estimating drug resistance in HIV or the survival time in cancer. Still, the reliability of the exact values of such complex markers derived from graphical models can be questioned.</p> <p>Results</p> <p>In a simulation study, we analyzed various aspects of the stability of estimated mutagenetic trees mixture models. It turned out that the induced probabilistic distributions and the tree topologies are recovered with high precision by an EM-like learning algorithm. However, only for models with just one major model component, also GPS values of single patients can be reliably estimated.</p> <p>Conclusion</p> <p>It is encouraging that the estimation process of mutagenetic trees mixture models can be performed with high confidence regarding induced probability distributions and the general shape of the tree topologies. For a model with only one major disease progression process, even genetic progression scores for single patients can be reliably estimated. However, for models with more than one relevant component, alternative measures should be introduced for estimating the stage of disease progression.</p

From training to artisanal practice : rethinking choreographic relationships in modern dance

In the first part of the twentieth century early modern dancers created both a new art form and the forms of group social organisation that were its condition of possibility. This paper critically examines the balletic and disciplinary &lsquo;training&rsquo; model of dancer formation and proposes that the assumption of training in dance can obscure other ways of understanding dance-making relationships and other values in early modern dance. An &lsquo;artisanal&rsquo; mode of production and knowledge transmission based on a non-binary relationship between&nbsp;&lsquo;master&rsquo; and apprentice and occurring in a quasi-domestic and personalised space of some intimacy is proposed as a more pertinent way to think the enabling conditions of modern dance creation

On the Correlations between Galaxy Properties and Supermassive Black Hole Mass

We use a large sample of upper limits and accurate estimates of supermassive black holes masses coupled with libraries of host galaxy velocity dispersions, rotational velocities and photometric parameters extracted from Sloan Digital Sky Survey i-band images to establish correlations between the SMBH and host galaxy parameters. We test whether the mass of the black hole, MBH, is fundamentally driven by either local or global galaxy properties. We explore correlations between MBH and stellar velocity dispersion sigma, bulge luminosity, bulge mass Sersic index, bulge mean effective surface brightness, luminosity of the galaxy, galaxy stellar mass, maximum circular velocity Vc, galaxy dynamical and effective masses. We verify the tightness of the MBH-sigma relation and find that correlations with other galaxy parameters do not yield tighter trends. We do not find differences in the MBH-sigma relation of barred and unbarred galaxies. The MBH-sigma relation of pseudo-bulges is also coarser and has a different slope than that involving classical bulges. The MBH-bulge mass is not as tight as the MBH-sigma relation, despite the bulge mass proving to be a better proxy of MBH than bulge luminosity. We find a rather poor correlation between MBH and Sersic index suggesting that MBH is not related to the bulge light concentration. The correlations between MBH and galaxy luminosity or mass are not a marked improvement over the MBH sigma relation. If Vc is a proxy for the dark matter halo mass, the large scatter of the MBH-Vc relation then suggests that MBH is more coupled to the baryonic rather than the dark matter. We have tested the need for a third parameter in the MBH scaling relations, through various linear correlations with bulge and galaxy parameters, only to confirm that the fundamental plane of the SMBH is mainly driven by sigma, with a small tilt due to the effective radius. (Abridged)Comment: 32 pages, 18 figures, 6 tables, accepted for publication in MNRA

Haplotype Estimation from Fuzzy Genotypes Using Penalized Likelihood

The Composite Link Model is a generalization of the generalized linear model in which expected values of observed counts are constructed as a sum of generalized linear components. When combined with penalized likelihood, it provides a powerful and elegant way to estimate haplotype probabilities from observed genotypes. Uncertain (“fuzzy”) genotypes, like those resulting from AFLP scores, can be handled by adding an extra layer to the model. We describe the model and the estimation algorithm. We apply it to a data set of accurate human single nucleotide polymorphism (SNP) and to a data set of fuzzy tomato AFLP scores

Haplotype association analyses in resources of mixed structure using Monte Carlo testing

<p>Abstract</p> <p>Background</p> <p>Genomewide association studies have resulted in a great many genomic regions that are likely to harbor disease genes. Thorough interrogation of these specific regions is the logical next step, including regional haplotype studies to identify risk haplotypes upon which the underlying critical variants lie. Pedigrees ascertained for disease can be powerful for genetic analysis due to the cases being enriched for genetic disease. Here we present a Monte Carlo based method to perform haplotype association analysis. Our method, hapMC, allows for the analysis of full-length and sub-haplotypes, including imputation of missing data, in resources of nuclear families, general pedigrees, case-control data or mixtures thereof. Both traditional association statistics and transmission/disequilibrium statistics can be performed. The method includes a phasing algorithm that can be used in large pedigrees and optional use of pseudocontrols.</p> <p>Results</p> <p>Our new phasing algorithm substantially outperformed the standard expectation-maximization algorithm that is ignorant of pedigree structure, and hence is preferable for resources that include pedigree structure. Through simulation we show that our Monte Carlo procedure maintains the correct type 1 error rates for all resource types. Power comparisons suggest that transmission-disequilibrium statistics are superior for performing association in resources of only nuclear families. For mixed structure resources, however, the newly implemented pseudocontrol approach appears to be the best choice. Results also indicated the value of large high-risk pedigrees for association analysis, which, in the simulations considered, were comparable in power to case-control resources of the same sample size.</p> <p>Conclusions</p> <p>We propose hapMC as a valuable new tool to perform haplotype association analyses, particularly for resources of mixed structure. The availability of meta-association and haplotype-mining modules in our suite of Monte Carlo haplotype procedures adds further value to the approach.</p