A sequence-dependent coarse-grain model of B-DNA with explicit description of bases and phosphate groups parametrised from large scale Molecular Dynamics simulations
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This paper presents a score that can be used for evaluating probabilistic forecasts of multicategory events. The score is a reinterpretation of the logarithmic score or ignorance score, now formulated as the relative entropy or Kullback–Leibler divergence ...
A novel hierarchy of coarse-grain, sequence-dependent, rigid-base models of B-form DNA in solution is introduced. The hierarchy depends on both the assumed range of energetic couplings, and the extent of sequence dependence of the model parameters. A signi ...
We study the distributed inference task over regression and classification models where the likelihood function is strongly log-concave. We show that diffusion strategies allow the KL divergence between two likelihood functions to converge to zero at the r ...
Shadowgraphy was employed to study snow saltation in boundary-layer wind tunnel experiments with fresh, naturally deposited snow. The shadowgraphy method allowed for a temporally and spatially high-resolution investigation of snow particle characteristics ...
Sequence dependent mechanics of DNA is believed to play a central role in the functioning of the cell through the expression of genetic information. Nucleosome positioning, gene regulation, DNA looping and packaging within the cell are only some of the pro ...
Many speech technology systems rely on Gaussian Mixture Models (GMMs). The need for a comparison between two GMMs arises in applications such as speaker verification, model selection or parameter estimation. For this purpose, the Kullback-Leibler (KL) dive ...
This paper investigates the construction of determin- istic measurement matrices preserving the entropy of a random vector with a given probability distribution. In particular, it is shown that for a random vector with i.i.d. discrete components, this is a ...
When a phenomenon is described by a parametric model and multiple datasets are available, a key problem in statistics is to discover which datasets are characterized by the same parameter values. Equivalently, one is interested in partitioning the family o ...
We show that estimation of parameters for the popular Gaussian model of speech in noise can be regularised in a Bayesian sense by use of simple prior distributions. For two example prior distributions, we show that the marginal distribution of the uncorrup ...
When a phenomenon is described by a parametric model and multiple datasets are available, a key problem in statistics is to discover which datasets are characterized by the same parameter values. Equivalently, one is interested in partitioning the family o ...