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We describe a new Bayesian click-through rate (CTR) prediction algorithm used for Sponsored Search in Microsoft's Bing search engine. The algorithm is based on a probit regression model that maps discrete or real-valued input features to probabilities. It maintains Gaussian beliefs over weights of the model and performs Gaussian online updates derived from(More)
BACKGROUND The ornamental crop Calluna vulgaris is of increasing importance to the horticultural industry in the northern hemisphere due to a flower organ mutation: the flowers of the 'bud-flowering' phenotype remain closed i.e. as buds throughout the total flowering period and thereby maintain more colorful flowers for a longer period of time than the(More)
Calluna vulgaris is one of the most important landscaping plants produced in Germany. Its enormous economic success is due to the prolonged flower attractiveness of mutants in flower morphology, the so-called bud-bloomers. In this study, we present the first genetic linkage map of C. vulgaris in which we mapped a locus of the economically highly desired(More)
The production of heather (Calluna vulgaris) in Germany is highly dependent on cultivars with mutated flower morphology, the so-called diplocalyx bud bloomers. So far, this unique flower type of C. vulgaris has not been reported in any other plant species. The flowers are characterised by an extremely extended flower attractiveness, since the flower buds(More)
BACKGROUND Variety protection is of high relevance for the horticultural community and juridical cases have become more frequent in a globalized economy due to essential derivation of varieties. This applies equally to Calluna vulgaris, a vegetatively propagated species from the Ericaceae family that belongs to the top-selling pot plants in Europe. We(More)
We present an efficient Bayesian online learning algorithm for clustering vectors of binary values based on a well known model, the mixture of Bernoulli profiles. The model includes conjugate Beta priors over the success probabilities and maintains discrete probability distributions for cluster assignments. Clustering is then formulated as inference in a(More)
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