86 research outputs found
Duurzame energie: Een nuchter verhaal
No full textEen samenvatting van het boek 'Sustainable Energy - without the hot air' van David J.C. MacKay. Professor MacKay is hoogleraar aan de Universiteit van Cambridge en Chief Scientific Advisor to the Department of Energy and Climate Change van de Britse regering. In het boek vergelijkt hij het gebruik van energie met de hoeveelheid energie die opgewekt kan worden met duurzame energie.Delft Research Initiative
Chaos in three physical systems
No full textExamples of various types of chaos are described in some physical systems:
(1) Subshifts of second species orbits in the circular restricted three body problem (with S.V. Bolotin);
(2) Anosov energy levels for the frictionless dynamics of a mechanical linkage, and a normally hyperbolic Anosov submanifold for weak friction and suitable feedback control (with T.J. Hunt);
(3) "Ergodic pumping": a proposed mechanism for the power stroke of myosin, and a design principle for nanobiotechnology (with D.J.C. MacKay)
Bayesian neural network learning for repeat purchase modelling in direct marketing.
No full textWe focus on purchase incidence modelling for a European direct mail company. Response models based on statistical and neural network techniques are contrasted. The evidence framework of MacKay is used as an example implementation of Bayesian neural network learning, a method that is fairly robust with respect to problems typically encountered when implementing neural networks. The automatic relevance determination (ARD) method, an integrated feature of this framework, allows to assess the relative importance of the inputs. The basic response models use operationalisations of the traditionally discussed Recency, Frequency and Monetary (RFM) predictor categories. In a second experiment, the RFM response framework is enriched by the inclusion of other (non-RFM) customer profiling predictors. We contribute to the literature by providing experimental evidence that: (1) Bayesian neural networks offer a viable alternative for purchase incidence modelling; (2) a combined use of all three RFM predictor categories is advocated by the ARD method; (3) the inclusion of non-RFM variables allows to significantly augment the predictive power of the constructed RFM classifiers; (4) this rise is mainly attributed to the inclusion of customer\slash company interaction variables and a variable measuring whether a customer uses the credit facilities of the direct mailing company.Marketing; Companies; Models; Model; Problems; Neural networks; Networks; Variables; Credit;
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