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First complex, then simple
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Very good formats. Thanks Shivanand for appreciating. It is our experience that first sifting, rigorously, carefully through complex data, as truly complex, is more efficient as a first step. After which the researcher can work to locate the simple models, the layers of the story. There are several interconnected issues to this problem and this approach.
First, as researchers ourselves we find it more efficient to use a fast filter to detect the presence of any signal, and then use these indications to fit smaller models.
Many methods are available for doing this sensibly and quickly. Some of these are fully nonparametric statistical learning machines [ 2 ] and others are more parametric based schemes [ 3 ].
In any of these approaches, discovery is the initial goal. Second, this approach is sometimes faulted on the grounds that results from a fast signal detector are impossible to interpret and hence are of little help.
It is certainly true that learning machine output can be hard to understand if we stop with just the declaration of signal present, or signal not present. Yet many learning machine methods are available for sorting the results and then getting down to simple models.
Third, it is also true that multiple small or large models can be equally compelling and all more or less correct: the models of Nature are strongly underdetermined.
NAME : RIMA BISWAS
Confirmation after discovery, after multiple disclosures should be reinforcing across domains and models. A single tiny best model is a rare event, not impossible, but also not efficiently considered as a likely and reachable endpoint.Our platform is user-friendly and compatible with all major operating systems. I expect him to appreciate my lifestyle choices and support me in whatever I do in the future.
Fill in all the required fields. In any of these approaches, discovery is the initial goal. Subscribe to our blog Get thought-provoking updates on marriage, love and culture.
Create Beautiful Biodata For Marriage
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