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It is clear that a major advantage of SEM is that it can visualize data and hypotheses in a graphic model.
From my perspective, this bottom-up era represents an ugly hiatus in what should have been a seamless progression of Helmholtz's ideas to our current appreciation of the brain as an inference machine (Dayan et al., 1995)—an organ that continually generates predictions and hypotheses in a top-down fashion.
For example, you might talk about a work's use of characters or symbolism to depict its point about society, or you could talk about the research questions and hypotheses in a journal article.
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This study looks at these questions and hypotheses in an even younger population.
In a Bayesian framework 'discovering' such rules means 'finding' the best-fitting structure and hypothesis in a given set of possible structures and hypotheses.
Bench research allows financial investors, scientists, and society to maximize control over experiments, develop experimental procedures, and test hypotheses in a safe and cost-effective manner.
Instead, such models should be seen as an important opportunity to generate and test hypotheses in a simple and controlled system.
Computational models can be used to predict (un measured behaviour or system responses and formalize hypotheses in a testable manner.
These new approaches make it much easier to extract biologically relevant and reliable hypotheses in an objective and reasonably unbiased fashion.
Instead, the goal is to allow the user to interactively explore the data, i.e., to re-adjust visualization and analysis methods in order to establish and verify hypotheses in an iterated process.
Unlike humans, who may have pet theories, AI scans through data and generates hypotheses in an unbiased way.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
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CEO of Professional Science Editing for Scientists @ prosciediting.com